Blogs

142465

Association Rule Mining in data mining, as the name suggests, involves discovering relationships between seemingly independent relational databases or other data repositories through simple If/Then statements.  As someone deeply involved in data analysis, I find association rule mining fascinating. While many machine learning algorithms operate with numeric datasets, association rule mining is tailored for non-numeric, categorical data. It involves more than simple counting but is relatively straightforward compared to complex mathematical models.  In my experience, the procedure aims to identify frequently occurring patterns, correlations, or associations in datasets across various relational and transactional databases. Association rule mining in machine learning is crucial in extracting valuable insights from data, especially in scenarios where traditional mathematical approaches may not be suitable.  What is Association Rule Mining? The Association rule is a learning technique that helps identify the dependencies between two data items. Based on the dependency, it then maps accordingly so that it can be more profitable. Association rule furthermore looks for interesting associations among the variables of the dataset. It is undoubtedly one of the most important concepts of Machine Learning and has been used in different cases such as association in data mining and continuous production, among others. However, like all other techniques, association in data mining, too, has its own set of disadvantages. The same has been discussed in brief in this article. An association rule has 2 parts: an antecedent (if) and a consequent (then) An antecedent is something that’s found in data, and a consequent is an item that is found in combination with the antecedent. Have a look at this rule for instance: “If a customer buys bread, he’s 70% likely of buying milk.” In the above association rule, bread is the antecedent and milk is the consequent. Simply put, it can be understood as a retail store’s association rule to target their customers better. If the above rule is a result of a thorough analysis of some data sets, it can be used to not only improve customer service but also improve the company’s revenue. Association rules are created by thoroughly analyzing data and looking for frequent if/then patterns. Then, depending on the following two parameters, the important relationships are observed: Support: Support indicates how frequently the if/then relationship appears in the database. Confidence: Confidence tells about the number of times these relationships have been found to be true. Must read: Free excel courses! So, in a given transaction with multiple items, Association Rule Mining primarily tries to find the rules that govern how or why such products/items are often bought together. For example, peanut butter and jelly are frequently purchased together because a lot of people like to make PB&J sandwiches. Learn Data Science Courses online at upGrad Association Rule Mining is sometimes referred to as “Market Basket Analysis”, as it was the first application area of association mining. The aim is to discover associations of items occurring together more often than you’d expect from randomly sampling all the possibilities. The classic anecdote of Beer and Diaper will help in understanding this better. The story goes like this: young American men who go to the stores on Fridays to buy diapers have a predisposition to grab a bottle of beer too. However unrelated and vague that may sound to us laymen, association rule mining shows us how and why! Let’s do a little analytics ourselves, shall we? Suppose an X store’s retail transactions database includes the following data: Total number of transactions: 600,000 Transactions containing diapers: 7,500 (1.25 percent) Transactions containing beer: 60,000 (10 percent) Transactions containing both beer and diapers: 6,000 (1.0 percent) From the above figures, we can conclude that if there was no relation between beer and diapers (that is, they were statistically independent), then we would have got only 10% of diaper purchasers to buy beer too. However, as surprising as it may seem, the figures tell us that 80% (=6000/7500) of the people who buy diapers also buy beer. This is a significant jump of 8 over what was the expected probability. This factor of increase is known as Lift – which is the ratio of the observed frequency of co-occurrence of our items and the expected frequency. How did we determine the lift? Simply by calculating the transactions in the database and performing simple mathematical operations. So, for our example, one plausible association rule can state that the people who buy diapers will also purchase beer with a Lift factor of 8. If we talk mathematically, the lift can be calculated as the ratio of the joint probability of two items x and y, divided by the product of their probabilities. Lift = P(x,y)/[P(x)P(y)] However, if the two items are statistically independent, then the joint probability of the two items will be the same as the product of their probabilities. Or, in other words, P(x,y)=P(x)P(y), which makes the Lift factor = 1. An interesting point worth mentioning here is that anti-correlation can even yield Lift values less than 1 – which corresponds to mutually exclusive items that rarely occur together. Association Rule Mining has helped data scientists find out patterns they never knew existed. Basic Fundamentals of Statistics for Data Science How does Association Rule Learning work?   Association rule learning is a machine learning method that helps recognize interesting relations or associations between extracts within large information sets. This approach is mainly applied in data mining and business intelligence to discover relationships, associations, and dependencies between different sets. Here’s a comprehensive explanation of how Association rule learning works:  Input Data:  Rule-based reasoning involves a dataset comprising various transactions or instances and items. A transaction is like a supermarket basket, its representation of items, and the algorithm seeks to find out the rules about this arrangement of things.  Support and Confidence Metrics:  The support quantifies how often itemsets co-occur in the dataset, reflecting the frequency with which a particular collection of items appears together. Confidence, in contrast, measures the chance of an item being present given that another is there for association rule.  Apriori Algorithm:  One of the popular algorithms employed for association data mining is the Apriori algorithm. It runs in numerous repetitive steps, commencing with finding items that are shared frequently and developing those findings to obtain a more sophisticated set of patterns.  Frequent Itemset Generation:  First of all, the algorithm detects such frequent itemset for which a specified minimum support threshold is maintained. It omits rare itemset and only considers those items that appear quite often in the dataset.  Rule Generation:  This is then followed by the generation of association rules from these frequent itemsets. These rules are written in a format of ‘If –Then,’ thereby containing data explaining the relationship between item sets. The co-occurrence patterns that are found in the data can be visualized by means of rules.  Evaluation and Pruning:  By evaluation using confidence, rules below a certain specified con-fi-dence threshold will be pruned out. Pruning also assists in eliminating regulations that are less meaningful, so pruning ensures only beneficial and significant relations are maintained.  Interpretation and Application:  After transforming and mining, the association rules are invoked to be understood either by data analysts or domain experts. These rules provide significant information about interactions of elements, helping with decision-making procedures in diverse areas.  Types Of Association Rules In Data Mining There are typically four different types of association rules in data mining. They are Multi-relational association rules Generalized Association rule Interval Information Association Rules Quantitative Association Rules Multi-Relational Association Rule Also known as MRAR, multi-relational association rule is defined as a new class of association rules that are usually derived from different or multi-relational databases. Each rule under this class has one entity with different relationships that represent the indirect relationships between entities.  Generalized Association Rule Moving on to the next type of association rule, the generalized association rule is largely used for getting a rough idea about the interesting patterns that often tend to stay hidden in data.  Quantitative Association Rules This particular type is actually one of the most unique kinds of all the four association rules available. What sets it apart from the others is the presence of numeric attributes in at least one attribute of quantitative association rules. This is in contrast to the generalized association rule, where the left and right sides consist of categorical attributes.  Algorithms Of Associate Rule In Data Mining There are mainly three different types of algorithms that can be used to generate associate rules in data mining. Let’s take a look at them. Apriori Algorithm Apriori algorithm identifies the frequent individual items in a given database and then expands them to larger item sets, keeping in check that the item sets appear sufficiently often in the database.  