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In this article, you will learn about Top 13 Highest Paying Data Science Jobs in India. Take a glimpse below. Data Analyst Data Scientist Machine Learning Engineer Machine Learning Scientist Applications Architect Data Architect Enterprise Architect Infrastructure Architect Statistician Business Intelligence Analyst Data Engineer Quantitative Analyst  Data Warehouse Architect Read more to know each in detail. The data science landscape is a dynamic space, almost mimicking the real globally, connected world. New technologies are being developed and applied year on year, and the transformation is likely to continue into the coming decade. Therefore, a surge in data science jobs can be expected in the near future.  There is a rising demand for data science practitioners around the world. These job opportunities would continue to surge beyond 2020, adding more than 1.5 lakh new jobs. This trend is a natural response to data being an important resource for organizations in the digital age. We listed the top 10 highest paying data science jobs in India. This list is updated and irrespective of your work level, beginner/entry-level or middle level, these will help you decide the direction you want to go in next. If you are aspiring for one of the best data science jobs in India, the salary package is not the only factor. Career growth, job security, and reputation make a career attractive. If you want to know about the highest paying data science jobs, this article may help you. Currently, about 70% of data science-related job postings require less than five years of experience, provided the candidates possess the necessary qualifications and demonstrate the right skill sets. Read on to get familiar with the job titles, the must-have credentials, and the top recruiters in the data science field! What Does Data Science Involve? Data science comprises collecting, manipulating, storing, and analyzing data. It facilitates data-driven approaches for decision-making, thus fostering an environment of continuous growth.  Amazon’s online shopping site serves as a prime example of how data collection can improve performance. Amazon customizes the home page views of users based on what they search, purchase, and spend. In other words, it remembers datasets and provides useful product recommendations to fit customer needs.  McKinsey Global Institute calls big data the next frontier of innovation and productivity. The MGI study estimates that a retailer who leverages data has the potential to increase its operating margins by more than 60 percent. Parallelly, personal-location data can enable consumers to pay much less for buying goods and services, hence capturing an economic surplus of about $600 billion. So, big data can be a game-changer in building healthy economies.  Following the mantra of “Using tech for good,” data science can be applied to shape a better reality of the future. Innovative applications can address a variety of critical issues, from improving diagnostic accuracy in the medical field to transforming healthcare with wearable tech. Data science can even help farmers and food suppliers in cutting down on food waste, improving yields, and ultimately earning more. Must read: Free excel courses! Today, many nonprofits are leveraging data technologies to implement innovative programs at the grassroots level, and also to boost fundraising efforts by predicting funding needs. So far as governments are concerned, there is a recent but resounding interest in using data science to identify potential security threats and monitoring initiatives on the ground.  The Future of Data Science in India  The career prospects of a data science graduate are very bright, not only in India but also at a global level. As the world is moving towards a more data-driven and analytical approach to making decisions based on data rather than instincts, almost every media to large size company is making expert data science personnel a part of their journey.  An enormous amount of data is generated daily, both structured and unstructured. In order to leverage such data to retrieve invaluable insights, organizations reach out to data scientists. There are different implementations of data science techniques, such as artificial intelligence, machine learning, deep learning, etc.  Some of the greatest advantages a company gets by applying data science in their business include Being able to segment and target audiences precisely so their purchase decisions can be influenced. Analyse the feasibility and profitability of newer and older products  Gaining better knowledge of customers and their buying preferences and curating marketing campaigns accordingly Detecting and preventing possible customer loss. With the growing number of startups and new businesses, the market is getting saturated day by day. In such situations, all these aforementioned benefits will significantly improve customer experience, customer retention, and customer loyalty. Hence, the future of Data Science in India is only expected to climb upward.  Factors Influencing Data Scientist Salary   Several factors influence a data scientist salary in India or abroad.   First, experience plays a big role. More experienced data scientists usually earn higher salaries because they bring valuable skills and knowledge to the table.   Second, education is key. Data scientists with advanced degrees can receive higher salaries due to their specific skills and expertise.  Third, the sector in which you work has an influence on your compensation. For example, data scientists in technology typically earn more than those who work in different industries.  Fourth, geographic location is important. The typical wage of a data scientist in India differs from that of a foreign country such as the United States.  For example, San Francisco and New York offer higher incomes to compensate for their higher cost of living.   Data Scientist Salary in India Based on Experience   Read on to discover more about the salary of data scientist in India based on professional experience.  Fresher Data Scientist Salary  Freshers, or those new to data science and with little to no professional experience, typically start with pay commensurate with their entry-level positions. In India, the median wage for freshers, generally classified as individuals with 0–1 year of experience, is around ₹5.11 lakh annually. This beginning compensation represents the basic skills and expertise gained via academic training while recognizing the importance of hands-on experience training and skill advancement.  Junior Data Scientist Salary  Upon gaining more knowledge and proficiency, these individuals become Junior Data Scientists. Junior data scientists may fetch a higher median income of around ₹7.06 lakh annually, provided they have one to five years of related expertise. This pay rise indicates the worth of their developing expertise in data visualization and analysis, modeling of statistics, and higher-level programming languages, as well as their ability to participate and contribute effectively to company goals and projects.  Mid-Level Data Scientist Salary  Mid-level data scientists often have three to six years of work knowledge and experience. The highest salary of a data scientist in India with 6-7 years of experience is around 14 LPA, with an average pay of around INR 13.67 LPA.   This range may be greater in tech companies and among persons with specific skills in machine learning, deep learning, or big data technologies. Companies want mid-level data professionals to have outstanding analytical abilities, a solid understanding of trendy data science technologies, and effective interpersonal abilities as they develop work experience in the industry.   Senior Data Scientist Salary  After earning 10 to 12 years of expertise, you’re a senior data scientist. These skilled experts generally make no less than INR 24 lakh annually.  Senior data scientists use data analysis to gain insights that can help with company growth and corporate initiatives. They have comparable tasks as young data scientists but are more active in team leadership and have greater influence over long-term data-driven choices and initiatives.   Here’s a breakdown of the average data scientist salary in India:  Data scientist fresher salary in India: (0-1 year): INR 5.10 lakh/year Junior data scientists (1-4 years): INR 8.05 lakh/ year Mid-level (5-9 years): INR 13.69 lakh/year Experienced (10-12 years or more): INR 24 lakh/year Highest Paying Data Science Jobs in India There is a range of data scientist salary based on the type of data science jobs they chose; however, the highest salary of data scientist in India is near about 20 LPA.  1. Data Analyst Role: Data analysts transform and manipulate large data sets. They also assist higher-level executives in gleaning insights from their analytics. Analysts should have adequate knowledge of A/B testing and tracking web analytics. It has always been one of the best data science jobs in India, and dealing with money – yours and others – is the stuff of dreams. Preferred qualifications: Entry-level openings in this area require a minimum of a bachelor’s degree (with emphasis on science/math/statistics). You should demonstrate aptitude in mathematics and logical ability. Usually, those proficient in programming—with skills in SQL, Python, Oracle, etc.—are given preference by hiring managers.  One of the key jobs of data analysts’ is to allocate a numerical value to significant business functions to ensure the organization’s performance can be retrieved and compared. Usually, data analysts must know how to use data to help organizations make informed decisions. Data analysts adapt and execute voluminous data. They guide the superior-level administrator to collect the visions from the analysis. The data scientist highest salary in India is resultant of their ability to handle all these challenging responsibilities. They own a high level of understanding of the A/B test and track the website analytics. The approximate earning of the data analyst is ₹4,18,452 per annum in India. They review information to know its accuracy and create reports on their outcomes for streamlining management or other sectors of an organization. They need to solve problems within the organization or discover ways to provide a competitive edge to the organization. Although programming might not be required, they must have proficiency in working with databases (like SQL) or developing data visualization (like graphs and charts) to elucidate their findings.  Data analysts with programming skills can get the highest salary of data scientist in India. Future Scope: Looking at the past data analytics industry trends, it is fair to say that the industry is experiencing exceptional growth. Observing the relevant fields, it is certain that till 2025, the trends will only go up in India and are expected to touch $118.7 billion by the year 2026.  Salary The average data analyst salary in India is ₹6.4 lakhs per annum. Source Data Analyst Salary based on Locations: City Salary Bangalore ₹6.9 Lakh New Delhi ₹6.7 Lakh Mumbai ₹6.6 Lakh Hyderabad ₹6.7 Lakh Pune ₹6.4 Lakh Chennai ₹6.5 Lakh Noida ₹6.7 Lakh Gurgaon ₹7.1 Lakh Top employers for data analyst jobs: Source Data Analyst Salary based on Experience: Experience Salary 1 Year ₹4.6 Lakh 2 Year ₹5.3 Lakh 3 Year ₹6.0 Lakh 5 Year ₹7.7 Lakh 6 Year ₹8.2 Lakh Data Analyst Salary based on Industry: Industry Salary IT Services ₹6.1 Lakh Internet ₹7.5 Lakh Software Product ₹6.8 Lakh Financial Services ₹7.1 Lakh KPO ₹7.1 Lakh Top Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Programs Inferential Statistics Programs 2 Hypothesis Testing Programs Logistic Regression Programs 3 Linear Regression Programs Linear Algebra for Analysis Programs 2. Data Scientist Role: It is a more technical position than a data analyst. Data scientists may perform data preparation tasks (cleaning, organizing, and so on) that allow companies to take strategic actions. They handle large datasets and uncover useful patterns and trends in the data.  Preferred qualifications: A master’s degree or advanced qualifications such as PhD are desirable for the designation of a data scientist. Sometimes, companies seek sector specialists (healthcare, retail, information technology, etc.) to fill high-responsibility positions. Hands-on experience is critical for data scientist roles, apart from having a sound background in IT, CS, math, and other such disciplines.  Data scientists gather and analyze huge sets of structured as well as unstructured data. They use unstructured data from sources like emails, social media feeds, and smart devices. Usually, they need to combine concepts of mathematics, statistics, and science. Moreover, they process and model the data. Subsequently, they interpret the results to develop practical plans for companies. The reason behind data scientist highest salary in India is that they are also analytical experts who employ their skills in both social science and technical domains to determine trends and handle data. Future Scope: It is soon to be expected that Data science will become a part of every job, whether they are tech-related or not. Especially in a developing country like India, it has ample room for growth; therefore, it will be a great career option for the next decade.  Salary Average data scientist salary in India is ₹14.4 lakhs. Source Data Scientist Salary based on Locations: City Salary Bangalore ₹15.5 Lakhs New Delhi ₹13.6 Lakhs Mumbai ₹13.2 Lakhs Hyderabad ₹14.8 Lakhs Pune ₹12.8 Lakhs Chennai ₹13.3 Lakhs Noida ₹13.7 Lakhs Gurgaon ₹14.1 Lakhs Data Scientist Salary based on Experience: Experience Salary 1 Year ₹9.5 Lakhs 2 Year ₹10.5 Lakhs 3 Year ₹11.6 Lakhs 5 Year ₹16.4 Lakhs 8 Year ₹19.9 Lakhs Data Scientist Salary based on Industry: Industry Salary IT Services ₹13.2 Lakhs Internet ₹18.3 Lakhs Software Product ₹16.6 Lakhs Financial Services ₹15.1 Lakhs KPO ₹15.3 Lakhs Our learners also read: Free Online Python Course for Beginners Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 3. Machine Learning Engineer Another data science-related job is a Machine Learning Engineer. Role: As a machine learning engineer, you are responsible for creating data funnels and delivering software solutions. Additionally, your job would involve running tests and experiments to monitor the system functionality and performance.  