Eclat Algorithm ECLAT algorithm is also known as Equivalence Class Clustering and bottomup. Latice Traversal is another widely used method for associate rule in data mining. Some even consider it to be a  better and more efficient version of the Apriori algorithm. FP-growth Algorirthm Also known as the recurring pattern, this algorithm is particularly useful for finding frequent patterns without the need for candidate generation. It mainly operates in two stages namely, FP-tree construction and extract frequently used item sets.  Now that you have a basic understanding of what is association rule,  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 Let’s look at some areas where Association Rule Mining has helped quite a lot: 1. Market Basket Analysis: This is the most typical example of association mining. Data is collected using barcode scanners in most supermarkets. This database, known as the “market basket” database, consists of a large number of records on past transactions. A single record lists all the items bought by a customer in one sale. Knowing which groups are inclined towards which set of items gives these shops the freedom to adjust the store layout and the store catalog to place the optimally concerning one another. 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 The purpose of ARM analysis is to characterise the most intriguing patterns effectively. The Market Basket Analysis or MBA, often referred to as the ARM analysis, is a technique for identifying consumer patterns by mining associations from store transactional databases. Each and every commodity today includes a bar code. The corporate sector quickly documents this information as having enormous potential worth in marketing. Commercial businesses are particularly interested in “association rules” that pinpoint the trends such that the inclusion of one thing in a basket denotes the acquisition of one or more subsequent items. The outcomes of this “market basket analysis” can then be utilised to suggest product pairings. This helps managers in making efficient decisions.  Methods for Data Mining (DM) are also used to identify groups of items that are bought at the same time. Choosing which goods to place next to one another on store shelves might assist raise sales significantly. The following two phases can be used to decompose the ARM issue. Find groups of objects or item sets with operation support higher than specified minimum support. Recurring item sets are those that have the minimum support. To generate frequent patterns for databases, use large item sets. 2. Medical Diagnosis: Association rules in medical diagnosis can be useful for assisting physicians for curing patients. Diagnosis is not an easy process and has a scope of errors which may result in unreliable end-results. Using relational association rule mining, we can identify the probability of the occurrence of illness concerning various factors and symptoms. Further, using learning techniques, this interface can be extended by adding new symptoms and defining relationships between the new signs and the corresponding diseases. Must read: Data structures and algorithm free! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 3. Census Data: Every government has tonnes of census data. This data can be used to plan efficient public services(education, health, transport) as well as help public businesses (for setting up new factories, shopping malls, and even marketing particular products). This application of association rule mining and data mining has immense potential in supporting sound public policy and bringing forth an efficient functioning of a democratic society. Our learners also read: Free Online Python Course for Beginners 4. Protein Sequence: Proteins are sequences made up of twenty types of amino acids. Each protein bears a unique 3D structure which depends on the sequence of these amino acids. A slight change in the sequence can cause a change in structure which might change the functioning of the protein. This dependency of the protein functioning on its amino acid sequence has been a subject of great research. Earlier it was thought that these sequences are random, but now it’s believed that they aren’t. Nitin Gupta, Nitin Mangal, Kamal Tiwari, and Pabitra Mitra have deciphered the nature of associations between different amino acids that are present in a protein. Knowledge and understanding of these association rules will come in extremely helpful during the synthesis of artificial proteins. 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? 5.Building an Intelligent Transportation System The Intelligent Transportation System (ITS) integrates cutting-edge beam technology, intelligent technology, and switch technology across the board. A flexible, precise, on-time, and organised interconnected transportation controlling system is the foundation of an intelligent transportation system. The advanced traffic system (ITS) is put together on an informative network and created using sensors in parking lots and weather centres, cars, transfer stations, and transmission equipment to carry information centres throughout the traffic information. The system gathers all the data by analysing real-time data on traffic conditions, parking availability, and other travel-related information. The system then uses the data to choose the best routes. The following requirements should be met for the application of ITS:  Credible, correct, and genuine road and traffic data collection. Efficient, reliable information exchange between traffic management and road management facilities.  The use of self-learning software applications by traffic toll management centres. decide on the route choices. 6.Recommendation Systems: Association rule learning in data mining is used by online platforms in their recommendation systems. Through assessing user patterns and establishing correlations between the tastes of these users and what they do, such systems recommend appropriate products, services, or content. This increases the involvement and satisfaction of users, which turns into money if it comes to recommendations.  7.Fraud Detection:  The application of various association’ rule learning approaches’ is also a significant part of the development mechanism for detecting frauds related to financial transactions. Through patterns, it detects irregularities in activities that are not legitimate in the system, like atypical spending habits or transactions, facilitating early warning, preventing fraud, and ensuring sound financial health for institutions while protecting customers.  Best tools for Association Rule Mining The best way to understand what is association rule mining, is by understanding its tools and how they work. Associate Rule is known as Association Rule Mining, where it uses diverse models and tools to analyse patterns in data sets. Association Rules in Data Mining has some amazing tools. We have a list of some amazing open-source tools that are great for working with Association Rules in Data Mining. WEKA – Waikato Environment for Knowledge Analysis Another free and open-source tool for Association Rule in Data Mining is WEKA. A graphic user interface or common terminal programmes can be used to access it. Additionally, it is accessible through a Java API and utilised for data preparation, Machine Learning algorithm development, and visualisation of data on just about any system. WEKA includes a number of ML techniques that may be used to address actual data mining issues. RapidMiner Another well-known open-source advanced analytic tool is RapidMiner. It is known for its user-friendly visual interface. It enables users to connect to any source of data, including social networking, cloud storage, commercial applications, and corporate data stores. Additionally, in order to prepare the data and analysis, RapidMiner includes automatic in-database processing. It is a great tool for Association Rule in Data Mining. Orange An open-source tool called Orange is used primarily for data processing and display. Orange is used to explore and preprocess data and association rule mining in python as it is also used as a modelling tool that was written in Python. In Orange, one must choose the add-on to install “Associate” in order to make use of ARM. These add-ons will also enable network analysis, text mining, and NLP in addition to it. Orange is one of the most popular tools for Association Rule in Data Mining.  Associate Rule is known as affinity analysis as well, which leverages these tools to find all possible patterns and co-occurrences. These tools should be enough to answer your questions and doubt regarding what is association rule mining and how it works! Conclusion In wrapping up, I must emphasize the significance of association rule mining in extracting meaningful insights from complex datasets. Throughout our exploration, we’ve seen how this technique uncovers valuable patterns and dependencies, guiding decisions across industries. From market basket analysis to medical diagnosis, association rule mining is vital in optimizing strategies and driving innovation. Understanding its types and algorithms empowers us to navigate the data landscape effectively. With tools like WEKA, RapidMiner, and Orange, we can unlock the full potential of data-driven decision-making. In essence, association rule mining in data mining is a cornerstone of modern analytics, enabling us to harness the power of data for transformative impact.  If you happen to have any doubts, queries, or suggestions – do drop them in the comments below!