Machine learning engineers lie at the intersection of data science and software engineering. They use programming frameworks and big data tools to guarantee that the raw data amassed from data pipelines are filtered and ready to scale as required. They design self-reliant software for predictive model automation. They closely work with data scientists and ascertain that the proposed models can instil huge amounts of real-time data for producing precise outcomes. The data scientist highest salary in India is because of their ability to handle the above-described tasks and help organizations achieve their goals. They create the data pipes as well and convey the software resolutions. Moreover, their job role also involves executing tests to analyze the network’s function. Preferred qualifications: Machine learning engineers are expected to have strong statistical and programming skills. Software engineers with sufficient ML experience are preferred for such roles. You can brush theoretical topics with online courses and gain practical experience by implementing projects. Many online certifications with integrated industry mentoring are also available in the market.  Future Scope: In India and other parts of the world, the future scope of machine learning engineering is very bright. As the sector can get interconnected with almost any sector one can possibly imagine, the demand for machine learning engineers is expected to rise significantly in the near future.  upGrad’s Exclusive Data Science Webinar for you – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4 Salary: Average machine learning engineer salary in India is ₹10.1 lakhs. It is one of the highest-paying data science jobs in India. Source Machine Learning Engineer Salary based on Locations: City Salary Bangalore ₹10.5 Lakhs New Delhi ₹9.2 Lakhs Mumbai ₹8.6 Lakh Hyderabad ₹10.1 Lakh Pune ₹8.4 Lakh Chennai ₹8.8 Lakh Noida ₹9.0 Lakhs Gurgaon ₹10.6 Lakh Machine Learning Engineer Salary based on Experience: Experience Salary 1 Year ₹7.0 Lakh 2 Year ₹7.8 Lakh 3 Year ₹9.4 Lakhs 4 Year ₹12.2 Lakhs 5 Year ₹15.1 Lakhs Machine Learning Engineer Salary based on Industry: Industry Salary IT Services & Consulting ₹9.5 Lakhs Internet ₹13.8 Lakhs Software Product ₹12.2 Lakhs Financial Services ₹9.1 Lakhs Analytics & KPO ₹14.6 Lakhs 4. Machine Learning Scientist Role: As an ML scientist, you are tasked with researching new approaches, such as algorithms, supervised and unsupervised learning techniques. Organizations hire these professionals in positions with job titles like Research Scientist or Research Engineer. Preferred qualifications: Job postings for this role list the desired profile as “someone with an engineering degree with appropriate postgraduate studies and extensive proven research experience.”  Future Scope: Any profession that directly contributes to the machine learning field is expected to experience a rise in demand in the upcoming years as there is still a lot of development that has to happen in India.  Salary: The average salary of a machine learning scientist in India is around ₹5.7 LPA, making it one of the highest paying data science jobs. Source Machine Learning Scientist Salary based on Locations: City Salary Bangalore ₹5.4 Lakhs New Delhi ₹9.0 Lakhs Kolkata ₹7.5 Lakhs Hyderabad ₹7.7 Lakhs Pune ₹5.6 Lakhs Machine Learning Scientist Salary based on Experience: Experience Salary 1 Year ₹5.4 Lakhs 2 Year ₹5.6 Lakhs 3 Year ₹5.8 Lakhs 4 Year ₹9.8 Lakhs Machine Learning Scientist Salary based on Industry: Industry Salary IT Services & Consulting ₹5.8 Lakhs Internet ₹16.0 Lakhs Software Product ₹5.8 Lakhs Financial Services ₹20.5 Lakhs Hardware & Networking ₹5.9 Lakhs 5. Applications Architect Role: These practitioners track applications, supervising how they are operating within the company and how users are interacting with them. As the job title suggests, their job is to build the architecture of applications, replete with components like the user interface and app infrastructure. In addition to being one of the highest-paid data science jobs in India, this is also a fast-paced one.   Preferred qualifications: To qualify for an opening for applications architect, you would generally need a computer science degree, along with industry certifications in programming and architectural design. The excessive & growing demand makes application architects land on one of the highest salary data science jobs in India, while motivating candidates to bag data scientist highest salary in India with their improved skill set.  Future Scope: With the software development field experiencing booming growth, a huge need for talented professionals makes it a great career path now and in the upcoming years. Candidates can initiate their development journey now with upskilling to make themselves futureproof.  Salary: Average application architect salary in India is ₹34.9 lakhs. It is one of the highest paying data science jobs. Source Application Architect Salary based on Locations: City Salary Bangalore ₹30.4 Lakh New Delhi ₹30.9 Lakh Mumbai ₹30.8 Lakh Hyderabad ₹30.7 Lakh Pune ₹30.6 Lakh Chennai ₹30.7 Lakh Noida ₹30.8 Lakh Gurgaon ₹30.8 Lakh Top employers for application architect job: Source Application Architect Salary based on Experience: Experience Salary 9 Year ₹25.8 Lakh 10 Year ₹27.2 Lakh 11 Year ₹28.3 Lakh 12 Year ₹28.8 Lakh 14 Year ₹30.0 Lakh Application Architect Salary based on Industry: Industry Salary IT Services & Consulting ₹30.0 Lakh Internet ₹31.4 Lakh Software Product ₹26.5 Lakh Financial Services ₹31.1 Lakh Emerging Technologies ₹26.2 Lakh Read: 6 Best Career Options after BSC: What to do After B.Sc? 6. Data Architect Role: One of the highest paying data science jobs worldwide, data architect creates new database systems, use performance and design analytics to improve the interconnected data ecosystem within the company. The end goal is to make the information easily accessible for use by data scientists. It has always been one of the best data science jobs in India, and dealing with money – yours and others – is the stuff of dreams. Must read: Data structures and algorithm free! Preferred qualifications: To become a data architect, you would need a computer engineering education with sufficient command over applied mathematical and statistical concepts. Ideally, you should have completed coursework in subjects like data management, programming, big data development, systems analysis, and technology architecture.  Data architects create the basis for an online computing network that connects servers for information transmission. They are accountable for deploying the fundamental functions of networking solutions and deciding whether they follow regulations and compliance. Usually, they serve as experts to solve problems with clients or employees while accessing the Cloud. Occasionally, data scientist salary in India is inclusive of testing the cloud during any vulnerability. Skills required for the position are similar for computer engineers that are proficient in programming and networking. Future Scope: Data architecture is projected as one of the most promising fields under data science from 2018 to 2027. The demand for data architects is expected to rise steadily in the time bracket.  Salary: Average data architect salary in India is ₹26.9 lakhs. Source Data Architect Salary based on Locations: City Salary Bangalore ₹26.1 Lakhs New Delhi ₹23.3 Lakhs Mumbai ₹25.4 Lakhs Hyderabad ₹26.3 Lakhs Pune ₹24.3 Lakhs Chennai ₹25.9 Lakhs Noida ₹22.8 Lakhs Gurgaon ₹24.7 Lakhs Data Architect Salary based on Experience: Experience Salary 7 Year ₹ 23.4 Lakhs 8 Year ₹ 26.6 Lakhs 9 Year ₹ 27.4 Lakhs 10 Year ₹ 28.3 Lakhs 12 Year ₹ 29.7 Lakhs Data Architect Salary based on Industry: Industry Salary IT Services & Consulting ₹ 26.8 Lakhs Internet ₹ 22.7 Lakhs Software Product ₹ 27.8 Lakhs Financial Services ₹ 26.0 Lakhs Analytics & KPO ₹ 27.9 Lakhs 7. Enterprise Architect Role: As an enterprise architect, your duties include aligning the company’s strategy with technological solutions. You help businesses achieve their objectives by identifying needs and then designing system architecture to meet specific requirements.  Preferred qualifications: A bachelor level education combined with a master’s degree and field instruction in enterprise architecture can help you enter the workforce as an enterprise architect. The excessive & growing demand makes enterprise architects land on one of the highest salary data science jobs in India.  Future Scope: Being more beneficial to only businesses and filling different business gaps, the application field of enterprise architecture might be a little constrained, yet the targeted audience for this field is enormous and is expected to grow. Therefore, there will be a handsome amount of job opportunities in the future.  Salary: The average enterprise architect salary in India is ₹37.6 lakhs per annum. Source Enterprise Architect Salary based on Locations: City Salary Bangalore ₹ 36.7 Lakhs New Delhi ₹ 36.3 Lakhs Mumbai ₹ 34.8 Lakhs Hyderabad ₹ 35.5 Lakhs Pune ₹ 35.8 Lakhs Chennai ₹ 34.7 Lakhs Noida ₹ 36.7 Lakhs Gurgaon ₹ 34.7 Lakhs Enterprise Architect Salary based on Experience: Experience Salary 11 Years ₹ 32.8 Lakhs 12 Years ₹ 36.1 Lakhs 13 Years ₹ 40.0 Lakhs 14 Years ₹ 41.5 Lakhs 15 Years+ ₹ 43.0 Lakhs Enterprise Architect Salary based on Industry: Industry Salary IT Services & Consulting ₹ 36.5 Lakhs Internet ₹ 38.2 Lakhs Software Product ₹ 39.0 Lakhs Financial Services ₹ 36.7 Lakhs Analytics & KPO ₹ 34.9 Lakhs 8. Infrastructure Architect Role: An infrastructure architect oversees the existing business systems to ensure that they support the new technological requirements. Nowadays, organizations also hire Cloud infrastructure architects to supervise their cloud strategies.  Preferred qualifications: A degree in computer engineering or software development with adequate training in database administration, information system development, and system maintenance. Infrastructure architect has become one of the highest salary data science jobs in India due to its demand. Future Scope: Organizations will always need human gatekeepers to oversee whether or not the systems are running properly. The number of such roles is bound to experience growth with the rise in the implementation of relevant technology. Therefore, the future opportunities in this field are also bright.  Salary The average salary of an Infrastructure Architect in India is around ₹25.2 LPA. However, it can range anywhere from ₹14 LPA to ₹33 LPA. Source Infrastructure Architect Salary based on Locations: City Salary Bangalore ₹ 25.3 Lakhs New Delhi ₹ 25.7 Lakhs Mumbai ₹ 27.2 Lakhs Hyderabad ₹ 25.4 Lakhs Pune ₹ 25.8 Lakhs Chennai ₹ 25.4 Lakhs Noida ₹ 25.8 Lakhs Gurgaon ₹ 25.8 Lakhs Infrastructure Architect Salary based on Experience: Experience Salary 9 Year ₹ 23.6 Lakhs 10 Year ₹ 24.7 Lakhs 11 Year ₹ 25.3 Lakhs 12 Year ₹ 25.7 Lakhs 15 Years+ ₹ 26.3 Lakhs Infrastructure Architect Salary based on Industry: Industry Salary IT Services & Consulting ₹ 25.2 Lakhs Emerging Technologies ₹ 20.0 Lakhs Software Product ₹ 22.3 Lakhs Financial Services ₹ 23.5 Lakhs Analytics & KPO ₹ 21.3 Lakhs Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 9. Statistician Role: Statisticians are hired to collect, analyze, and interpret data, thus assisting the decision-makers with their work. Their day-to-day responsibilities also include communicating findings (data relationships and trends) to stakeholders and contributing to setting operational strategies. In addition to being one of the highest paying data science jobs in India, it is also a fast-paced one. Preferred qualifications: Entry-level openings may accept candidates with a bachelor’s degree. But most statisticians hold at least a postgraduate diploma in mathematics, computer science, economics, or other quantitative fields.  Future Scope: Not only in India but also abroad, the role of a statistician is highly prioritised. In India, the entry-level jobs might pay comparatively less, but with growing experience, the pay also gets increases. According to the Bureau of Labor Statistics reports, the job role of a Statistician is expected to see 33% growth by 2026.  Salary: Average statistician salary in India is ₹5.8 lakhs. Source Statistician Salary based on Locations: City Salary Bangalore ₹ 7.3 Lakhs New Delhi ₹ 3.8 Lakhs Mumbai ₹ 5.5 Lakhs Hyderabad ₹ 6.1 Lakhs Pune ₹ 7.7 Lakhs Kolkata ₹ 8.1 Lakhs Ahmedabad ₹ 3.9 Lakhs Chennai ₹ 3.6 Lakhs Statistician Salary based on Experience: Experience Salary 1 Year ₹ 3.5 Lakhs 2 Year ₹ 4.9 Lakhs 3 Year ₹ 5.7 Lakhs 4 Year ₹ 5.9 Lakhs 5 Year ₹ 7.3 Lakhs Statistician Salary based on Industry: Industry Salary IT Services & Consulting ₹ 5.9 Lakhs Education & Training ₹ 4.8 Lakhs Healthcare / Pharma ₹ 13.5 Lakhs Financial Services ₹ 8.0 Lakhs Analytics & KPO ₹ 10.6 Lakhs 10. Business Intelligence Analyst Role: BI analysts formulate strategic plans for businesses while ensuring that the required information can be utilized with ease. They also facilitate end-user understanding of the BI tools and applications developed by them. Preferred qualifications: The work of BI analysts calls for a combination of technical expertise with the know-how of business and management concepts. Many candidates hold an MBA with a specialization in analytics. Having business research and project coordination experience can give you a competitive edge.  Future Scope: As the business intelligence field itself is considered one of the fastest growing industries, the job of a business intelligence analyst holds great potential. Inclination towards a more analytical approach in business is another reason behind the increasing demand for the job, which is also expected to stay constant in the future.  Salary: Average business intelligence salary in India is ₹8.6 lakhs. Source Business Intelligence Analyst Salary based on Locations: City Salary Bangalore ₹ 8.9 Lakhs New Delhi ₹ 8.2 Lakhs Mumbai ₹ 7.4 Lakhs Hyderabad ₹ 8.3 Lakhs Pune ₹ 7.8 Lakhs Chennai ₹ 7.4 Lakhs Noida ₹ 7.5 Lakhs Gurgaon ₹ 7.3 Lakhs Business Intelligence Analyst Salary based on Experience: Experience Salary 1 Year ₹ 6.0 Lakhs 2 Year ₹ 7.0 Lakhs 3 Year ₹ 8.0 Lakhs 4 Year ₹ 9.1 Lakhs 5 Year ₹ 10.0 Lakhs Business Intelligence Analyst Salary based on Industry: Industry Salary IT Services & Consulting ₹ 8.1 Lakhs Internet ₹ 10.8 Lakhs Software Product ₹ 10.8 Lakhs Financial Services ₹ 7.5 Lakhs Analytics & KPO ₹ 8.7 Lakhs 11. Data Engineer A data engineer is responsible for maintaining and managing the data at the organisational level. It is one of the highest-paying jobs for data scientists. The data engineers use various tools and technologies, such as statistical analyses, programming skills, software development, etc.  