by Abhinav Rai

13 Jul 2024

Blogs

187104

Lately the term ‘Big Data’ has been under the limelight, but not many people know what is big data. Businesses, governmental institutions, HCPs (Health Care Providers), and financial as well as academic institutions, are all leveraging the power of Big Data to enhance business prospects along with improved customer experience. Simply Stating, What Is Big Data? Simply stating, big data is a larger, complex set of data acquired from diverse, new, and old sources of data. The data sets are so voluminous that traditional software for data processing cannot manage it. Such massive volumes of data are generally used to address problems in business you might not be able to handle. IBM maintains that businesses around the world generate nearly 2.5 quintillion bytes of data daily! Almost 90% of the global data has been produced in the last 2 years alone. So we know for sure that the best way to answer ‘what is big data’ is mentioning that it has penetrated almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the globe. But, at this point, it is important to know what is big data? Lets talk about big data, characteristics of big data, types of big data and a lot more. Check out our free courses to get an edge over the competition. 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 You won’t belive how this Program Changed the Career of Students What is Big Data? Gartner Definition  According to Gartner, the definition of Big Data –  “Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” This definition clearly answers the “What is Big Data?” question – Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations. However, there are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data: It refers to a massive amount of data that keeps on growing exponentially with time. It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques. It includes data mining, data storage, data analysis, data sharing, and data visualization. The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data. Big Data Applications That Surround You Types of Big Data Now that we are on track with what is big data, let’s have a look at the types of big data: Structured Structured is one of the types of big data and By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner.  Read: Big data engineering jobs and its career opportunities What is big data technology and its types? Structured one of the types of big data is easy to input, store, query and analyze thanks to its predefined data model and schema. Most traditional databases and spreadsheets hold structured data like tables, rows, and columns. This makes it simple for analysts to run SQL queries and extract insights using familiar BI tools. However, structuring data requires effort and expertise during the design phase. As data volumes grow to petabyte scale, rigid schemas become impractical and limit the flexibility needed for emerging use cases. Also some data like text, images, video etc. cannot be neatly organized in tabular formats. Therefore, while structured data brings efficiency, scale and variety of big data necessitates semi-structured and unstructured types of digital data in big data to overcome these limitations. The value lies in consolidating these multiple types rather than relying solely on structured data for modern analytics. Unstructured Unstructured data refers to the data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important types of big data. Unstructured types of big data constitutes over 80% of data generated today and continues to grow exponentially from sources like social posts, digital images, videos, audio files, emails, and more. It does not conform to any data model, so conventional tools cannot give meaningful insights from it. However, unstructured data tends to be more subjective, rich in meaning, and reflective of human communication compared to tabular transaction data.  With immense business value hidden inside, specialized analytics techniques involving NLP, ML, and AI are essential to process high volumes of unstructured content. For instance, sentiment analysis of customer social media rants can alert companies to issues before mainstream notice. Text mining of maintenance logs and field technician reports can improve future product designs. And computer vision techniques on image data from manufacturing floors can automate quality checks. While analysis requires advanced skill, the unstructured data’s scale, variety, and information density deliver new opportunities for competitive advantage across industries. Check out the big data courses at upGrad Semi-structured Semi structured is the third type of big data. Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Thus we come to the end of types of data. Lets discuss the characteristics of data. 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 Semi-structured variety in big data includes elements of both structured and unstructured data. For example, XML, JSON documents contain tags or markers to separate semantic elements, but the data is unstructured free flowing text, media, etc. Clickstream data from website visits have structured components like timestamps and pages visited, but the path a user takes is unpredictable. Sensor data with timestamped values is semi-structured. This hybrid data abstraction effortlessly incorporates the variety and volume of big data across system interfaces.  For analytic applications, semi-structured data poses technical and business-level complexities for processing, governance, and insight generation. However, flexible schemas and object-oriented access methods are better equipped to handle velocity and variety in semi-structured types of digital data in big data at scale. With rich contextual information encapsulated, established databases have expanded native JSON, XML, and Graph support for semi-structured data to serve modern real-time analytics needs. Characteristics of Big Data Back in 2001, Gartner analyst Doug Laney listed the 3 ‘V’s of Big Data – Variety, Velocity, and Volume. Let’s discuss the characteristics of big data. These characteristics, isolatedly, are enough to know what is big data. Let’s look at them in depth: 1) Variety Variety of Big Data refers to structured, unstructured, and semistructured data that is gathered from multiple sources. While in the past, data could only be collected from spreadsheets and databases, today data comes in an array of forms such as emails, PDFs, photos, videos, audios, SM posts, and so much more. Variety is one of the important characteristics of big data. The traditional types of data are structured and also fit well in relational databases. With the rise of big data, the data now comes in the form of new unstructured types. These unstructured, as well as semi-structured data types, need additional pre-processing for deriving meaning and support of metadata. 2) Velocity Velocity essentially refers to the speed at which data is being created in real-time. In a broader prospect, it comprises the rate of change, linking of incoming data sets at varying speeds, and activity bursts. The speed of data receipt and action is simply known as velocity. The highest velocity for data will stream directly into the memory against being written to the disk. Few internet-based smart products do operate in real-time or around real-time. This mostly requires evaluation as well as in real-time. Learn: Mapreduce in big data The velocity of variety in big data is crucial because it allows companies to make quick, data-driven decisions based on real-time insights. As data streams in at high speeds from sources like social media, sensors, mobile devices, etc., companies can spot trends, detect patterns, and derive meaning from that data more rapidly. High velocity characteristics of big data combined with advanced analytics enables faster planning, problem detection, and decision optimization. For example, a company monitoring social media chatter around its brand can quickly respond to emerging issues before they spiral out of control. 3) Volume Volume is one of the characteristics of big data. We already know that Big Data indicates huge ‘volumes’ of data that is being generated on a daily basis from various sources like social media platforms, business processes, machines, networks, human interactions, etc. Such a large amount of data are stored in data warehouses. Thus comes to the end of characteristics of big data. The data volume matters when you discuss the big data characteristics. In the context of big data, you will need to process a very high volume of low-density or unstructured data. This will be data related to an unknown value. Example data feeds on Twitter, clickstreams on web pages or mobile apps, or even sensor-based equipment. For a few organizations, it means ten times a few terabytes of data. For some others, it could mean hundreds of times petabytes. Big Data Roles and Salaries in the Finance Industry Advantages of Big Data (Features) One of the biggest advantages of Big Data is predictive analysis. Big Data analytics tools can predict outcomes accurately, thereby, allowing businesses and organizations to make better decisions, while simultaneously optimizing their operational efficiencies and reducing risks. By harnessing data from social media platforms using Big Data analytics tools, businesses around the world are streamlining their digital marketing strategies to enhance the overall consumer experience. Big Data provides insights into the customer pain points and allows companies to improve upon their products and services. Being accurate, Big Data combines relevant data from multiple sources to produce highly actionable insights. Almost 43% of companies lack the necessary tools to filter out irrelevant data, which eventually costs them millions of dollars to hash out useful data from the bulk. Big Data tools can help reduce this, saving you both time and money. Big Data analytics could help companies generate more sales leads which would naturally mean a boost in revenue. Businesses are using Big Data analytics tools to understand how well their products/services are doing in the market and how the customers are responding to them. Thus, the can understand better where to invest their time and money. With Big Data insights, you can always stay a step ahead of your competitors. You can screen the market to know what kind of promotions and offers your rivals are providing, and then you can come up with better offers for your customers. Also, Big Data insights allow you to learn customer behaviour to understand the customer trends and provide a highly ‘personalized’ experience to them. Read: Career Scope for big data jobs. 