Top skills required for Data Engineers – SQL Data Warehousing Data Architecture Coding  Operating System Machine Learning The question of which company pays the highest salary for data scientists in India is tricky to answer as various factors determine the salary. But the average salary for a data engineer is 10.8 lakhs per annum. Source Data Engineer Salary based on Locations: City Salary Bangalore ₹ 11.2 Lakhs New Delhi ₹ 11.1 Lakhs Mumbai ₹ 10.0 Lakhs Hyderabad ₹ 10.6 Lakhs Pune ₹ 9.9 Lakhs Noida ₹ 10.3 Lakhs Gurgaon ₹ 12.5 Lakhs Chennai ₹ 10.4 Lakhs Data Engineer Salary based on Experience: Experience Salary 1 Year ₹ 6.5 Lakhs 2 Year ₹ 7.6 Lakhs 3 Year ₹ 9.1 Lakhs 4 Year ₹ 11.3 Lakhs 5 Year ₹ 13.8 Lakhs Data Engineer Salary based on Industry: Industry Salary IT Services & Consulting ₹ 9.3 Lakhs Software Product ₹ 11.5 Lakhs. Internet ₹ 14.1 Lakhs Financial Services ₹ 11.1 Lakhs Analytics & KPO ₹ 11.7 Lakhs (Source) 12. Quantitative Analyst  Quantitative analysts are commonly called ‘quants’. They are fully responsible for using advanced statistical tools. They involve themselves in analysis to solve problems, identify the right answers to questions, and predict future happenings.  The highest-paying companies for data scientists in India require Quantitative Analysts to possess various skill sets, such as. Object-Oriented Programming Big Data Modelling C++ Data Mining Machine Learning Python SQL This is also one of the highest-paying jobs in data science. The average salary for a quantitative analyst is 22.9 lakhs per annum. Source Quantitative Analyst Salary based on Locations: City Salary Bangalore ₹ 18.8 Lakhs New Delhi ₹ 15.8 Lakhs Mumbai ₹ 17.3 Lakhs Hyderabad ₹ 22.2 Lakhs Pune ₹ 10.3 Lakhs Noida ₹ 11.5 Lakhs Gurgaon ₹ 23.6 Lakhs Chennai ₹ 10.9 Lakhs Quantitative Analyst Salary based on Experience: Experience Salary 1 Year ₹ 16.0 Lakhs 2 Year ₹ 17.2 Lakhs 3 Year ₹ 14.1 Lakhs 4 Year ₹ 16.4 Lakhs 5 Year ₹ 17.2 Lakhs Quantitative Analyst Salary based on Industry: Industry Salary IT Services & Consulting ₹ 16.6 Lakhs Banking ₹ 22.4 Lakhs Internet ₹ 12.4 Lakhs Financial Services ₹ 25.5 Lakhs Analytics & KPO ₹ 13.4 Lakhs Source 13. Data Warehouse Architect The highest-paying data science companies in India also have Data Warehouse Architects in their organisation.  The profession is considered a specialised sub-field in the world of data engineering. They are responsible for organising data storage systems. The skills required for a Data Warehouse Architect are – SQL Python  Oracle Python  Cobol They are one of those professionals who get the highest salaries of data scientists in India. The average salary for a Data Warehouse Architect is 25.9 lakhs per annum. Source Data Warehouse Architect Salary based on Locations: City Salary Bangalore ₹ 20.0 Lakhs Chennai ₹ 26.0 Lakhs Mumbai ₹ 17.4 Lakhs Hyderabad ₹ 26.3 Lakhs Pune ₹ 23.0 Lakhs Data Warehouse Architect Salary based on Experience: Experience Salary 8 Years ₹ 19.5 Lakhs 9 Years ₹ 22.3 Lakhs 12 Years ₹ 25.4 Lakhs 13 Years ₹ 26.3 Lakhs 15 Years+ ₹ 30.0 Lakhs Data Warehouse Architect Salary based on Industry: Industry Salary IT Services & Consulting ₹ 26.0 Lakhs Software Product ₹ 24.8 Lakhs Internet ₹ 32.5 Lakhs Financial Services ₹ 25.8 Lakhs Analytics & KPO ₹ 25.9 Lakhs Source. Learn: Business Analysis Vs Business Intelligence: Differences Between BA & BI Average Salary of Data Scientists in Other Countries   Data scientists’ pay varies greatly across national borders. These differences result from the fluctuating cost of living, demand for data professionals, and the field’s explosive global expansion. As previously stated, a data scientist’s pay is heavily influenced by their location; in the US, pay for this position is very high.   Novices in data science often make around $95,000 (roughly 70 lakh) annually. A person with four to six years of experience may earn $135,000 to $195,000 annually in the USA.   With seven to ten years of data science knowledge and skills, you may make anywhere from $165,000 to $260,000 annually—more than one crore in Indian rupees.  Data scientists in Germany and the UK may make substantial salaries. They might earn between £50,000 and £70,000 annually in the UK. It might range from €40,000 to €70,000 per year in Germany. Because of these nations’ robust IT and research sectors, there is an increasing need for highly qualified data professionals.  Data scientist wages are rising quickly in emerging nations like Brazil and India. The average salary of data scientist in India is around 5 to 8 lakh. However, they’re still less frequent than in more developed markets.  Check the list of median salaries for data scientists in various nations below:   Country  Average Data Scientist Salary  United States  ₹ 89 LPA (USD 108,053 p.a.)  Australia  ₹ 56.19 LPA (AUD 101,296 p.a.)  Canada  ₹ 61.20 LPA (CAD 99,656 p.a.)  United Kingdom  ₹ 50.07 LPA (£ 49,954 p.a.)  Singapore  ₹ 56.49 LPA (SGD 93,000 p.a.)  Europe  ₹ 48.22 LPA (€55475)  Must-have skills for data science professionals Background in mathematics, statistics, and computer science Knowledge of analytical tools – for example, R Understanding of calculus, multivariate analysis, data visualization, etc. Ability to convey, both verbally and visually, complex ideas in simple terms Intellectual curiosity, business acumen, and team orientation Apart from having the right skills, the data scientist salary also depends on the hiring company. Below are some companies that give the highest salary of data scientist in India.  Companies Offering Lucrative Data Science Salary in India 1. Flipkart Flipkart is a major e-commerce player in Indias, selling everything from fashion and lifestyle products to consumer electronics. It employs data scientists in various roles, giving them an average salary of Rs 17,00,000 annually.  2. Walmart Walmart has its modern wholesale stores spread across 8 locations in India. It hires for many data science positions, paying an average annual compensation of Rs 22,75,000. Typically, the compensation ranges between Rs 17,00,000 and Rs 28,00,000 per annum.  3. Amazon Being one of the biggest e-commerce players globally, Amazon needs data science talent for numerous core functions. Some of them include supply chain optimization, detection of frauds and fake reviews, inventory and sales forecasting. Amazon has several offices in India that hire data scientists. Glassdoor estimates the salary range at Rs 578,000-Rs 19,98,000, depending on the role.  Know more: Amazon AWS VPC Tutorial: An Extensive Guide on Creating an AWS VPC 4. Hewlett Packard Enterprise  IT solutions by HPE aim to assist customers by managing and assessing their data. Data scientists at HPE can make anywhere from Rs 8,00,000 to Rs 22,00,000.  5. IBM IBM is a leader in providing valuable collaborative tools to data scientists, application developers, and other experts. Data scientists at IBM make it easier to work with structured and unstructured data and train models at scale. They make an average salary of Rs 13,50,000 in India.  6. Deloitte Deloitte uses advanced statistical methods to offer end-to-end business solutions to practitioners in the field of data science. This Big Four firm employs professionals in different roles with total compensation between Rs 12 and 13 lakh per annum. 7. Accenture This prominent global professional services firm designs strategies and delivers them using integrated data resources. The typical salary of data scientists at Accenture is Rs. 10, 50,000 p.a.  8. [24]7.ai [24]7.ai is a leader in customer-driven solutions that help companies interact with consumers using artificial intelligence. Data science jobs at this firm pay Rs 16,00,000 p.a. on average.  9. LinkedIn LinkedIn operates through web and mobile apps, providing employment-related services to millions of people worldwide. Data scientist salaries start from Rs 13,00,000 p.a. and touch Rs 22,00,000 p.a. for high-level positions.  10. Citrix This US-based multinational corporation offers networking, cloud computing, and SaaS in India. The analytics and research teams at Citrix undertake data science projects for attaining a competitive advantage in the market and predict results to align its work with the mission. Annual pay for data science and related jobs starts from Rs 6,00,000 and can go up to Rs 27,00,000.  11. Fractal Analytics It is the second-largest analytics firm in India. Fractal hires data science professions at annual packages of around Rs 15,00,000.  12. Two Roads Tech This software development firm in Bangalore works mainly in the financial domain. The average salary range for data scientists at Two Roads Tech is Rs 23,00,000 to Rs 35,00,000. These were the top recruiters that provide data scientist highest salary in India.  Top Trends in Data Science The scope for data science is limitless in the current global context. So, it is no surprise that the highest paying jobs in data science show inclination towards individuals who continuously update their skill sets. Your critical thinking abilities, married with awareness about the latest market trends, can set you apart from the crowd. Here are a few prompts that will get you started on your knowledge-filled journey:  Extended business value: As the big data landscape gets complicated and the need for better governance rises, data analyses would entail even higher costs. We would need novel chip infrastructures to reduce reliance on centralized systems. So, scalable AI and ML solutions would have to be accelerated.  The rise of data pipelines: As more data is generated, we would need better mechanisms for filtering and analytical purposes. Data pipelines augmented with AI capabilities would facilitate graph analytics, which can be immensely powerful in predicting and planning for natural disasters. Owing to the increased creation of data, updates are required for data analysis. Data channels augmented with AI capabilities will allow graphical analysis. Consequently, it would be prominently significant in the planning and prediction of unusual scenarios. Responsible AI: A Gartner study forecasts that about 75 percent of enterprises will move on from piloting AI and start operationalizing it by 2024. In the wake of the coronavirus outbreak, AI, ML, and NLP techniques can also provide vital predictions and countermeasure impact for similar pandemic situations.  Massive IoT adoption: More smart devices (Google Assistant, Microsoft Cortana, Amazon Alexa, Apple Siri, etc.) would automate everyday tasks in our homes and offices.  The collision of data and analytics roles: As vendors start offering end-to-end workflows and technology advancements occur, there would be little distinction between the data and analytics markets. Augmented analytics would demand greater collaboration and processes that support efficient communication between the two roles. Prolonged value in the business: Because data science is becoming critical and the need to obtain higher authority increases, the data evaluation needs a big amount. It is necessary to have novel-chip infrastructures to reduce trust in central networks. Hence, machine learning resolutions and scalable AI abilities must be enhanced. The high data scientist salary in India is due to their ability to enhance business value. Sought-after Data Scientists skills: Various skills are required to become a successful data scientist. What skills are required relies on the type of industry. Data science can be used in different scenarios, including enhancing supply chain processes, determining marketing strategies, and providing services depending on customers’ demands. Here are the most sought-after skills of data scientists: Data Query and Collection: Data scientists should be able to collect and query data from different sources. They should be able to determine what sources are required and what information is most valuable to filter through. After successfully performing these tasks, data scientists can assess problems or create a sophisticated forecast depending on the information they hold. Programming and coding: One of the reasons for the highest salary of data scientist in India is they also possess great coding and programming skills. They are experienced at writing and analyzing code in Python and R. Their exceptional coding and programming skills help them easily understand programming. Advanced coding skills are required to deploy data structures or to outshine as a data engineer or data architect. Mathematics, Statistics, and Computer Science Theory: Data scientists must master traditional academic subjects like mathematics, computer science, and statistics. These skills help them configure machine learning for algorithmic learning using different research methods. Consequently, they gain more insight on various topics and improve a network’s security practices. Teamwork and data presentation: Typically in larger organizations, many tasks need individual participation. However, certain projects need data scientists to work in teams and also master soft skills. The same entails working with engineers to derive a solution. Powerful communication skills are obligatory for data scientists. Analytical and creative thinking: It is possible to earn the highest salary of data scientist in India if analytical and creative thinking skills are incorporated in addition to core technical skills. These skills help them achieve solutions to different issues from the gathered information. Moreover, these skills help them efficiently handle their data collecting and problem-solving tasks. Summing up These are the top 10 highest paying data science jobs in India. Data science is shaping the industry as we know it. In this article, we described the responsibilities and qualifications of some of the highest paying jobs in data science. We also looked at the leading companies who recruit talent in this space. Candidates who possess the required skills can earn as high Rs 28,00.000 annually working with big players like Walmart. Glassdoor estimates the average data science salary in India at Rs 10,00,000 a year.  The structure of the Data Science Program designed to facilitate you in becoming a true talent in the field of Data Science, which makes it easier to bag the best employer in the market. Register today to begin your learning path journey with upGrad!  Data science has tremendous applications in industries across the globe, and not just in tech. Moreover, the demand and supply of data science professionals in today’s job market are incongruent. So, aspirants who hone your skills, earn the necessary credentials, and march on the career path of your choice!  If you are interested in learning Data Science and opt for a career in this field, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

12 Apr 2024

Blogs

11940

In this era, with huge chunks of data, it becomes essential to deal with them. The data springing from organizations with growing customers is way larger than any traditional data management tool can store. It leaves us with the question of managing larger sets of data, which could range from gigabytes to petabytes, without using a single large computer or traditional data management tool. This is where the Apache Hadoop framework grabs the spotlight. Before diving into Hadoop command implementation, let’s briefly comprehend the Hadoop framework and its importance. What is Hadoop? Hadoop is commonly used by major business organizations to solve various problems, from storing large GBs (Gigabytes) of data every day to computing operations on the data. Traditionally defined as an open-source software framework used to store data and processing applications, Hadoop stands out quite heavily from the majority of traditional data management tools. It improves the computing power and extends the data storage limit by adding a few nodes in the framework, making it highly scalable. Besides, your data and application processes are protected against various hardware failures. Hadoop follows a master-slave architecture to distribute and store data using MapReduce and HDFS. As depicted in the figure below, the architecture is tailored in a defined manner to perform data management operations using four primary nodes, namely Name, Data, Master, and Slave. The core components of Hadoop are built directly on top of the framework. Other components integrate directly with the segments. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Source Hadoop Commands Major features of the Hadoop framework show a coherent nature, and it becomes more user-friendly when it comes to managing big data with learning Hadoop Commands. Below are some convenient Hadoop Commands that allow performing various operations, such as management and HDFS clusters file processing. This list of commands is frequently required to achieve certain process outcomes. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses 1. Hadoop Touchz hadoop fs -touchz /directory/filename This command allows the user to create a new file in the HDFS cluster. The “directory” in the command refers to the directory name where the user wishes to create the new file, and the “filename” signifies the name of the new file which will be created upon the completion of the command. 2. Hadoop Test Command  hadoop fs -test -[defsz] <path> This particular command fulfills the purpose of testing the existence of a file in the HDFS cluster. The characters from “[defsz]” in the command have to be modified as needed. Here is a brief description of these characters: d -> Checks if it is a directory or not e -> Checks if it is a path or not f -> Checks if it is a file or not s -> Checks if it is an empty path or not r -> Checks the path existence and read permission w -> Checks the path existence and write permission z -> Checks the file size In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses 3. Hadoop Text Command hadoop fs -text <src> The text command is particularly useful to display the allocated zip file in text format. It operates by processing source files and providing its content into a plain decoded text format. 4. Hadoop Find Command hadoop fs -find <path> … <expression> This command is generally used for the purpose to search for files in the HDFS cluster. It scans the given expression in the command with all the files in the cluster, and displays the files that match the defined expression. Read: Top Hadoop Tools 5. Hadoop Getmerge Command hadoop fs -getmerge <src> <localdest> Getmerge command allows merging one or multiple files in a designated directory on the HDFS filesystem cluster. It accumulates the files into one single file located in the local filesystem. The “src” and “localdest” represents the meaning of source-destination and local destination. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? 6. Hadoop Count Command hadoop fs -count [options] <path> As obvious as its name, the Hadoop count command counts the number of files and bytes in a given directory. There are various options available that modify the output as per the requirement. These are as follows: q -> quota shows the limit on the total number of names and usage of space u -> displays only quota and usage h -> gives the size of a file v -> displays header 7. Hadoop AppendToFile Command hadoop fs -appendToFile <localsrc> <dest> It allows the user to append the content of one or many files into a single file on the specified destination file in the HDFS filesystem cluster. On execution of this command, the given source files are appended into the destination source as per the given filename in the command. 8. Hadoop ls Command hadoop fs -ls /path The ls command in Hadoop shows the list of files/contents in a specified directory, i.e., path. On adding “R” before /path, the output will show details of the content, such as names, size, owner, and so on for each file specified in the given directory. 9. Hadoop mkdir Command hadoop fs -mkdir /path/directory_name This command’s unique feature is the creation of a directory in the HDFS filesystem cluster if the directory does not exist. Besides, if the specified directory is present, then the output message will show an error signifying the directory’s existence. 10. Hadoop chmod Command hadoop fs -chmod [-R] <mode> <path> This command is used when there is a need to change the permissions to accessing a particular file. On giving the chmod command, the permission of the specified file is changed. However, it is important to remember that the permission will be modified when the file owner executes this command. Hadoop Developer Salary Insights Salary Based on Location City Average Annual Salary Bangalore ₹8 Lakhs New Delhi ₹7 Lakhs Mumbai ₹8.2 Lakhs Hyderabad ₹7.8 Lakhs Pune ₹7.9 Lakhs Chennai ₹8.1 Lakhs Kolkata ₹7.5 Lakhs Salary Based on Experience Experience(Years) Average Annual Salary 0-2 ₹4.5 Lakhs 3 ₹6 Lakhs 4 ₹7.4 Lakhs 5 ₹8.5 Lakhs 6 ₹9.9 Lakhs Salary Based on Company Type Company Type Average Annual Salary Forbes Global 2000 ₹10.7 Lakhs Public ₹10.6 Lakhs Fortune India 500 ₹9.3 Lakhs MNCs ₹ 5.8 Lakhs – ₹ 7.4 Lakhs Startups ₹ 6.3 Lakhs – ₹ 8.1 Lakhs Also Read: Impala Hadoop Tutorial Conclusion Beginning with the important issue of data storage faced by the major organizations in today’s world, this article discussed the solution for limited data storage by introducing Hadoop and its impact on carrying out data management operations by using Hadoop commands. For beginners in Hadoop, an overview of the framework is described along with its components and architecture. After reading this article, one can easily feel confident about their knowledge in the aspect of the Hadoop framework and its applied commands. upGrad’s Exclusive PG Certification in Big Data: upGrad offers an industry-specific 7.5 months program for PG Certification in Big Data where you will organize, analyze, and interpret Big Data with IIIT-Bangalore. Designed carefully for working professionals, it will help the students gain practical knowledge and foster their entry into Big Data roles. Program Highlights: Learning relevant languages and tools Learning advanced concepts of Distributed Programming, Big Data Platforms, Database, Algorithms, and Web Mining An accredited certificate from IIIT Bangalore Placement assistance to get absorbed in top MNCs 1:1 mentorship to track your progress & assisting you at every point Working on Live projects and assignments Eligibility: Math/Software Engineering/Statistics/Analytics background Check our other Software Engineering Courses at upGrad.