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 Who is using Big Data? 5 Applications The people who’re using Big Data know better that, what is Big Data. Let’s look at some such industries: 1) Healthcare Big Data has already started to create a huge difference in the healthcare sector. With the help of predictive analytics, medical professionals and HCPs are now able to provide personalized healthcare services to individual patients. Apart from that, fitness wearables, telemedicine, remote monitoring – all powered by Big Data and AI – are helping change lives for the better. The healthcare industry is harnessing big data in various innovative ways – from detecting diseases faster to providing better treatment plans and preventing medication errors. By analyzing patient history, clinical data, claims data, and more, healthcare providers can better understand patient risks, genetic factors, environmental factors to customize treatments rather than follow a one-size-fits-all approach. Population health analytics on aggregated EMR data also allows hospitals to reduce readmission rates and unnecessary costs. Pharmaceutical companies are leveraging big data to improve drug formulation, identify new molecules, and reduce time-to-market by analyzing years of research data. The insights from medical imaging data combined with genomic data analysis enables precision diagnosis at early stages. 2) Academia Big Data is also helping enhance education today. Education is no more limited to the physical bounds of the classroom – there are numerous online educational courses to learn from. Academic institutions are investing in digital courses powered by Big Data technologies to aid the all-round development of budding learners. Educational institutions are leveraging big data in dbms in multifaceted ways to elevate learning experiences and optimize student outcomes. By analyzing volumes of student academic and behavioral data, predictive models identify at-risk students early to recommend timely interventions. Tailored feedback is provided based on individual progress monitoring. Curriculum design and teaching practices are refined by assessing performance patterns in past course data. Self-paced personalized learning platforms powered by AI recommend customized study paths catering to unique learner needs and competency levels. Academic corpus and publications data aids cutting-edge research and discovery through knowledge graph mining and natural language queries. Knowledge Read: Big data jobs & Career planning 3) Banking The banking sector relies on Big Data for fraud detection. Big Data tools can efficiently detect fraudulent acts in real-time such as misuse of credit/debit cards, archival of inspection tracks, faulty alteration in customer stats, etc. Banks and financial institutions depend heavily on big data in dbms and analytics to operate services, reduce risks, retain customers, and increase profitability. Predictive models flag probable fraudulent transactions in seconds before completion by scrutinizing volumes of past transactional data, customer information, credit history, investments, and third-party data. Connecting analytics to the transaction processing pipeline has immensely reduced false declines and improved fraud detection rates. Client analytics helps banks precisely segment customers, contextualise engagement through the right communication channels, and accurately anticipate their evolving needs to recommend the best financial products. Processing volumes of documentation and loan application big data types faster using intelligent algorithms and automation enables faster disbursal with optimized risks. Trading firms leverage big data analytics on historical market data, economic trends, and news insights to support profitable investment decisions. Thus, big data radically enhances banking experiences by minimizing customer risks and maximizing personalisation through every engagement. 4) Manufacturing According to TCS Global Trend Study, the most significant benefit of Big Data in manufacturing is improving the supply strategies and product quality. In the manufacturing sector, Big data helps create a transparent infrastructure, thereby, predicting uncertainties and incompetencies that can affect the business adversely. Manufacturing industries are optimizing end-to-end value chains using volumes of operational data generated from sensors, equipment logs, inventory flows, supplier networks, and customer transactions. By combining this real-time structured and unstructured big data types with enterprise data across siloed sources, manufacturers gain comprehensive visibility into operational performance, production quality, supply-demand dynamics, and fulfillment. Advanced analytics transforms this data into meaningful business insights around minimizing process inefficiencies, improving inventory turns, reducing machine failures, shortening production cycle times, and meeting dynamic customer demands continually. Overall, equipment effectiveness is improved with predictive maintenance programs. Data-based simulation, scheduling, and control automation increases speed, accuracy, and compliance. Real-time synchronization of operations planning with execution enabled by big data analytics creates the responsive and intelligent factory of the future. 5) IT One of the largest users of Big Data, IT companies around the world are using Big Data to optimize their functioning, enhance employee productivity, and minimize risks in business operations. By combining Big Data technologies with ML and AI, the IT sector is continually powering innovation to find solutions even for the most complex of problems. Planning a Big Data Career? Know All Skills, Roles & Transition Tactics! The technology and IT sectors pioneer big data-enabled transformations across other industries, though the first application starts from within. IT infrastructure performance, application usage telemetry, network traffic data, security events, and business KPIs provide technology teams with comprehensive observability into systems health, utilization, gaps and dependencies. This drives data-based capacity planning, proactive anomaly detection and accurate root cause analysis to optimize IT service quality and employee productivity. User behavior analytics identifies the most valued features and pain points to prioritize software enhancements aligned to business needs. For product companies, big data analytics features of big data logs, sensor data, and customer usage patterns enhances user experiences by detecting issues and churn faster. Mining years of structured and unstructured data aids context-aware conversational AI feeding into chatbots and virtual assistants. However, robust information management and governance practices remain vital as the scale and complexity of technology data environments continue to expand massively. With positive business outcomes realized internally, IT domain expertise coupled with analytics and AI skillsets power data transformation initiatives across external customer landscapes. 6. Retail Big Data has changed the way of working in traditional brick and mortar retail stores. Over the years, retailers have collected vast amounts of data from local demographic surveys, POS scanners, RFID, customer loyalty cards, store inventory, and so on. Now, they’ve started to leverage this data to create personalized customer experiences, boost sales, increase revenue, and deliver outstanding customer service. Retailers are even using smart sensors and Wi-Fi to track the movement of customers, the most frequented aisles, for how long customers linger in the aisles, among other things. They also gather social media data to understand what customers are saying about their brand, their services, and tweak their product design and marketing strategies accordingly.  7. Transportation  Big Data Analytics holds immense value for the transportation industry. In countries across the world, both private and government-run transportation companies use Big Data technologies to optimize route planning, control traffic, manage road congestion, and improve services. Additionally, transportation services even use Big Data to revenue management, drive technological innovation, enhance logistics, and of course, to gain the upper hand in the market. The transportation sector is adopting big data and IoT technologies to monitor, analyse, and optimize end-to-end transit operations intelligently. Transport authorities can dynamically control traffic flows, mitigating congestion, optimising tolls, and identifying incidents faster by processing high-velocity telemetry data streams from vehicles, roads, signals, weather systems, and rider mobile devices. Journey reliability and operational efficiency are improved through data-based travel demand prediction, dynamic route assignment, and AI-enabled dispatch. Predictive maintenance reduces equipment downtime. Riders benefit from real-time tracking, estimated arrivals, and personalized alerts, minimising wait times. Logistics players leverage big data for streamlined warehouse management, load planning, and shipment route optimisation, driving growth and customer satisfaction. However, key challenges around data quality, privacy, integration, and skills shortage persist. They need coordinated efforts from policymakers and technology partners before their sustainable value is fully realised across an integrated transportation ecosystem. Big Data Case studies 1. Walmart  Walmart leverages Big Data and Data Mining to create personalized product recommendations for its customers. With the help of these two emerging technologies, Walmart can uncover valuable patterns showing the most frequently bought products, most popular products, and even the most popular product bundles (products that complement each other and are usually purchased together). Based on these insights, Walmart creates attractive and customized recommendations for individual users. By effectively implementing Data Mining techniques, the retail giant has successfully increased the conversion rates and improved its customer service substantially. Furthermore, Walmart uses Hadoop and NoSQL technologies to allow customers to access real-time data accumulated from disparate sources.  2. American Express The credit card giant leverages enormous volumes of customer data to identify indicators that could depict user loyalty. It also uses Big Data to build advanced predictive models for analyzing historical transactions along with 115 different variables to predict potential customer churn. Thanks to Big Data solutions and tools, American Express can identify 24% of the accounts that are highly likely to close in the upcoming four to five months.  3. General Electric In the words of Jeff Immelt, Chairman of General Electric, in the past few years, GE has been successful in bringing together the best of both worlds – “the physical and analytical worlds.” GE thoroughly utilizes Big Data. Every machine operating under General Electric generates data on how they work. The GE analytics team then crunches these colossal amounts of data to extract relevant insights from it and redesign the machines and their operations accordingly. Today, the company has realized that even minor improvements, no matter how small, play a crucial role in their company infrastructure. According to GE stats, Big Data has the potential to boost productivity by 1.5% in the US, which compiled over a span of 20 years could increase the average national income by a staggering 30%! 4. Uber  Uber is one of the major cab service providers in the world. It leverages customer data to track and identify the most popular and most used services by the users. Once this data is collected, Uber uses data analytics to analyze the usage patterns of customers and determine which services should be given more emphasis and importance. Apart from this, Uber uses Big Data in another unique way. Uber closely studies the demand and supply of its services and changes the cab fares accordingly. It is the surge pricing mechanism that works something like this – suppose when you are in a hurry, and you have to book a cab from a crowded location, Uber will charge you double the normal amount!   5. Netflix Netflix is one of the most popular on-demand online video content streaming platform used by people around the world. Netflix is a major proponent of the recommendation engine. It collects customer data to understand the specific needs, preferences, and taste patterns of users. Then it uses this data to predict what individual users will like and create personalized content recommendation lists for them. Today, Netflix has become so vast that it is even creating unique content for users. Data is the secret ingredient that fuels both its recommendation engines and new content decisions. The most pivotal data points used by Netflix include titles that users watch, user ratings, genres preferred, and how often users stop the playback, to name a few. Hadoop, Hive, and Pig are the three core components of the data structure used by Netflix.  6. Procter & Gamble Procter & Gamble has been around us for ages now. However, despite being an “old” company, P&G is nowhere close to old in its ways. Recognizing the potential of Big Data, P&G started implementing Big Data tools and technologies in each of its business units all over the world. The company’s primary focus behind using Big Data was to utilize real-time insights to drive smarter decision making. To accomplish this goal, P&G started collecting vast amounts of structured and unstructured data across R&D, supply chain, customer-facing operations, and customer interactions, both from company repositories and online sources. The global brand has even developed Big Data systems and processes to allow managers to access the latest industry data and analytics. 7. IRS Yes, even government agencies are not shying away from using Big Data. The US Internal Revenue Service actively uses Big Data to prevent identity theft, fraud, and untimely payments (people who should pay taxes but don’t pay them in due time). The IRS even harnesses the power of Big Data to ensure and enforce compliance with tax rules and laws. As of now, the IRS has successfully averted fraud and scams involving billions of dollars, especially in the case of identity theft. In the past three years, it has also recovered over US$ 2 billion. Careers In Big Data Big data characteristics are seemingly transforming the way businesses work while also driving growth through the economy globally. Businesses are observing immense benefits using the characteristics of big data for protecting their database, aggregating huge volumes of information, as well as making informed decisions to benefit organizations. No wonder it is clear that big data has a huge range across a number of sectors. For instance, in the financial industry, big data comes across as a vital tool that helps make profitable decisions. Similarly, some data organizations might look at big data as a means for fraud protection and pattern detection in large-sized datasets. Nearly every large-scale organization currently seeks talent in big data, and hopefully, the demand is prone to a significant rise in the future as well. 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? Wrapping Up We hope we were able to answer the “What is Big Data?” question clearly enough. We hope you understood about the types of big data, characteristics of big data, use cases, etc.  Organizations actually mine both unstructured as well structured data sets. This helps in leveraging machine learning as well as framing predictive modeling techniques. The latter helps extract meaningful insights. With such findings, a data manager will be able to make data-driven decisions and solve a plethora of main business problems. A number of significant technical skills help individuals succeed in the field of big data. Such skills include-       Data mining       Programming       Data visualization       Analytics 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 Abhinav Rai

18 Feb 2024

Blogs

18603

Summary: In this article, you will find the answers to 26 important Data Analyst Interview Questions like – What are the key requirements for becoming a Data Analyst? What are the important responsibilities of a data analyst? What does “Data Cleansing” mean? What are the best ways to practice this? What is the difference between data profiling and data mining? What is KNN imputation method? What should a data analyst do with missing or suspected data? Name the different data validation methods used by data analysts. Define Outlier? What is “Clustering?” Name the properties of clustering algorithms. And more… Read more to know each in detail. Attending a data analyst interview and wondering what are all the questions and discussions you will go through? Before attending a data analysis interview, it’s better to have an idea of the type of data analyst interview questions so that you can mentally prepare answers for them. When one is appearing for an interview, they are being compared with other candidates as well. To think that I can crack it without any prep is good but one should never underestimate the competition as well. It is wise to keep one prepared for an interview. Now this “preparation” sounds vague. The preparation should be strategic, it should begin with an understanding of the company, job role, and culture of the company. And should be escalated to gaining additional knowledge of the domain the interview is for. In this article, we will be looking at some most important data analyst interview questions and answers. Data Science and Data Analytics are both flourishing fields in the industry right now. Naturally, careers in these domains are skyrocketing. The best part about building a career in the data science domain is that it offers a diverse range of career options to choose from! Check out data science free courses Organizations around the world are leveraging Big Data to enhance their overall productivity and efficiency, which inevitably means that the demand for expert data professionals such as data analysts, data engineers, and data scientists is also exponentially increasing. However, to bag these jobs, only having the basic qualifications isn’t enough. Having data science certifications by your side will increase the weight of your profile.   The knowledge of data science would come to the rescue during the data analyst interview. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. You need to clear the trickiest part – the interview. Worry not, we’ve created this Data analyst interview questions and answers guide to understand the depth and real-intend behind the questions.  Also, check Full Stack Development Bootcamp Job Guaranteed from upGrad Top Data Analyst Interview Questions & Answers 1. What are the key requirements for becoming a Data Analyst? These are standard data science interview questions frequently asked by interviewers to check your perception of the skills required. This data analyst interview question tests your knowledge about the required skill set to become a data scientist. To become a data analyst, you need to: Be well-versed with programming languages (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, Db2, etc.), and also have extensive knowledge on reporting packages (Business Objects). Be able to analyze, organize, collect and disseminate Big Data efficiently. You must have substantial technical knowledge in fields like database design, data mining, and segmentation techniques. Have a sound knowledge of statistical packages for analyzing massive datasets such as SAS, Excel, and SPSS, to name a few. Proficient in using data visualization tools for comprehensible representation. A data analyst should be having knowledge of the data visualisation tools as well. Data cleaning Strong Microsoft Excel skills Linear Algebra and Calculation Along with that, in order to these data analyst interview questions, make sure to represent the use case of all that you have mentioned. Bring a layer to your answers by sharing how these skills will be utilised and why they are useful. Our learners also read: Excel online course free! 2. What are the important responsibilities of a data analyst? This is the most commonly asked data analyst interview question. You must have a clear idea of what your job entails to deliver the impression of being well-versed in your job role and a competent contender for the position.  A data analyst is required to perform the following tasks: Collect and interpret data from multiple sources and analyze results. Filter and “clean” data gathered from multiple sources. Offer support to every aspect of data analysis. Analyze complex datasets and identify the hidden patterns in them. Keep databases secured. Implementing data visualization skills to deliver comprehensive results. Data preparation Quality Assurance Report generations and preparation Troubleshooting Data extraction Trends interpretation How Can You Transition to Data Analytics? Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. 