by Rohit Sharma

12 Apr 2024

Blogs

20915

Attending a PySpark interview and wondering what are all the questions and discussions you will go through? Before attending a PySpark interview, it’s better to have an idea about the types of PySpark interview questions that will be asked so that you can mentally prepare answers for them. To help you out, I have created the top PySpark interview question and answers guide to understand the depth and real-intend of PySpark interview questions. Let’s get started. As the name suggests, PySpark is an integration of Apache Spark and the Python programming language. Apache Spark is a widely used open-source framework that is used for cluster-computing and is developed to provide an easy-to-use and faster experience. Python is a high-level general-purpose programming language. It is mainly used for Data Science, Machine Learning and Real-Time Streaming Analytics, apart from its many other uses. Originally, Apache spark is written in the Scala programming language, and PySpark is actually the Python API for Apache Spark. In this article, we will take a glance at the most frequently asked PySpark interview questions and their answers to help you get prepared for your next interview. If you are a beginner and interested to learn more about data science, check out our data analytics certification from top universities. Read: Dataframe in Apache PySpark Source PySpark Interview Questions and Answers 1. What is PySpark? This is almost always the first PySpark interview question you will face. PySpark is the Python API for Spark. It is used to provide collaboration between Spark and Python. PySpark focuses on processing structured and semi-structured data sets and also provides the facility to read data from multiple sources which have different data formats. Along with these features, we can also interface with RDDs (Resilient Distributed Datasets ) using PySpark. All these features are implemented using the py4j library.  2. List the advantages and disadvantages of PySpark? (Frequently asked PySpark Interview Question) The advantages of using PySpark are:  Using the PySpark, we can write a parallelized code in a very simple way. All the nodes and networks are abstracted. PySpark handles all the errors as well as synchronization errors. PySpark contains many useful in-built algorithms. Must read: Learn excel online free! The disadvantages of using PySpark are: PySpark can often make it difficult to express problems in MapReduce fashion. When compared with other programming languages, PySpark is not efficient.  Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 3. What are the various algorithms supported in PySpark? The different algorithms supported by PySpark are: spark.mllib mllib.clustering mllib.classification mllib.regression mllib.recommendation mllib.linalg mllib.fpm 4. What is PySpark SparkContext? PySpark SparkContext can be seen as the initial point for entering and using any Spark functionality. The SparkContext uses py4j library to launch the JVM, and then create the JavaSparkContext. By default, the SparkContext is available as ‘sc’.  5. What is PySpark SparkFiles? One of the most common PySpark interview questions. PySpark SparkFiles is used to load our files on the Apache Spark application. It is one of the functions under SparkContext and can be called using sc.addFile to load the files on the Apache Spark. SparkFIles can also be used to get the path using SparkFile.get or resolve the paths to files that were added from sc.addFile. The class methods present in the SparkFiles directory are getrootdirectory() and get(filename). Read: Spark Project Ideas upGrad’s Exclusive Data Science Webinar for you – document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 6. What is PySpark SparkConf? PySpark SparkConf is mainly used to set the configurations and the parameters when we want to run the application on the local or the cluster. We run the following code whenever we want to run SparkConf: class pyspark.Sparkconf( localdefaults = True, _jvm = None, _jconf = None ) 7. What is PySpark StorageLevel? PySpark StorageLevel is used to control how the RDD is stored, take decisions on where the RDD will be stored (on memory or over the disk or both), and whether we need to replicate the RDD partitions or to serialize the RDD. The code for StorageLevel is as follows:  class pyspark.StorageLevel( useDisk, useMemory, useOfHeap, deserialized, replication = 1) 8. What is PySpark SparkJobinfo? One of the most common questions in any PySpark interview. PySpark SparkJobinfo is used to gain information about the SparkJobs that are in execution. The code for using the SparkJobInfo is as follows:  class SparkJobInfo(namedtuple(“SparkJobInfo”, “jobId stageIds status ”)): Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? 9. What is PySpark SparkStageinfo? One of the most common question in any PySpark interview question and answers guide. PySpark SparkStageInfo is used to gain information about the SparkStages that are present at that time. The code used fo SparkStageInfo is as follows:  class SparkStageInfo(namedtuple(“SparkStageInfo”, “stageId currentAttemptId name numTasks unumActiveTasks” “numCompletedTasks numFailedTasks” )): Our learners also read: Free Python Course with Certification 10. What is PySpark DataFrames? This is one of the most common PySpark dataframe interview questions. PySpark DataFrames are the distributed assortment of well-organized data. They are identical to relational database tables and are included in named columns. Moreover, PySpark DataFrames are more efficiently optimized than Python or R programming languages. The reason is they can be created from various sources like Structured Data Files, Hive Tables, external databases, existing RDDs, etc. The greatest advantage of using PySpark DataFrame is that the data in it is distributed over various machines in the cluster. The corresponding operations will run parallel on all the machines. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 11. What is PySpark Join? PySpark Join helps combine two DataFrames. By binding these, it is easy to join multiple DataFrames. It enables all fundamental join type operations accessible in traditional SQL like INNER, RIGHT OUTER, LEFT OUTER, LEFT SEMI, LEFT ANTI, SELF JOIN, and CROSS. PySpark Joins are transformations that use data shuffling throughout the network. 12. How to rename a DataFrame column in PySpark? It is one of the most frequently asked PySpark dataframe interview questions. You can use PySpark withColumnRenamed() to rename a DataFrame column. Frequently, you need to remain single or multiple columns on PySpark DataFrame. It can be done in multiple ways. DataFrame is an immutable collection, so you can’t update or rename a column instead when using withColumnRenamed(). This is because it prepares a new DataFrame with the updated column names. Two common ways to rename nested columns are –renaming all columns or renaming selected multiple columns. 13. Are PySpark and Spark the same? These types of PySpark coding questions test the candidates’ basic knowledge of the PySpark fundamentals. PySpark has been launched to support the collaboration of Python and Apache Spark. Essentially, it is a Python API for Spark. PySpark assists you in interfacing with Resilient Distributed Datasets (RDDs) in Python programming language and Apache Spark. 14. What is PySparkSQL? When preparing for PySpark coding interview questions, you must prepare for PySparkSQL. It is a PySpark library to implement SQL-like analysis on a large amount of either structured or semi-structured data. You can also use SQL queries with PySparkSQL. Moreover, it can be connected to Apache Hive, and HiveQL can also be implemented. PySparkSQL works as a wrapper over the PySpark core. PySparkSQL introduced the DataFrame, a tabular illustration of structured data that is identical to that of a table from an RDBMS (relational database management system). 15. Are there any prerequisites to learning PySpark? One of the fundamental PySpark coding questions is about the prerequisites to learn PySpark. It is assumed that the readers are aware of what a framework and a programming language are before moving towards different concepts in the PySpark tutorial. It is beneficial if the readers have some knowledge of Python and Spark in advance. 16. What do you understand by PySpark SparkFiles? It is allowed to upload our files in Apache Spark by using sc.addFile. Here sc is the default SparkContext. It also assists in getting the path on a worker through SparkFiles.get. It also resolves the paths to files that are added via SparkContext.addFile().PySpark SparkFiles includes certain classmethods likeget(filename) and getrootdirectory(). 17. What are the key characteristics of PySpark? Knowing PySpark characteristics is important after you complete preparing for the PySpark coding interview questions. The four key characteristics of PySpark are as below. (i) Nodes are abstracted: You can’t access the individual worker nodes. (ii) APIs for Spark features: PySpark offers APIs for using Spark features. (iii) PySpark is dependent on MapReduce: PySpark is dependent on the MapReduce model of Hadoop. So, it lets a programmer provide the map and the reduced functions. (iv) Abstracted Network: Abstracted networks in PySpark allow implicit communication only. 18. What is SparkCore? What are the major functions of SparkCore? SparkCore is the Spark platform’s general execution engine that supports all the functionalities. It provides in-memory computing capabilities to offer a decent speed and a universal execution model to support different applications. It also supports Scala, Java, and Python APIs to simplify the development process. The key functions of SparkCore include the basic I/O functions, monitoring, scheduling, effective memory management, fault tolerance, fault recovery, and interaction with storage systems. 19. What it means by PySpark serializers? One of the mid-level PySpark interview coding questions can be around PySpark serializers. In PySpark, the serialization process is used to perform Spark performance tuning. PySpark incorporates serializers because you must constantly check the data sent or received across the network to the memory or disk. Two types of serializers in PySpark are as below. (i) PickleSerializer: It serializes the objects using Python’s PickleSerializer and class pyspark.PickleSerializer). It supports most of the Python objects. (ii) MarshalSerializer: It performs objects’ serialization. It can be employed through class pyspark.MarshalSerializer. It is faster than the PickleSerializer, but it supports limited types. 20. What is PySpark ArrayType? PySpark ArrayType is a collection data type that outspreads PySpark’s DataType class (the superclass for all types). It only contains the same types of files. You can use ArraType()to construct an instance of an ArrayType. Two arguments it accepts are discussed below. (i) valueType: The valueType must extend the DataType class in PySpark. (ii) valueContainsNull: It is an optional argument that states whether a value can accept null and it is by default value, is True. 21. What is PySpark Partition? How many partitions can one make in PySpark? You may be asked a PySpark interview question around PySpark Partition. It is a method that splits a huge dataset into smaller datasets depending on one or multiple partition keys. It improves the execution speed when the transformations on partitioned data operate faster. The reason is that every partition’s transformations run in parallel. PySpark allows two types of partitioning i.e. partitioning on disc (File system) and partitioning in memory (DataFrame). Its syntax is partitionBy (self, *cols) . Including 4x of partitions to the number of cores in the cluster accessible for application is recommended. 22. What is Parquet file in PySpark? You may be asked PySpark interview coding questions on the file type in PySpark. The Parquet file in PySpark is a column-type format supported by different data processing systems. It helps Spark SQL to perform read and write operations. Its column-type format storage offers the following benefits. (i) It consumes less space. (ii)It allows you to retrieve specific columns for access. (iii)It employs type-specific encoding. (iv)It provides better-summarized data. (v)It supports limited I/O operations. 23. Why is PySpark faster than pandas? This kind of PySpark interview question tests your in-depth knowledge of PySpark. PySpark is speedier than pandas because it supports parallel execution of statements in a distributed environment. PySpark can be implemented on different machines and cores not supported in Pandas. Benefits of Using PySpark Below are the benefits of using PySpark and knowing Pyspark interview questions Accelerated Data Processing PySpark’s forte lies in its ability to handle mammoth datasets with unparalleled speed. Leveraging parallel processing, it dissects hefty tasks into manageable chunks, executing them simultaneously across diverse nodes in a cluster. This not only slashes processing time but also facilitates real-time data analysis, rendering PySpark indispensable for big data applications, under pyspark questions Seamless Python Integration One of PySpark’s hallmarks is its seamless integration with Python, a language renowned for its simplicity and versatility. Built upon Python API, PySpark empowers users to wield Python’s familiar syntax effortlessly. This seamless integration is a boon for data scientists well-versed in Python and its arsenal of data analysis libraries like NumPy and Pandas. These can be considered as one of the pyspark coding interview questions for experienced. Scalability at Its Core It is engineered for scalability that easily accommodate burgeoning data volumes without sacrificing performance. This scalability is pivotal for organizations grappling with expanding datasets, necessitating a tool that can effortlessly adapt to their evolving needs. With PySpark, businesses can effortlessly scale their data processing capabilities up or down as per requirement. Cost-Effective Solution In a landscape littered with pricey data processing tools, PySpark emerges as a beacon of cost-effectiveness. Riding on the wings of Apache Spark’s open-source framework, PySpark incurs zero licensing costs. This accessibility democratizes data processing, empowering startups and small businesses with limited resources to harness the power of big data analytics and which is also known to be included inpyspark programming interview questions. Advanced Analytics Arsenal PySpark interview questions data professionals with a formidable array of advanced analytics tools, rendering it a versatile ally in data exploration. Boasting built-in libraries for machine learning, graph processing, and streaming data, PySpark caters to a diverse range of use cases. Moreover, its compatibility with external libraries like TensorFlow and Keras further amplifies its analytical prowess,pyspark coding interview questions and answers. Streamlined Parallel Programming Navigating the labyrinth of parallel programming can be daunting, especially when grappling with voluminous datasets. PySpark comes to the rescue by furnishing an intuitive API that abstracts away the complexities of parallel operations. This streamlined approach liberates data scientists and analysts to focus on analysis, unencumbered by the intricacies of parallel programming. Vibrant Community Support Backed by a robust community of developers and enthusiasts, PySpark thrives on collaborative innovation and support. Its open-source ethos fosters a rich ecosystem of resources and documentation, making it a veritable treasure trove for beginners. This abundant support network ensures that aspiring data fans can embark on their PySpark journey with confidence and clarity. How do I prepare for PySpark interview? Preparing for a PySpark interview requires strategic planning and diligent study for pyspark interview questions and answers. Here’s a step-by-step guide to help you ace your PySpark interview, also considered as important pyspark interview questions for experienced data engineer Understand the Basics Begin by familiarizing yourself with the fundamentals of PySpark. Ensure you have a solid grasp of its architecture, RDDs (Resilient Distributed Datasets), DataFrames, transformations, and actions. Brush up on Python basics as well since PySpark is built on top of Python, helpful pyspark coding interview questions. Dive into PySpark APIs Delve deeper into PySpark APIs to understand their functionalities and usage. Focus on key APIs like SparkContext, DataFrame API, and SQLContext. Practice writing code snippets to perform common tasks such as data manipulation, filtering, aggregation, and joins using PySpark APIs. Tese concepts should  be known for pyspark interview questions and answers for experienced. Data Handling and Transformation Demonstrate your proficiency in handling and transforming data using PySpark. Understand various data sources supported by PySpark such as CSV, JSON, Parquet, and Hive. Practice loading data from different sources into PySpark DataFrames, performing transformations, and saving results back to storage is available for pyspark interview questions for data engineer. Performance Tuning Techniques Familiarize yourself with performance tuning techniques in PySpark to optimize query execution and resource utilization. Learn about caching, partitioning, and broadcasting to improve job performance. Understand how to monitor and analyze job execution using Spark UI and Spark logs. Spark SQL and DataFrames Master Spark SQL and DataFrames, as they are integral parts of PySpark. Practice writing SQL queries on DataFrames using SparkSession’s SQLContext. Understand the benefits of using DataFrames over RDDs and when to leverage each based on the use case and pyspark code interview questions. Machine Learning with PySpark Gain proficiency in using PySpark for machine learning tasks. Learn about MLlib, PySpark’s machine learning library, and its supported algorithms for classification, regression, clustering, and collaborative filtering. Practice building machine learning pipelines and evaluating model performance. Real-world Projects and Use Cases To showcase your practical skills, work on real-world PySpark projects and use cases. Implement end-to-end data processing pipelines, from data ingestion to model deployment. Document your projects and be prepared to discuss your approach, challenges faced, and solutions implemented during the interview. Practice Coding and Problem-solving Practice coding and problem-solving using PySpark. Solve coding challenges and interview questions related to data manipulation, aggregation, and analysis using PySpark. Focus on writing clean, efficient, and optimized code to showcase your programming skills. Stay Updated and Network Stay updated with the latest advancements in PySpark and big data technologies. Follow relevant blogs, forums, and communities to stay abreast of industry trends and best practices. Network with professionals in the field and participate in PySpark meetups or events to broaden your knowledge and connections. Mock Interviews and Feedback Conduct mock interviews by to simulate real interview scenarios and receive constructive feedback. Practice explaining your solutions clearly and concisely, emphasizing your problem-solving approach and thought process. Address any weaknesses identified during mock interviews to improve your performance, which will affect pyspark interview questions for 5 years experience. What skills do you need to learn PySpark? Proficiency in Python At the heart of PySpark lies Python, a versatile and user-friendly programming language. Thus, a solid grasp of Python fundamentals forms the cornerstone of PySpark mastery. Familiarity with Python syntax, data structures, functions, and libraries like NumPy and Pandas lays a robust foundation for leveraging PySpark’s capabilities. Understanding of Data Processing Concepts A deep understanding of data processing concepts is paramount for harnessing PySpark’s full potential. Concepts like distributed computing, parallel processing, and data transformations form the bedrock of PySpark’s functionality. Familiarity with these concepts equips learners with the insights needed to optimize data processing workflows and tackle real-world challenges effectively, as important in interview questions on pyspark Knowledge of Apache Spark Architecture PySpark operates atop Apache Spark, an open-source distributed computing framework. Thus, a comprehensive understanding of Spark’s architecture is indispensable for mastering PySpark. Learners should acquaint themselves with Spark’s core components, such as RDDs (Resilient Distributed Datasets), DataFrames, and SparkSQL, to navigate PySpark’s intricacies with confidence. Proficiency in Data Manipulation and Analysis PySpark serves as a potent tool for data manipulation and analysis on a massive scale. Hence, proficiency in data manipulation techniques, including filtering, sorting, joining, and aggregating datasets, is essential. Additionally, familiarity with exploratory data analysis (EDA) methodologies empowers learners to glean actionable insights from vast datasets using PySpark. Understanding of Machine Learning Concepts PySpark boasts built-in libraries for machine learning, making it a formidable ally for predictive analytics tasks. Therefore, a foundational understanding of machine learning concepts, such as regression, classification, clustering, and feature engineering, is beneficial. Proficiency in PySpark’s MLlib library enables learners to develop and deploy machine learning models at scale. Familiarity with SQL PySpark seamlessly integrates with SQL, enabling users to perform SQL-like queries on distributed datasets using SparkSQL. Thus, a basic understanding of SQL syntax and query execution is advantageous for leveraging PySpark’s SQL capabilities. Proficiency in SQL equips learners with a versatile toolset for data exploration and manipulation in PySpark. Problem-Solving and Critical Thinking Skills The realm of big data analytics often presents complex challenges that require creative problem-solving and critical thinking skills. Learners should cultivate these skills to devise efficient solutions, optimize data processing workflows, and troubleshoot issues encountered while working with PySpark. Is PySpark in demand? Yes, pyspark interview questions are in high demand in today’s data-driven world. As organizations grapple with ever-expanding datasets, the need for efficient data processing and analysis tools has surged. PySpark, with its ability to handle large volumes of data at lightning-fast speeds and its seamless integration with Python, has become a top choice for data professionals. Its scalability, cost-effectiveness, and advanced analytics capabilities further contribute to its popularity, especially when pyspark programming questions Additionally, the vibrant community support surrounding PySpark ensures that users can access ample resources and assistance. As businesses across various industries recognize the importance of leveraging big data for strategic decision-making, the demand for PySpark expertise continues to grow. Hence, mastering PySpark can open up lucrative opportunities in the job market and propel one’s career in data science and analytics. Conclusion We hope you went through all the frequently asked PySpark Interview Questions. Apache Spark is mainly used to handle BigData and is in very high demand as companies move forward to use the latest technologies to drive their businesses. If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science. If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Do check out his course in order to learn from the best academicians and industry leaders to upgrade your career in this field. Study data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.  