3. What does “Data Cleansing” mean? What are the best ways to practice this? If you are sitting for a data analyst job, this is one of the most frequently asked data analyst interview questions. Data cleansing primarily refers to the process of detecting and removing errors and inconsistencies from the data to improve data quality. Although containing valuable information, an unstructured database is hard to move through and find valuable information. Data cleansing simplifies this process by modifying unorganized data to keep it intact, precise, and useful. The best ways to clean data are: Segregating data, according to their respective attributes. Breaking large chunks of data into small datasets and then cleaning them. Analyzing the statistics of each data column. Creating a set of utility functions or scripts for dealing with common cleaning tasks. Keeping track of all the data cleansing operations to facilitate easy addition or removal from the datasets, if required. To answer these types of data analytics interview questions, go into a little explanation to demonstrate your domain knowledge. One can strategize to answer this to show what the journey of data looks like from beginning to end. For example,  Removal of unwanted observations which are not in reference to the filed of study one is carrying. Quality Check Data standardisation Data normalisation Deduplication Data Analysis Exporting of data 4. Name the best tools used for data analysis. A question on the most used tool is something you’ll mostly find in any data analytics interview questions. Such data science interview questions and data analyst behavioral interview questions are intended to test your knowledge and practical comprehension of the subject. Candidates with ample practical knowledge are the only ones to excel in this question. So make sure to practice tools and analytics questions for your analyst interview and data analyst behavioral interview questions The most useful tools for data analysis are: Tableau Google Fusion Tables Google Search Operators KNIME RapidMiner Solver OpenRefine NodeXL io Apache Spark R Programming SAS Python Microsoft Power BI TIBCO Spotfire Qlik Google Data Studio Jupyter Notebook Looker Domo Checkout: Data Analyst Salary in India 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. What is the difference between data profiling and data mining? Data Profiling focuses on analyzing individual attributes of data, thereby providing valuable information on data attributes such as data type, frequency, and length, along with their discrete values and value ranges. It assesses source data to understand structure and quality through data collection and performing quality checks on it. As the name profiling suggests, the data profiling evaluates the data from the specified source, and once that has been done it helps in analysing the data. On the other hand, data mining prepares the statistics and insights of the data. It digs deeper into the data. To answer these data analyst interview questions, one can share how data mining finds the patterns in the data by understanding the correlation between the datasets. Whereas, data profiling analyses the data to understand the actual content and data present in the data set. On the contrary, data mining aims to identify unusual records, analyze data clusters, and sequence discovery, to name a few. Data mining runs through the prebuilt database to find existing patterns and correlations to obtain value out of it by an optimum implementation. Data mining follows computer-led methodologies and complex algorithms to deliver results.  upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 6. What is KNN imputation method? KNN imputation method seeks to impute the values of the missing attributes using those attribute values that are nearest to the missing attribute values. The similarity between two attribute values is determined using the distance function. In brief, the KNN computation method is used to predict the missing values in the dataset. It can be fine to say that it is used as a replacement for the traditional imputation techniques. The key steps in KNN imputation are: Identify the dataset rows with missing values for the attribute to be credited. For each row with a missing value, calculate the distance between that row and other rows using a metric like Euclidean distance. The distance is computed based on the other attribute values in those rows. Select the k nearest rows to the row with the missing value based on the calculated distances. The value of k is usually small, like 5 or 10. Aggregate the attribute values to be imputed from the k nearest neighbors. This can be done by taking the mean or mode for numeric and categorical attributes. Impute the aggregated value for the missing attribute in the row. Repeat steps 2-5 for all rows with missing values. The major advantage of KNN imputation is that it uses the correlation structure between the attributes to impute values rather than relying on global measures like mean/mode. The value of k also provides flexibility in how local or global the imputation is. A smaller k gives more localized imputations. KNN imputation provides a simple and effective way to fill in missing values while preserving the data distribution and relationships between attributes. It is especially useful when the missing values are spread across many rows. The key steps in KNN imputation are: If you want to get data insights interview questions, identify the dataset rows with missing values for the attribute to be credited. For each row with a missing value, calculate the distance between that row and other rows using a metric like Euclidean distance. The distance is computed based on the other attribute values in those rows. Select the k nearest rows to the row with the missing value based on the calculated distances. The value of k is usually small, like 5 or 10. Aggregate the attribute values to be imputed from the k nearest neighbors. This can be done by taking the mean or mode for numeric and categorical attributes. Impute the aggregated value for the missing attribute in the row. Repeat steps 2-5 for all rows with missing values. The major advantage of KNN imputation is that it uses the correlation structure between the attributes to impute values rather than relying on global measures like mean/mode. The value of k also provides flexibility in how local or global the imputation is. A smaller k gives more localized imputations. These are some of the factors or the interview questions for data analysts KNN imputation provides a simple and effective way to fill in missing values while preserving the data distribution and relationships between attributes. It is especially useful when the missing values are spread across many rows. So let us get an idea of some basic data analysts interview questions or data analytics interview question: 7. What should a data analyst do with missing or suspected data? It is a very common data analyst interview question or data analytic interview question. When answering these questions often related to interview questions for data analysts, which should be answered like the one mentioned below. When a data analyst encounters missing or suspected incorrect data in a dataset, it presents a challenge that must be carefully addressed. The first step is to thoroughly analyze the dataset using deletion methods for data cleaning interview questions, single imputation, and model-based methods to identify missing or potentially invalid data. These methods help quantify the extent of the issue. The analyst should then prepare a detailed validation report documenting all the missing and suspicious values findings. This includes noting which attributes and rows are affected, the proportion of data that is missing or suspicious, and any patterns in where the data issues occur.  These questions’ answer is a must for a fresher as these are data analyst interview questions for freshers. In the next, basic data analyst interview questions the analyst must scrutinize the suspicious data points more deeply to determine their validity. Statistical tests can detect outliers and determine which points are likely errors versus those that are valid but unusual. Subject matter expertise can also be leveraged to assess whether values make sense or are reasonable. This is another question frequently asked  data analyst fresher interview questions. For any data identified as definitively invalid, the analyst should replace those values with an appropriate validation code rather than deleting them entirely. This preserves information about where the original data was incorrect or missing. Finally, the analyst needs to determine the best methods for the remaining missing data. Simple imputation methods like a mean, median, or mode can be applied. More complex methods like multiple imputations or machine learning to model the missing values require more work but generate higher quality complete data sets. The technique chosen depends on the analyst’s objectives and how much missing data exists. These are basics for interview questions for data analytics. 8. Name the different data validation methods used by data analysts. There are many ways to validate datasets. Some of the most commonly used data validation methods by Data Analysts include:  Field Level Validation – In this method, data validation is done in each field as and when a user enters the data. It helps to correct the errors as you go. Form Level Validation – In this method, the data is validated after the user completes the form and submits it. It checks the entire data entry form at once, validates all the fields in it, and highlights the errors (if any) so that the user can correct it.  Data Saving Validation – This data validation technique is used during the process of saving an actual file or database record. Usually, it is done when multiple data entry forms must be validated.  Search Criteria Validation – This validation technique is used to offer the user accurate and related matches for their searched keywords or phrases. The main purpose of this validation method is to ensure that the user’s search queries can return the most relevant results. Must read: Data structures and algorithms free course! 9. Define Outlier A data analyst interview question and answers guide will not be complete without this question. An outlier is a term commonly used by data analysts when referring to a value that appears to be far removed and divergent from a set pattern in a sample. The outlier values vary greatly from the data sets. These could be either smaller, or larger but they would be away from the main data values. There could be many reasons behind these outlier values such as measurement, errors, etc. There are two kinds of outliers – Univariate and Multivariate. The two methods used for detecting outliers are: Box plot method – According to this method, if the value is higher or lesser than 1.5*IQR (interquartile range), such that it lies above the upper quartile (Q3) or below the lower quartile (Q1), the value is an outlier. Standard deviation method – This method states that if a value is higher or lower than mean ± (3*standard deviation), it is an outlier. Exploratory Data Analysis and its Importance to Your Business 10. What is “Clustering?” Name the properties of clustering algorithms. Clustering is a method in which data is classified into clusters and groups. A clustering algorithm groups unlabelled items into classes and groups of similar items. These cluster groups have the following properties: Hierarchical or flat Hard and soft Iterative Disjunctive Clustering can be defined as categorising similar types of objects in one group. The clustering is done to identify similar types of data sets in one group. These data sets share one or more than one quality with each other. Our learners also read: Learn Python Online Course Free  11. What is K-mean Algorithm? K-mean is a partitioning technique in which objects are categorized into K groups. In this algorithm, the clusters are spherical with the data points are aligned around that cluster, and the variance of the clusters is similar to one another. It computes the centroids assuming that it already knows the clusters. It confirms the business assumptions by finding which types of groups exist. It is useful for many reasons, first of all, because it can work with large data sets and is easily accommodative to the new examples. The key steps in the K-means algorithm are: Select the number of clusters K to generate. Randomly set each data point to one of the K clusters. Compute the cluster centroids for the newly formed clusters by taking the mean of all data points assigned to that cluster. Compute the distance between each data point and each cluster centroid. Re-assign each point to the closest cluster. Re-compute the cluster centroids with the new cluster assignments. Repeat steps 4 and 5 until the cluster assignments block the change or the maximum number of iterations is reached. The distance metric operated to compute the distance between data points and cluster centroids is typically Euclidean distance. K-means seeks to minimize the sum of squared lengths between each data point and its assigned cluster centroid. K-means is popular because it is simple, scalable, and converges quickly. It works well for globular clusters. The main drawback is that the number of clusters K needs to be specified, which requires domain knowledge. K-means is also sensitive to outlier data points and does not work well for non-globular clusters. It provides a fast, easy clustering algorithm for exploratory data analysis. 12. Define “Collaborative Filtering”. Collaborative filtering is an algorithm that creates a recommendation system based on the behavioral data of a user. For instance, online shopping sites usually compile a list of items under “recommended for you” based on your browsing history and previous purchases. The crucial components of this algorithm include users, objects, and their interests. It is used to broaden the options the users could have. Online entertainment applications are another example of collaborative filtering. For example, Netflix shows recommendations basis the user’s behavior. It follows various techniques, such as- i) Memory-based approach ii) Model-based approach 13. Name the statistical methods that are highly beneficial for data analysts? Accurate predictions and valuable results can only be achieved through the right statistical methods for analysis. Research well to find the leading ones used by the majority of analysts for varied tasks to deliver a reliable answer in the analyst interview questions.  Bayesian method Markov process Simplex algorithm Imputation Spatial and cluster processes Rank statistics, percentile, outliers detection Mathematical optimization In addition to this, there are various types of data analysis as well, which the data analysts use- i) Descriptive ii) Inferential iii) Differences iv) Associative v) Predictive  14. What is an N-gram? An n-gram is a connected sequence of n items in a given text or speech. Precisely, an N-gram is a probabilistic language model used to predict the next item in a particular sequence, as in (n-1). An n-gram is a connected sequence of n items in a given text or speech. Precisely, an N-gram is a probabilistic language model used to predict the next item in a particular sequence, as in (n-1). The N-gram stands for the sequence of N words. It is a probabilistic model having its usage in machine learning, specifically Natural Language Processing (NLP). Speech recognition and predictive texting are the applications of N-gram as it produces the contiguous sequence of n items from the given speech or text. There could be a unigram, bigram, trigram, etc. for example, Trigram Learn Bigram Learn at Trigram Learn at upGrad 15. What is a hash table collision? How can it be prevented? This is one of the important data analyst interview questions. When two separate keys hash to a common value, a hash table collision occurs. This means that two different data cannot be stored in the same slot. Hash collisions can be avoided by: Separate chaining – In this method, a data structure is used to store multiple items hashing to a common slot. Open addressing – This method seeks out empty slots and stores the item in the first empty slot available. A better way to prevent the hash collision would be to use good and appropriate hash functions. The reason is that a good hash function would uniformly distribute the elements. Once the values would be distributed evenly over the hash table there would be lesser chances of having collisions.  Basic Fundamentals of Statistics for Data Science 16. Define “Time Series Analysis”. Series analysis can usually be performed in two domains – time domain and frequency domain. Time series analysis is the method where the output forecast of a process is done by analyzing the data collected in the past using techniques like exponential smoothening, log-linear regression method, etc. Time Series Analysis analyses the sequence of data points that are collected over different times. This brings the structure to how the analysts record the data, instead of going ahead observing the data points randomly, they observe data over set intervals of time. There are various types of time series analysis- Moving average Exponential smoothing ARIMA It is used for nonstationary data, data that is dynamic an dconstantly moving. It has applications in various industries such as finance, retail, economics, etc. 17. How should you tackle multi-source problems? Multi-source problems are a group of computational data composed of dynamic, unstructured, and overlapping data that is hard to go through or obtain patterns from. To tackle multi-source problems, you need to: Identify similar data records and combine them into one record that will contain all the useful attributes, minus the redundancy. Facilitate schema integration through schema restructuring. More specifically in analyst interview questions, some key techniques for handling multi-source data integration challenges are: Entity resolution: Identify which records refer to the same real-world entity across different sources. Deduplication, record linkage, and entity-matching methods can help merge duplicate records.  Schema mapping: Map attributes and fields from different sources to each other. This helps relate to how differently structured data connects. Both manual schema mapping and automated schema matching are options. Conflict resolution: When merging records, conflicting attribute values may arise. Business rules and statistical methods must be applied to determine which value to keep. Data fusion: Integrate the data at a lower level by fusing multiple records for the same entity through pattern recognition and machine learning algorithms. This creates a single consolidated record. Creating master data: Build master data sets linked to and pull attributes from multiple sources in real-time when needed for analysis. The master record acts as a single point of reference. Maintaining metadata: Metadata management is essential to track the meaning, relationships, origin, and characteristics of the multi-source data. This aids in both integration and analysis. Employing these techniques requires understanding the semantics, quality, overlap, and technical details of all the combined data sources. With thoughtful multi-source data integration, unified views can be formed to enable more holistic analysis. This will help in the data analyst interview preparation. 18. Mention the steps of a Data Analysis project. The core steps of a Data Analysis project include: The foremost requirement of a Data Analysis project is an in-depth understanding of the business requirements.  The second step is to identify the most relevant data sources that best fit the business requirements and obtain the data from reliable and verified sources.  The third step involves exploring the datasets, cleaning the data, and organizing the same to gain a better understanding of the data at hand.  In the fourth step, Data Analysts must validate the data. The fifth step involves implementing and tracking the datasets. The final step is to create a list of the most probable outcomes and iterate until the desired results are accomplished. The whole meaning of the data analysis is to help in effective decision-making. The data analysis projects are the steps towards achieving it. For example, while undergoing the above-said process, the analysts use the past data and once the data has been analysed it gets put in a presentable form so the decision-making process can be smoother. 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 19. What are the problems that a Data Analyst can encounter while performing data analysis? A critical data analyst interview question you need to be aware of. A Data Analyst can confront the following issues while performing data analysis: Presence of duplicate entries and spelling mistakes. These errors can hamper data quality. Poor quality data acquired from unreliable sources. In such a case, a Data Analyst will have to spend a significant amount of time in cleansing the data.  Data extracted from multiple sources may vary in representation. Once the collected data is combined after being cleansed and organized, the variations in data representation may cause a delay in the analysis process. Incomplete data is another major challenge in the data analysis process. It would inevitably lead to erroneous or faulty results.  20. What are the characteristics of a good data model? For a data model to be considered as good and developed, it must depict the following characteristics: It should have predictable performance so that the outcomes can be estimated accurately, or at least, with near accuracy. It should be adaptive and responsive to changes so that it can accommodate the growing business needs from time to time.  It should be capable of scaling in proportion to the changes in data.  It should be consumable to allow clients/customers to reap tangible and profitable results. It should be presented in a visualised format. So that the results could be understood and predicted easily. Good data is transparent and comprehendible. It should be derived from the correct data points and sources.  It should be simple to understand, simplicity does not necessarily mean weak rather it should be simple and should make sense. 21. Differentiate between variance and covariance. Variance and covariance are both statistical terms. Variance depicts how distant two numbers (quantities) are in relation to the mean value. So, you will only know the magnitude of the relationship between the two quantities (how much the data is spread around the mean). It measures how far each number is from the mean. In simple terms, it could be said to be a measure of variability. On the contrary, covariance depicts how two random variables will change together. Thus, covariance gives both the direction and magnitude of how two quantities vary with respect to each other. And also how two variables are related to each other. Positive covariance would tell that two variables are positively related. The key characteristics of a normal distribution are: The shape of the distribution follows a bell curve, with the highest frequency of values around the mean and symmetric tapering on both sides. The mean, median, and mode are all equal in a normal distribution. About 68% of values fall within 1 standard deviation from the mean. 95% are within 2 standard deviations. 