by Rohit Sharma

05 Mar 2024

Blogs

5726

Introduction The world around is changing rapidly, we live a data-driven age now. Data is everywhere, from your social media comments, posts, and likes to your order and purchase data on the e-commerce websites that you visit daily. Your search data is used by the search engines to enhance your search results. For large organizations, this data is in the form of customer data, sales figures, financial data, and much more. You can imagine how much data is produced every second! Huge amounts of data are referred to as Big Data.  Check out our free courses to get an edge over the competition. Let us start with the basics concepts of Big Data and further proceed to list out and discuss the characteristics of big data. Read: Big data career path What is Big Data? Big Data refers to the huge collections of data that are structured and unstructured. This data may be sourced from servers, customer profile information, order and purchase data, financial transactions, ledgers, search history, and employee records. In large companies, this data collection is continuously growing with time. But the amount of data a company has is not important, but what it is doing with that data. Companies aim to analyze these huge collections of data properly to gain insights. The analysis helps them in understanding patterns in the data that eventually lead to better business decisions. All this helps in reducing time, efforts, and costs. But this humongous amount of data cannot be stored, processed, and studied using traditional methods of data analysis. Hence companies hire data analysts and data scientists who write programs and develop modern tools. Learn more about big data skills one needs to develop. Characteristics of Big data with examples will help you understand the various characteristics properly. Many Big Data characteristics have been discussed below precisely: Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Types of Big Data Big Data is present in three basic forms. They are –  1. Structured data As the name suggests, this kind of data is structured and is well-defined. It has a consistent order that can be easily understood by a computer or a human. This data can be stored, analyzed, and processed using a fixed format. Usually, this kind of data has its own data model. You will find this kind of data in databases, where it is neatly stored in columns and rows. Two sources of structured data are: Machine-generated data – This data is produced by machines such as sensors, network servers, weblogs, GPS, etc.  Human-generated data – This type of data is entered by the user in their system, such as personal details, passwords, documents, etc. A search made by the user, items browsed online, and games played are all human-generated information. For example, a database consisting of all the details of employees of a company is a type of structured data set. Learn: Mapreduce in big data 2. Unstructured data Any set of data that is not structured or well-defined is called unstructured data. This kind of data is unorganized and difficult to handle, understand and analyze. It does not follow a consistent format and may vary at different points of time. Most of the data you encounter comes under this category. For example, unstructured data are your comments, tweets, shares, posts, and likes on social media. The videos you watch on YouTube and text messages you send via WhatsApp all pile up as a huge heap of unstructured data. 3. Semi-structured data This kind of data is somewhat structured but not completely. This may seem to be unstructured at first and does not obey any formal structures of data models such as RDBMS. For example, NoSQL documents have keywords that are used to process the document. CSV files are also considered semi-structured data. After learning the basics and the characteristics of Big data with examples, now let us understand the features of Big Data. Read: Why to Become a Big Data Developer? Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Characteristics of Big Data There are several characteristics of Big Data with example. The primary characteristics of Big Data are – 1. Volume Volume refers to the huge amounts of data that is collected and generated every second in large organizations. This data is generated from different sources such as IoT devices, social media, videos, financial transactions, and customer logs. Storing and processing this huge amount of data was a problem earlier. But now distributed systems such as Hadoop are used for organizing data collected from all these sources. The size of the data is crucial for understanding its value. Also, the volume is useful in determining whether a collection of data is Big Data or not. Data volume can vary. For example, a text file is a few kilobytes whereas a video file is a few megabytes. In fact, Facebook from Meta itself can produce an enormous proportion of data in a single day. Billions of messages, likes, and posts each day contribute to generating such huge data. The global mobile traffic was tallied to be around 6.2 ExaBytes( 6.2 billion GB) per month in the year 2016. Also read: Difference Between Big Data and Hadoop 2. Variety Another one of the most important Big Data characteristics is its variety. It refers to the different sources of data and their nature. The sources of data have changed over the years. Earlier, it was only available in spreadsheets and databases. Nowadays, data is present in photos, audio files, videos, text files, and PDFs. The variety of data is crucial for its storage and analysis.  A variety of data can be classified into three distinct parts: Structured data Semi-Structured data Unstructured data 3. Velocity This term refers to the speed at which the data is created or generated. This speed of data producing is also related to how fast this data is going to be processed. This is because only after analysis and processing, the data can meet the demands of the clients/users. Massive amounts of data are produced from sensors, social media sites, and application logs – and all of it is continuous. If the data flow is not continuous, there is no point in investing time or effort on it. As an example, per day, people generate more than 3.5 billion searches on Google. Check out big data certifications at upGrad 4. Value Among the characteristics of Big Data, value is perhaps the most important. No matter how fast the data is produced or its amount, it has to be reliable and useful. Otherwise, the data is not good enough for processing or analysis. Research says that poor quality data can lead to almost a 20% loss in a company’s revenue.  Data scientists first convert raw data into information. Then this data set is cleaned to retrieve the most useful data. Analysis and pattern identification is done on this data set. If the process is a success, the data can be considered to be valuable. Knowledge Read: Big data jobs & Career planning 5. Veracity This feature of Big Data is connected to the previous one. It defines the degree of trustworthiness of the data. As most of the data you encounter is unstructured, it is important to filter out the unnecessary information and use the rest for processing. Read: Big data jobs and its career opportunities Veracity is one of the characteristics of big data analytics that denotes data inconsistency as well as data uncertainty. As an example, a huge amount of data can create much confusion on the other hand, when there is a fewer amount of data, that creates inadequate information. Other than these five traits of big data in data science, there are a few more characteristics of big data analytics that have been discussed down below: 1. Volatility  One of the big data characteristics is Volatility. Volatility means rapid change. And Big data is in continuous change. Like data collected from a particular source change within a span of a few days or so. This characteristic of Big Data hampers data homogenization. This process is also known as the variability of data. 2. Visualization  Visualization is one more characteristic of big data analytics. Visualization is the method of representing that big data that has been generated in the form of graphs and charts. Big data professionals have to share their big data insights with non-technical audiences on a daily basis. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Fundamental fragments of Big Data Let’s discuss the diverse traits of big data in data science a bit more in detail! Ingestion- In this step, data is gathered as well as processed. The process further extends when data is collected in batches or streams, and thereafter it is cleansed and organized to be finally prepared. Storage- After the collection of the required data, it is needed to be stored. Data is mainly stored in a data warehouse or data lake. Analysis- In this process, big data is processed to abstract valuable insights. There are four types of big data analytics: prescriptive, descriptive, predictive, and diagnostic. Consumption – This is the last stage of the big data process. The data insights are shared with non-technical audiences in the form of visualization or data storytelling. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Advantages and Attributes of Big Data  Big Data has emerged as a critical component of modern enterprises and sectors, providing several benefits and distinguishing itself from traditional data processing methods. The capacity to gather and interpret massive volumes of data has profound effects on businesses, allowing them to prosper in an increasingly data-driven environment.  Big Data characteristics come with several advantages. Here we have elucidated some of the advantages that explain the characteristics of Big Data with real-life examples:-  Informed Decision-Making: Big Data allows firms to make data-driven decisions. It helps businesses analyse huge amounts of data and can get important insights into consumer behaviour, market trends, and operational efficiency. This educated decision-making can result in better outcomes and a competitive advantage in the market. Improved Customer Experience: Big Data and its characteristics help in understanding customer data enabling companies to better understand consumer preferences, predict requirements, and personalise services. This results in better client experiences, increased satisfaction, and higher customer retention. Enhanced Operational Efficiency: The different features of Big Data analytics assist firms in optimizing their operations by finding inefficiencies and bottlenecks. This results in cheaper operations, lower costs, and improved overall efficiency. Product Development and Innovation: The 7 characteristics of Big Data offer insights that help stimulate both of these processes. Understanding market demands and customer preferences enables firms to produce new goods or improve existing ones in order to remain competitive. Risk Management: Various attributes of Big Data help by analysing massive databases, firms can identify possible hazards and reduce them proactively. Whether in financial markets, cybersecurity, or supply chain management, Big Data analytics aids in the effective prediction and control of risks. Personalised Marketing: By evaluating consumer behaviour and preferences, Big Data characteristics allow for personalised marketing techniques. This enables firms to design targeted marketing efforts, which increases the likelihood of turning leads into consumers with the help of Big Data and its characteristics.  Healthcare Advancements: Attributes of Big Data are being employed to examine patient information, medical history, and treatment outcomes. This contributes to customised therapy, early illness identification, and overall advances in healthcare delivery. Scientific Research and Discovery: Big Data is essential in scientific research because it allows researchers to evaluate massive datasets for patterns, correlations and discoveries. This is very useful in areas such as genetics, astronomy, and climate study. Real-time Analytics: Big Data characteristics and technologies enable businesses to evaluate and react to data in real-time. This is especially useful in areas such as banking, where real-time analytics may be used to detect fraud and anticipate stock market trends. Competitive Advantage: Businesses that properly use Big Data have a competitive advantage. Those who can quickly and efficiently assess and act on data insights have a higher chance of adapting to market changes and outperforming the competition. Application of Big Data in the Real World  The use of Big Data in the real world has become more widespread across sectors, affecting how businesses operate, make decisions, and engage with their consumers. Here, we look at some of the most famous Big Data applications in several industries. Healthcare  Predictive Analysis: Predictive analytics in healthcare uses Big Data to forecast disease outbreaks, optimise resource allocation, and enhance patient outcomes. Large datasets can be analysed to assist in uncovering trends and forecast future health hazards, allowing for proactive and preventative treatments. Personalised Medicine: Healthcare practitioners may adapt therapy to each patient by examining genetic and clinical data. Big Data facilitates the detection of genetic markers, allowing physicians to prescribe drugs and therapies tailored to a patient’s genetic composition. Electronic Health Records (EHR): The use of electronic health records has resulted in a massive volume of healthcare data. Big Data analytics is critical for processing and analyzing this information in order to improve patient care, spot patterns, and manage healthcare more efficiently. Finance Financial Fraud Detection: Big Data is essential to financial business’s attempts to identify and stop fraud. Real-time transaction data analysis identifies anomalous patterns or behaviours, enabling timely intervention to limit possible losses. Algorithmic Trading: Big Data is employed in financial markets to evaluate market patterns, news, and social media sentiment. Algorithmic trading systems use this information to make quick and educated investment decisions while optimizing trading methods. Credit Scoring and Risk Management: Big Data enables banks to more properly assess creditworthiness. Lenders can make more educated loan approval choices and manage risks by examining a wide variety of data, including transaction history, social behaviour, and internet activity. Retail  Customer Analytics: Retailers leverage Big Data to study customer behaviour, preferences, and purchasing history. This data is useful for establishing tailored marketing strategies, boosting inventory management, and improving the overall customer experience. Supply Chain Optimisation: Big Data analytics is used to improve supply chain operations by anticipating demand, enhancing logistics, and reducing delays. This ensures effective inventory management and lowers costs across the supply chain. Price Optimisation: Retailers use Big Data to dynamically modify prices depending on demand, rival pricing, and market trends. This allows firms to determine optimal pricing that maximises earnings while maintaining competition. Manufacturing  Predictive Maintenance: Big data is used in manufacturing to make predictions about the maintenance of machinery and equipment. Organisations can mitigate downtime by proactively scheduling maintenance actions based on sensor data and previous performance. Quality Control: Analysing data from the manufacturing process enables producers to maintain and enhance product quality. Big Data technologies understand patterns and abnormalities, enabling the early discovery and rectification of errors throughout the production process. Supplier Chain Visibility: Big Data gives firms complete visibility into their supplier chains. This insight aids in optimum utilisation of inventory, improved supplier collaboration, and on-time manufacturing and delivery. Telecommunications  Network Optimisation: Telecommunications businesses employ Big Data analytics to improve network performance. This involves examining data on call patterns, network traffic, and user behaviour to improve service quality and find opportunities for infrastructure enhancement. Customer Churn Prediction: By examining customer data, telecom companies can forecast which customers are likely to churn. This enables focused retention measures, such as tailored incentives or enhanced customer service, to help lessen turnover. Fraud Prevention: Big Data can help detect and prevent fraudulent activity in telecommunications, such as SIM card cloning and subscription fraud. Analysing trends and finding abnormalities aids in real-time fraud detection. Job Opportunities with Big Data  The Big Data employment market is varied, with possibilities for those with talents ranging from data analysis and machine learning to database administration and cloud computing. As companies continue to understand the potential of Big Data, the need for qualified people in these jobs is projected to remain high, making it an interesting and dynamic industry for anyone seeking a career in technology and analytics. Data Scientist: Data scientists use big data to uncover patterns and insights that are significant. They create and execute algorithms, analyse large databases, and present results to help guide decision-making. Data Engineer: The primary responsibility of a data engineer is to plan, build, and manage the infrastructure (such as warehouses and data pipelines) required for the effective processing and storing of massive amounts of data. Big Data Analysts: They interpret data to assist businesses in making educated decisions. They employ statistical approaches, data visualisation, and analytical tools to generate meaningful insights from large datasets. Machine Learning Engineer: By analysing large amounts of data using models and algorithms, machine learning engineers can build systems that are capable of learning and making judgments without the need for explicit programming. Database Administrator: Database administrators look after and administer databases, making sure they are scalable, secure, and function well. Administrators that work with Big Data often rely on distributed databases envisioned to manage large volumes of data. Business Intelligence (BI) Developer: BI developers construct tools and systems for collecting, interpreting, and presenting business information. They play an important role in converting raw data into usable insights for decision-makers. Data Architect: Data architects create the general architecture and structure of data systems, making sure that they satisfy the requirements of the company and follow industry best practices. Hadoop Developer: Hadoop developers work with tools such as HDFS, MapReduce, and Apache Spark. They create and execute solutions for processing and analyzing huge data collections. Data Privacy Analyst: With the growing significance of data privacy, analysts in this profession are responsible for ensuring that firms follow data protection legislation and apply appropriate privacy safeguards. IoT Data Analyst: Internet of Things (IoT) data analysts work with and analyse data created by IoT devices, deriving insights from massive volumes of sensor data collected in a variety of businesses. Cloud Solutions Architect: As enterprises transition to cloud platforms, cloud solutions architects develop and deploy Big Data solutions on cloud infrastructure to ensure scalability, dependability, and cost efficiency. Cybersecurity Analyst (Big Data): Experts in Big Data analyse enormous amounts of data to identify and address security issues. They employ advanced analytics to detect patterns suggestive of cyberattacks. Conclusion Big Data is the driving force behind major sectors such as business, marketing, sales, analytics, and research. It has changed the business strategies of customer-based and product-based companies worldwide. Thus, all the Big Data characteristics have to be given equal importance when it comes to analysis and decision-making. In this blog, we tried to list out and discuss the characteristics of big data, which, if grasped accurately, can fuel you to do wonders in the field of big data! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

by Rohit Sharma

04 Mar 2024

Blogs

10801

Introduction Data mining is the process in which information that was previously unknown, which could be potentially very useful, is extracted from a very vast dataset. Data mining architecture or architecture of data mining techniques is nothing but the various components which constitute the entire process of data mining. Learn data science to gain expertise in data mining and remain competitive in the market.  Data Mining Architecture Components Let’s take a look at the components which make the entire data mining architecture.  1. Sources of Data The place where we get our data to work upon is known as the data source or the source of the data. There are many documentations presented, and one might also argue that the whole World Wide Web (WWW) is a big data warehouse. The data can be anywhere, and some might reside in text files, a standard spreadsheet document, or any other viable source like the internet. 2. Database or Data Warehouse Server The server is the place that holds all the data which is ready to be processed. The fetching of data works upon the user’s request, and, thus, the actual datasets can be very personal. 3. Data Mining Engine The field of data mining is incomplete without what is arguably the most crucial component of it, known as a data mining engine. It usually contains a lot of modules that can be used to perform a variety of tasks. The tasks which can be performed can be association, characterization, prediction, clustering, classification, etc. 4. Modules for Pattern Evaluation This module of the architecture is mainly employed to measure how interesting the pattern that has been devised is actually. For the evaluation purpose, usually, a threshold value is used. Another critical thing to note here is that this module has a direct link of interaction with the data mining engine, whose main aim is to find interesting patterns.  Our learners also read: Free Python Course with Certification upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 5. GUI or Graphical User Interface As the name suggests, this module of the architecture is what interacts with the user. GUI serves as the much-needed link between the user and the system of data mining. GUI’s main job is to hide the complexities involving the entire process of data mining and provide the user with an easy to use and understand module which would allow them to get an answer to their queries in an easy to understand fashion. 6. Knowledge Base The base of all the knowledge is vital for any data mining architecture. The knowledge base is usually used as the guiding beacon for the pattern of the results. It might also contain the data from what the users have experienced. The data mining engine interacts with the knowledge base often to both increase the reliability and accuracy of the final result. Even the pattern evaluation module has a link to the knowledge base. It interacts with the knowledge base on a regular interval to get various inputs and updates from it. Read: 16 Data Mining Projects Ideas & Topics For Beginners Types of data mining architecture There are four different types of architecture which have been listed below: 1. No-coupling Data Mining No-coupling architecture typically does not make the use of any functionality of the database. What no-coupling usually does is that it retrieves the required data from one or one particular source of data. That’s it; this type of architecture does not take any advantages whatsoever of the database in question. Because of this specific issue, no-coupling is usually considered a poor choice of architecture for the system of data mining. Still, it is often used for elementary processes involving data mining. 2. Loose coupling Data Mining Loose coupling data mining process employs a database to do the bidding of retrieval of the data. After it is done finding and bringing the data, it stores the data into these databases. This type of architecture is often used for memory-based data mining systems that do not require high scalability and high performance. 3. Semi-Tight coupling Data Mining Semi-Tight architecture makes uses of various features of the warehouse of data. These features of data warehouse systems are usually used to perform some tasks pertaining to data mining. Tasks like indexing, sorting, and aggregation are the ones that are generally performed. 4. Tight-coupling Data Mining The tight-coupling architecture differs from the rest in its treatment of data warehouses. Tight-coupling treats the data warehouse as a component to retrieve the information. It also makes use of all the features that you would find in the databases or the data warehouses to perform various data mining tasks. This type of architecture is usually known for its scalability, integrated information, and high performance. There are three tiers of this architecture which are listed below: 5. Data layer Data layer can be defined as the database or the system of data warehouses. The results of data mining are usually stored in this data layer. The data that this data layer houses can then be further used to present the data to the end-user in different forms like reports or some other kind of visualization. 6. Data Mining Application layer The job of Data mining application layer is to find and fetch the data from a given database. Usually, some data transformation has to be performed here to get the data into the format, which has been desired by the end-user.  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 7. Front end layer This layer has virtually the same job as a GUI. The front-end layer provides intuitive and friendly interaction with the user. The result of the data mining is usually visualized as some form or the other to the user by making use of this front-end layer. Also read: What is Text Mining: Techniques and Applications Techniques of Data Mining  There are several data mining techniques which are available for the user to make use of; some of them are listed below: 1. Decision Trees Decision trees are the most common technique for the mining of the data because of the complexity or lack thereof in this particular algorithm. The root of the tree is a condition. Each answer then builds upon this condition by leading us in a specific way, which will eventually help us to reach the final decision. 2. Sequential Patterns Sequential patterns are usually used to discover events that occur regularly or trends that can be found in any transactional data. 3. Clustering Clustering is a technique that automatically defines different classes based on the form of the object. The classes thus formed will then be used to place other similar kinds of objects in them. 4. Prediction This technique is usually employed when we are required to accurately determine an outcome that is yet to occur. These predictions are made by accurately establishing the relationship between independent and dependent entities. 5. Classification This technique is based out of a similar machine learning algorithm with the same name. This technique of classification is used to classify each item in question into predefined groups by making use of mathematical techniques such as linear programming, decision trees, neural networks, etc. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? The Cornerstone: Delving into Data Warehouse Architecture Imagine a colossal library, meticulously organized and readily accessible, housing all your organizational data. This is the essence of a data warehouse, the foundational pillar of data mining architecture. Structured for efficient querying and analysis, it typically utilizes a star schema or snowflake schema to optimize data retrieval and performance. These schemas act as intricate maps, allowing data analysts to navigate with ease through the vast landscapes of information. Navigating the Labyrinth: OLAP Architecture in Data Mining – Unveiling Hidden Dimensions OLAP, short for Online Analytical Processing, empowers users to slice and dice data from various angles, shedding light on hidden patterns and insights. This OLAP architecture within the data warehouse leverages multidimensional cubes that enable fast retrieval and analysis of large datasets. Think of these cubes as Rubik’s cubes of information, where each side reveals a different perspective, granting invaluable insights for informed decision-making. Building the Engine: Demystifying the Architecture of a Typical Data Mining System Now, let’s delve into the core functionality of data mining itself. A typical data mining system architecture comprises five key stages, each playing a crucial role in the transformation of raw data into actionable insights: Data Acquisition: Data, the lifeblood of the system, is collected from diverse sources, including internal databases, external feeds, and internet-of-things (IoT) sensors. Imagine data flowing in like rivers, a vast lake of information ready to be explored. Data Preprocessing: Raw data can be messy and inconsistent, like unrefined ore. This stage involves cleansing, transforming, and integrating the data into a consistent format for further analysis. It’s akin to refining the ore, removing impurities and preparing it for further processing. Data Mining: Specialized algorithms, the skilled miners of the information world, are applied to uncover patterns, trends, and relationships within the preprocessed data. These algorithms work like sophisticated tools, sifting through the information to unveil hidden gems of knowledge. Pattern Evaluation: Extracted patterns, like potential diamonds unearthed from the mine, are carefully assessed for their validity, significance, and applicability. This stage involves rigorous testing and analysis to ensure the extracted insights are genuine and valuable. Deployment: Finally, the extracted insights are presented in a user-friendly format, such as reports, dashboards, or visualizations, empowering informed decision-making. Imagine these insights as polished diamonds, presented in a way that stakeholders can readily understand and utilize. Essential Components: Unveiling the Data Warehouse Components in Data Mining Several crucial components, each playing a distinct role, work in concert within the data warehouse architecture: Staging Area: This serves as a temporary haven for raw data, where it undergoes initial processing and preparation before being loaded into the main warehouse. Think of it as a sorting room, where data is organized and categorized before being placed on the shelves. ETL (Extract, Transform, Load): These processes act as the workhorses of the system, extracting data from various sources, transforming it into a consistent format, and loading it into the warehouse. Imagine ETL as a conveyor belt, efficiently moving and preparing the data for further analysis. Metadata Repository: This acts as the data dictionary, storing information about the data itself, including its structure, meaning, and lineage. It’s like a detailed index in the library, allowing users to easily find and understand the information they need. Query Tools: These empower users to interact with the data, ask questions, and extract insights. They are the tools that allow users to explore the library, search for specific information, and gain knowledge. Future-Proofing with Innovation: AI and Machine Learning Integration – Expanding the Horizons The realm of data mining is constantly evolving, driven by advancements in technology. The integration of AI and machine learning techniques promises even more sophisticated capabilities. These advanced algorithms can handle complex and unstructured data sources, like social media text and sensor data, unlocking deeper insights previously hidden within the information labyrinth. Imagine AI and machine learning as powerful new tools, opening up previously inaccessible data sources and revealing even more valuable gems of knowledge. Ethics and Transparency: Guiding Principles for Responsible Data Mining As data mining becomes more pervasive, ethical considerations take center stage. Responsible data practices, transparency in data collection and algorithm usage, and adherence to data privacy regulations are paramount to building trust and ensuring ethical data practices. Imagine navigating the information labyrinth responsibly, ensuring ethical treatment of the data while still extracting valuable insights. Democratizing Insights: Augmented Analytics – Empowering Everyone The rise of augmented analytics platforms is revolutionizing data accessibility. These platforms leverage natural language processing and automated model generation, empowering non-technical users to independently explore and analyze data, fostering a data-driven culture within organizations. Imagine everyone having access to a personal data analysis assistant, simplifying complex tasks and making insights readily available. Beyond the Horizon: Exploring the Future of Data Mining The future of data mining holds tremendous potential for innovation and growth, driven by advancements in technology and evolving business needs: Real-time Analytics: With the proliferation of IoT devices and sensors,data warehouse architecture in data mining will increasingly focus on real-time analytics, enabling organizations to respond promptly to changing market conditions, customer preferences, and emerging trends. Imagine having a real-time pulse on your business, constantly adapting and optimizing based on the latest data insights. Privacy-Preserving Techniques: To address privacy concerns, data mining algorithms will incorporate privacy-preserving techniques such as differential privacy, federated learning, and homomorphic encryption, ensuring compliance with data protection regulations while still extracting valuable insights. Imagine unlocking insights responsibly, safeguarding individual privacy while still gaining valuable knowledge. Interdisciplinary Applications: Data mining will continue to transcend traditional boundaries, finding applications in diverse fields such as healthcare, finance, transportation, and urban planning. Imagine data insights revolutionizing various industries, leading to breakthroughs and advancements in different sectors. Augmented Analytics: The rise of augmented analytics platforms will continue to empower non-technical users and democratize data exploration. Imagine a future where everyone, regardless of technical expertise, can leverage data to make informed decisions and contribute to organizational success. Conclusion  Due to the leaps and bounds made in the field of technology, the power and prowess of processing have significantly increased. This increment in technology has enabled us to go further and beyond the traditionally tedious and time-consuming ways of data processing, allowing us to get more complex datasets to gain insights that were earlier deemed impossible. This gave birth to the field of data mining. Data mining is a new upcoming field that has the potential to change the world as we know it. Data mining architecture or architecture of data mining system is how data mining is done. Thus, having knowledge of architecture is equally, if not more, important to having knowledge about the field itself. If you are curious to learn about data mining architecture, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

27 Feb 2024

Blogs

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What is a Data Analytics Lifecycle? Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes through several phases/ stages during its entire life. A data analytics architecture maps out such steps for data science professionals. It is a cyclic structure that encompasses all the data life cycle phases, where each stage has its significance and characteristics. The lifecycle’s circular form guides data professionals to proceed with data analytics in one direction, either forward or backward. Based on the newly received information, professionals can scrap the entire research and move back to the initial step to redo the complete analysis as per the lifecycle diagram for the data analytics life cycle. However, while there are talks of the data analytics lifecycle among the experts, there is still no defined structure of the mentioned stages. You’re unlikely to find a concrete data analytics architecture that is uniformly followed by every data analysis expert. Such ambiguity gives rise to the probability of adding extra phases (when necessary) and removing the basic steps. There is also the possibility of working for different stages at once or skipping a phase entirely. One of the other main reasons why the Data Analytics lifecycle or business analytics cycle was created was to address the problems of Big Data and Data Science. The 6 phases of Data Analysis is a process that focuses on the specific demands that solving Big Data problems require. The meticulous step-by-step 6 phases of Data Analysis method help in mapping out all the different processes associated with the process of data analysis.  Learn Data Science Courses online at upGrad So if we are to have a discussion about Big Data analytics life cycle, then these 6 stages will likely come up to present as a basic structure. The data analytics life cycle in big data constitutes the fundamental steps in ensuring that the data is being acquired, processed, analyzed and recycles properly. upGrad follows these basic steps to determine a data professional’s overall work and the data analysis results. Types of Data Anaytics Descriptive Analytics Descriptive analytics serves as a time machine for organizations, allowing them to delve into their past. This type of analytics is all about gathering and visualizing historical data, answering fundamental questions like “what happened?” and “how many?” It essentially provides a snapshot of the aftermath of decisions made at the organizational level, aiding in measuring their impact. For instance, in a corporate setting, descriptive analytics, often dubbed as “business intelligence,” might play a pivotal role in crafting internal reports. These reports could encapsulate sales and profitability figures, breaking down the numbers based on divisions, product lines, and geographic regions. Diagnostic Analytics While descriptive analytics lays the groundwork by portraying what transpired, diagnostic analytics takes a step further by unraveling the mysteries behind the events. It dives into historical data points, meticulously identifying patterns and dependencies among variables that can explain a particular outcome. In essence, it answers the question of “why did it happen?” In a practical scenario, imagine a corporate finance department using diagnostic analytics to dissect the impacts of currency exchange, local economics, and taxes on results across various geographic regions. Predictive Analytics Armed with the knowledge gleaned from descriptive and diagnostic analytics, predictive analytics peers into the future. It utilizes historical trends to forecast what might unfold in the days to come. A classic example involves predictive analysts using their expertise to project the business outcomes of decisions, such as increasing the price of a product by a certain percentage. In a corporate finance context, predictive analytics could be seamlessly integrated to incorporate forecasted economic and market-demand data. This, in turn, aids in predicting sales for the upcoming month or quarter, allowing organizations to prepare strategically. Prescriptive Analytics Taking the analytics journey to its zenith, prescriptive analytics utilizes machine learning to offer actionable recommendations. It goes beyond predicting future outcomes; it actively guides organizations on how to achieve desired results. This could involve optimizing company operations, boosting sales, and driving increased revenue. In the corporate finance department, prescriptive analytics could play a pivotal role in generating recommendations for relative investments. This might encompass making informed decisions about production and advertising budgets, broken down by product line and region, for the upcoming month or quarter. Phases of Data Analytics Lifecycle A scientific method that helps give the data analytics life cycle a structured framework is divided into six phases of data analytics architecture. The framework is simple and cyclical. This means that all these steps in the data analytics life cycle in big data will have to be followed one after the other. It is also interesting to note that these steps can be followed both forward and backward as they are cyclical in nature. So here are the 6 phases of data analyst that are the most basic processes that need to be followed in data science projects.  Phase 1: Data Discovery and Formation Everything begins with a defined goal. In this phase, you’ll define your data’s purpose and how to achieve it by the time you reach the end of the data analytics lifecycle. Everything begins with a defined goal. In this phase, you’ll define your data’s purpose and how to achieve it by the time you reach the end of the data analytics lifecycle. The goal of this first phase is to make evaluations and assessments to come up with a basic hypothesis for resolving any problem and challenges in the business.  The initial stage consists of mapping out the potential use and requirement of data, such as where the information is coming from, what story you want your data to convey, and how your organization benefits from the incoming data. As a data analyst, you will have to study the business industry domain, research case studies that involve similar data analytics and, most importantly, scrutinize the current business trends. Then you also have to assess all the in-house infrastructure and resources, time and technology requirements to match with the previously gathered data. After the evaluations are done, the team then concludes this stage with hypotheses that will be tested with data later. This is the preliminary stage in the big data analytics lifecycle and a very important one.  Basically, as a data analysis expert, you’ll need to focus on enterprise requirements related to data, rather than data itself. Additionally, your work also includes assessing the tools and systems that are necessary to read, organize, and process all the incoming data. Must read: Learn excel online free! Essential activities in this phase include structuring the business problem in the form of an analytics challenge and formulating the initial hypotheses (IHs) to test and start learning the data. The subsequent phases are then based on achieving the goal that is drawn in this stage. So you will need to develop an understanding and concept that will later come in handy while testing it with data.  Our learners also read: Python free courses! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Preparing for a data analyst role? Sharpen your interview skills with our comprehensive list of data analyst interview questions and answers to confidently tackle any challenge thrown your way. Phase 2: Data Preparation and Processing This stage consists of everything that has anything to do with data. In phase 2, the attention of experts moves from business requirements to information requirements. The data preparation and processing step involve collecting, processing, and cleansing the accumulated data. One of the essential parts of this phase is to make sure that the data you need is actually available to you for processing. The earliest step of the data preparation phase is to collect valuable information and proceed with the data analytics lifecycle in a business ecosystem. Data is collected using the below methods: Data Acquisition: Accumulating information from external sources. Data Entry: Formulating recent data points using digital systems or manual data entry techniques within the enterprise. Signal Reception: Capturing information from digital devices, such as control systems and the Internet of Things. The Data preparation stage in the big data analytics life cycle requires something known as an analytical sandbox. This is a scalable platform that data analysts and data scientists use to process data. The analytical sandbox is filled with data that was executed, loaded and transformed into the sandbox. This stage in the business analytical cycle does not have to happen in a predetermined sequence and can be repeated later if the need arises.  Read: Data Analytics Vs Data Science Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Phase 3: Design a Model After mapping out your business goals and collecting a glut of data (structured, unstructured, or semi-structured), it is time to build a model that utilizes the data to achieve the goal. This phase of the data analytics process is known as model planning.  There are several techniques available to load data into the system and start studying it: ETL (Extract, Transform, and Load) transforms the data first using a set of business rules, before loading it into a sandbox. ELT (Extract, Load, and Transform) first loads raw data into the sandbox and then transform it. ETLT (Extract, Transform, Load, Transform) is a mixture; it has two transformation levels. Also read: Free data structures and algorithm course! This step also includes the teamwork to determine the methods, techniques, and workflow to build the model in the subsequent phase. The model’s building initiates with identifying the relation between data points to select the key variables and eventually find a suitable model. Data sets are developed by the team to test, train and produce the data. In the later phases, the team builds and executes the models that were created in the model planning stage.  Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Phase 4: Model Building This step of data analytics architecture comprises developing data sets for testing, training, and production purposes. The data analytics experts meticulously build and operate the model that they had designed in the previous step. They rely on tools and several techniques like decision trees, regression techniques (logistic regression), and neural networks for building and executing the model. The experts also perform a trial run of the model to observe if the model corresponds to the datasets. It helps them determine whether the tools they have currently are going to sufficiently execute the model or if they need a more robust system for it to work properly.  Checkout: Data Analyst Salary in India Phase 5: Result Communication and Publication Remember the goal you had set for your business in phase 1? Now is the time to check if those criteria are met by the tests you have run in the previous phase. The communication step starts with a collaboration with major stakeholders to determine if the project results are a success or failure. The project team is required to identify the key findings of the analysis, measure the business value associated with the result, and produce a narrative to summarise and convey the results to the stakeholders. Phase 6: Measuring of Effectiveness As your data analytics lifecycle draws to a conclusion, the final step is to provide a detailed report with key findings, coding, briefings, technical papers/ documents to the stakeholders. Additionally, to measure the analysis’s effectiveness, the data is moved to a live environment from the sandbox and monitored to observe if the results match the expected business goal. If the findings are as per the objective, the reports and the results are finalized. However, suppose the outcome deviates from the intent set out in phase 1then. You can move backward in the data analytics lifecycle to any of the previous phases to change your input and get a different output. If there are any performative constraints in the model, then the team goes back to make adjustments to the model before deploying it.  Also Read: Data Analytics Project Ideas Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Importance of Data Analytics Lifecycle The Data Analytics Lifecycle outlines how data is created, gathered, processed, used, and analyzed to meet corporate objectives. It provides a structured method of handling data so that it may be transformed into knowledge that can be applied to achieve organizational and project objectives. The process offers the guidance and techniques needed to extract information from the data and move forward to achieve corporate objectives. Data analysts use the circular nature of the lifecycle to go ahead or backward with data analytics. They can choose whether to continue with their current research or abandon it and conduct a fresh analysis in light of the recently acquired insights. Their progress is guided by the Data Analytics lifecycle. Big Data Analytics Lifecycle example Take a chain of retail stores as an example, which seeks to maximize the prices of its products in order to increase sales. It is an extremely difficult situation because the retail chain has thousands of products spread over hundreds of sites. After determining the goal of the chain of stores, you locate the data you require, prepare it, and follow the big data analytics lifecycle. You see many types of clients, including regular clients and clients who make large purchases, such as contractors. You believe that finding a solution lies in how you handle different types of consumers. However, you must consult the customer team about this if you lack adequate knowledge To determine whether different client categories impact the model findings and obtain the desired output, you must first obtain a definition, locate data, and conduct hypothesis testing. As soon as you are satisfied with the model’s output, you may put it into use, integrate it into your operations, and then set the prices you believe to be the best ones for all of the store’s outlets. This is a small-scale example of how deploying the business analytics cycle can positively affect the profits of a business. But this model is used across huge business chains in the world.  Who uses Big data and analytics? Huge Data and analytics are being used by medium to large-scale businesses throughout the world to achieve great success. Big data analytics technically means the process of analyzing and processing a huge amount of data to find trends and patterns. This makes them able to quickly find solutions to problems by making fast and adequate decisions based on the data.  The king of online retail, Amazon, accesses consumer names, addresses, payments, and search history through its vast data bank and uses them in advertising algorithms and to enhance customer relations. The American Express Company uses big data to study consumer behavior. Capital One, a market leader, uses big data analysis to guarantee the success of its consumer offers. Netflix leverages big data to understand the viewing preferences of users from around the world. Spotify is a platform that is using the data analytics lifecycle in big data to its fullest. They use this method to make sure that each user gets their favourite type of music handed to them.  Big data is routinely used by companies like Marriott Hotels, Uber Eats, McDonald’s, and Starbucks as part of their fundamental operations. Benefits of Big data and analytics Learning the life cycle of data analytics gives you a competitive advantage. Businesses, be it large or small, can benefit a lot from big data effectively. Here are some of the benefits of Big data and analytics lifecycle. 1. Customer Loyalty and Retention Customers’ digital footprints contain a wealth of information regarding their requirements, preferences, buying habits, etc. Businesses utilize big data to track consumer trends and customize their goods and services to meet unique client requirements. This significantly increases consumer satisfaction, brand loyalty, and eventually, sales. Amazon has used this big data and analytics lifecycle to its advantage by providing the most customized buying experience, in which recommendations are made based on past purchases and items that other customers have purchased, browsing habits, and other characteristics. 2. Targeted and Specific Promotions With the use of big data, firms may provide specialized goods to their target market without spending a fortune on ineffective advertising campaigns. Businesses can use big data to study consumer trends by keeping an eye on point-of-sale and online purchase activity. Using these insights, targeted and specific marketing strategies are created to assist businesses in meeting customer expectations and promoting brand loyalty. 3. Identification of Potential Risks Businesses operate in high-risk settings and thus need efficient risk management solutions to deal with problems. Creating efficient risk management procedures and strategies depends heavily on big data. Big data analytics life cycle and tools quickly minimize risks by optimizing complicated decisions for unforeseen occurrences and prospective threats. 4. Boost Performance The use of big data solutions can increase operational effectiveness. Your interactions with consumers and the important feedback they provide enable you to gather a wealth of relevant customer data. Analytics can then uncover significant trends in the data to produce products that are unique to the customer. In order to provide employees more time to work on activities demanding cognitive skills, the tools can automate repetitive processes and tasks. 5. Optimize Cost One of the greatest benefits of the big data analytics life cycle is the fact that it can help you cut down on business costs. It is a proven fact that the return cost of an item is much more than the shipping cost. By using big data, companies can calculate the chances of the products being returned and then take the necessary steps to make sure that they suffer minimum losses from product returns.  Ways to Use Data Analytics Let’s delve into how this transformative data analysis stages can be harnessed effectively. Enhancing Decision-Making Data analytics life cycle sweeps away the fog of uncertainty, ushering in an era where decisions are grounded in insights rather than guesswork. Whether it’s selecting the most compelling content, orchestrating targeted marketing campaigns, or shaping innovative products, organizations leverage data analysis life cycle to drive informed decision-making. The result? Better outcomes and heightened customer satisfaction. Elevating Customer Service Customizing customer service to individual needs is no longer a lofty aspiration but a tangible reality with data analytics. The power of personalization, fueled by analyzed data, fosters stronger customer relationships. Insights into customers’ interests and concerns enable businesses to offer more than just products – they provide tailored recommendations, creating a personalized journey that resonates with customers. Efficiency Unleashed In the realm of operational efficiency, the life cycle of data analytics or data analytics phases emerges as a key ally. Streamlining processes, cutting costs, and optimizing production become achievable feats with a profound understanding of audience preferences. As the veil lifts on what captivates your audience, valuable time and resources are saved, ensuring that efforts align seamlessly with audience interests. Mastering Marketing Data analytics life cycle or data analytics phases empowers businesses to unravel the performance tapestry of their marketing campaigns. Insights gleaned allow for meticulous adjustments and fine-tuning strategies for optimal results. Beyond this, identifying potential customers primed for interaction and conversion becomes a strategic advantage. The precision of data analytics life cycle ensures that every marketing endeavor resonates with the right audience, maximizing impact. Data Analytics Tools Python: A Versatile and Open-Source Programming Language Python stands out as a powerful and open-source programming language that excels in object-oriented programming. This language offers a diverse array of libraries tailored for data manipulation, visualization, and modeling. With its flexibility and ease of use, Python has become a go-to choice for programmers and data scientists alike. R: Unleashing Statistical Power through Open Source Programming R, another open-source programming language, specializes in numerical and statistical analysis. It boasts an extensive collection of libraries designed for data analysis and visualization. Widely embraced by statisticians and researchers, R provides a robust platform for delving into the intricacies of data with precision and depth. Tableau: Crafting Interactive Data Narratives Enter Tableau, a simplified yet powerful tool for data visualization and analytics. Its user-friendly interface empowers users to create diverse visualizations, allowing for interactive data exploration. With the ability to build reports and dashboards, Tableau transforms data into compelling narratives, presenting insights and trends in a visually engaging manner. Power BI: Empowering Business Intelligence with Ease Power BI emerges as a business intelligence powerhouse with its drag-and-drop functionality. This tool seamlessly integrates with multiple data sources and entices users with visually appealing features. Beyond its aesthetics, Power BI facilitates dynamic interactions with data, enabling users to pose questions and obtain immediate insights, making it an indispensable asset for businesses. QlikView: Unveiling Interactive Analytics and Guided Insights QlikView distinguishes itself by offering interactive analytics fueled by in-memory storage technology. This enables the analysis of vast data volumes and empowers users with data discoveries that guide decision-making. The platform excels in manipulating massive datasets swiftly and accurately, making it a preferred choice for those seeking robust analytics capabilities. Apache Spark: Real-Time Data Analytics Powerhouse Apache Spark, an open-source life cycle of data analytics engine, steps into the arena to process data in real-time. It executes sophisticated analytics through SQL queries and machine learning algorithms. With its prowess, Apache Spark addresses the need for quick and efficient data processing, making it an invaluable tool in the world of big data. SAS: Statistical Analysis and Beyond SAS, a statistical phases of data analysis software, proves to be a versatile companion for data enthusiasts. It facilitates analytics, data visualization, SQL queries, statistical analysis, and the development of machine learning models for predictive insights. SAS stands as a comprehensive solution catering to a spectrum of data-related tasks, making it an indispensable tool for professionals in the field. What are the Applications of Data Analytics? In the dynamic landscape of the digital era, business analytics life cycle applications play a pivotal role in extracting valuable insights from vast datasets. These applications empower organizations across various sectors to make informed decisions, enhance efficiency, and gain a competitive edge. Let’s delve into the diverse applications of business analytics life cycle and their impact on different domains. Business Intelligence Data analytics lifecycle case study applications serve as the backbone of Business Intelligence (BI), enabling businesses to transform raw data into actionable intelligence. Through sophisticated analysis, companies can identify trends, customer preferences, and market dynamics. This information aids in strategic planning, helping businesses stay ahead of the curve and optimize their operations for sustained success. Healthcare In the healthcare sector, data analytics applications contribute significantly to improving patient outcomes and operational efficiency. By analyzing patient records, treatment outcomes, and demographic data, healthcare providers can make data-driven decisions, personalize patient care, and identify potential health risks. This not only enhances the quality of healthcare services but also helps in preventing and managing diseases more effectively. Finance and Banking Financial institutions harness the power of data analytics applications or data analytics life cycles for example to manage risk, detect fraudulent activities, and make informed investment decisions. Analyzing market trends and customer behavior allows banks to offer personalized financial products, streamline operations, and ensure compliance with regulatory requirements. This, in turn, enhances customer satisfaction and builds trust within the financial sector. E-Commerce In the realm of e-commerce, data analytics applications revolutionize the way businesses understand and cater to customer needs. By analyzing purchasing patterns, preferences, and browsing behavior, online retailers can create targeted marketing strategies, optimize product recommendations, and enhance the overall customer shopping experience. This leads to increased customer satisfaction and loyalty. Education Data analytics applications are transforming the education sector by providing insights into student performance, learning trends, and institutional effectiveness. Educators can tailor their teaching methods based on data-driven assessments, identify areas for improvement, and enhance the overall learning experience. This personalized approach fosters student success and contributes to the continuous improvement of educational institutions. Manufacturing and Supply Chain In the manufacturing industry, data analytics applications optimize production processes, reduce downtime, and improve overall efficiency. By analyzing supply chain data, manufacturers can forecast demand, minimize inventory costs, and enhance product quality. This results in streamlined operations, reduced wastage, and increased competitiveness in the market. Conclusion The data analytics lifecycle is a circular process that consists of six basic stages that define how information is created, gathered, processed, used, and analyzed for business goals. However, the ambiguity in having a standard set of phases for data analytics architecture does plague data experts in working with the information. But the first step of mapping out a business objective and working toward achieving them helps in drawing out the rest of the stages. upGrad’s Executive PG Programme in Data Science in association with IIIT-B and a certification in Business Analytics covers all these stages of data analytics architecture. The program offers detailed insight into the professional and industry practices and 1-on-1 mentorship with several case studies and examples. Hurry up and register now!

by Rohit Sharma

19 Feb 2024