99.7% are within 3 standard deviations. The probabilities of values can be calculated using the standard normal distribution formula. The total area under the normal curve is 1, representing 100% probability. It is unimodal and asymptotically approaches the x-axis on both sides. Normal distributions arise naturally in real-world situations like measurement errors, sampling, and random variations. When you gather more and more samples from a group, like measuring heights in different crowds, the central limit theorem says the average of those samples will follow a bell-shaped curve, similar to a normal distribution. It doesn’t matter what the original heights looked like in each crowd—it tends to even out with larger sample sizes. The symmetric bell shape provides a good model for understanding the inherent variability in many natural phenomena. 22. Explain “Normal Distribution.” One of the popular data analyst interview questions. Normal distribution, better known as the Bell Curve or Gaussian curve, refers to a probability function that describes and measures how the values of a variable are distributed, that is, how they differ in their means and their standard deviations. In the curve, the distribution is symmetric. While most of the observations cluster around the central peak, probabilities for the values steer further away from the mean, tapering off equally in both directions. The key characteristics of a normal distribution are: The shape of the distribution follows a bell curve, with the highest frequency of values around the mean and symmetric tapering on both sides. The mean, median, and mode are all equal in a normal distribution. About 68% of values fall within 1 standard deviation from the mean. 95% are within 2 standard deviations. 99.7% are within 3 standard deviations. The probabilities of values can be calculated using the standard normal distribution formula. The total area under the normal curve is 1, representing 100% probability. It is unimodal and asymptotically approaches the x-axis on both sides. Normal distributions arise naturally in real-world situations like measurement errors, sampling, and random variations. When you gather more and more samples from a group, like measuring heights in different crowds, the central limit theorem says the average of those samples will follow a bell-shaped curve, similar to a normal distribution. It doesn’t matter what the original heights looked like in each crowd—it tends to even out with larger sample sizes. The symmetric bell shape provides a good model for understanding the inherent variability in many natural phenomena. 23. Explain univariate, bivariate, and multivariate analysis. Univariate analysis refers to a descriptive statistical technique that is applied to datasets containing a single variable. The univariate analysis considers the range of values and also the central tendency of the values.  It requires each data to be analysed separately. It can be either inferential or descriptive. It could possibly give inaccurate results. An example of univariate data could be height. In a classroom of students, there is only one variable which is height. Bivariate analysis simultaneously analyzes two variables to explore the possibilities of an empirical relationship between them. It tries to determine if there is an association between the two variables and the strength of the association, or if there are any differences between the variables, and what is the importance of these differences. An example of bivariate data would be the income of the employees and the years of experience they hold.  Multivariate analysis is an extension of bivariate analysis. Based on the principles of multivariate statistics, the multivariate analysis observes and analyzes multiple variables (two or more independent variables) simultaneously to predict the value of a dependent variable for the individual subjects. An example of multivariate data would be students getting awards in sports function, their class, age, and gender. 24. Explain the difference between R-Squared and Adjusted R-Squared. The R-Squared technique is a statistical measure of the proportion of variation in the dependent variables, as explained by the independent variables. The Adjusted R-Squared is essentially a modified version of R-squared, adjusted for the number of predictors in a model. It provides the percentage of variation explained by the specific independent variables that have a direct impact on the dependent variables. In simple terms, R Squared measures the regression fitment, whereas the higher R squared measures a good fitment and the lower R Squared measures the low fitment. Whereas, the Adjusted R Squared takes into account those variables which actually had an effect on the performance model. R-squared measures how well the regression model fits the actual data. It denotes the proportion of variation in the dependent variable that the independent variables can illustrate. Its value goes from 0 to 1, with 1 being an ideal fit. As more variables are added to a model, the R-squared will never decrease, only increase or stay the same. This can give an optimistic view of the model’s fit. Adjusted R-squared attempts to correct this by penalizing for the addition of extraneous variables. It includes a degree of freedom adjustment based on the number of independent variables. Adjusted R-squared will only increase if added variables improve the model more than would be expected by chance. It can decrease if unnecessary variables are added. Adjusted R-squared gives a more realistic assessment of how well the model generalizes and predicts new data points. As a rule of thumb, the adjusted R-squared value should be close to the R-squared value for a well-fitting model. A large gap indicates overfitting. Adjusted R-squared provides a modified assessment of model fit by accounting for model complexity. It is a useful metric when comparing regression models and avoiding overfitting. 25. What are the advantages of version control? The main advantages of version control are – It allows you to compare files, identify differences, and consolidate the changes seamlessly.  It helps to keep track of application builds by identifying which version is under which category – development, testing, QA, and production. It maintains a complete history of project files that comes in handy if ever there’s a central server breakdown. It is excellent for storing and maintaining multiple versions and variants of code files securely. It allows you to see the changes made in the content of different files. Version control can also be called source control. It tracks the changes that happen in software. Using certain algorithms and functions manages those changes so the team who is responsible for the task can effectively work on the software without losing efficiency. The version control happens with the use of certain version control tools. It is responsible to manage changes happening in a computer program and saving them. For example, in Google Word Doc, whatever has been added in the doc, can be accessed by the user the next time they visit without having the need to save each change. Also, the changes or edits appeared on a real-time basis to all the users having the access to the doc. 26. How can a Data Analyst highlight cells containing negative values in an Excel sheet? Final question in our data analyst interview questions and answers guide. A Data Analyst can use conditional formatting to highlight the cells having negative values in an Excel sheet. Here are the steps for conditional formatting: Selelect the target range of cells you want to apply formatting. This would be the entire dataset or columns containing numbers with potential negative values. On the Home tab ribbon, click the Conditional Formatting dropdown and choose New Rule. In the New Formatting Rule dialog, go to the Format-onlyFormat cells that contain the section. In the dropdown, choose Less Than as the conditional Format. In the adjacent value field, enter 0 or the number that separates positives from negatives. Select the formatting style to apply from the choices like color scale shading, data bar icons, etc. Adjust any parameters to customize the appearance as needed. Click OK to create the rule and apply it to the selected cells. The cells meeting the less-than condition will be formatted with the chosen style. Additional rules can be created to highlight other thresholds or values as needed. 27. What is the importance of EDA (Exploratory data analysis)? Exploratory Data Analysis (EDA) is a crucial preliminary step in the data analysis process that involves summarizing, visualizing, and understanding the main characteristics of a dataset. Its significance lies in its ability to: Identify Patterns A key importance of EDA is leveraging visualizations, statistics, and other techniques to identify interesting patterns and relationships in the data. Plots can surface trends over time, correlations between variables, clusters in segments, and more. These patterns help generate insights and questions to explore further. EDA takes an open-ended approach to let the data guide the discovery of patterns without imposing preconceived hypotheses initially. Detect Anomalies Outlier detection is another important aspect of EDA. Spotting anomalies, inconsistencies, gaps, and suspicious values in the data helps identify problems that need addressing. Uncovering outliers can also flag interesting cases worthy of follow-up analysis. Careful data exploration enables analysts to detect anomalous data points that may skew or bias results if unnoticed. Data Quality Assessment EDA allows for assessing the overall quality of data by enabling the inspection of attributes at both a granular and aggregated level. Data properties like completeness, uniqueness, consistency, validity, and accuracy can be evaluated to determine data quality issues. Graphics like histograms can reveal limitations or errors in the data. This assessment is crucial for determining data reliability. Feature Selection Exploring the relationships between independent and target variables helps determine which features are most relevant to the problem. EDA guides dropping insignificant variables and selecting the strongest predictors for modeling. Reducing features improves model interpretability, training time, and generalization. Hypothesis Generation Exploratory data analysis enables productive hypothesis generation. By initially exploring datasets through visualizations and statistics without firm hypotheses, analysts can identify interesting patterns, relationships, and effects that warrant more rigorous testing.  Data Transformation Frequently, insights from EDA will guide transforming data to make it more suitable for analysis. This can involve scaling, normalization, log transforms, combining attributes, and more. EDA exposes the need for these transformations before feeding data to models. Tips to prepare for the Interview of Data Analyst Preparing for a data analyst interview requires technical knowledge, problem-solving skills, effective communication, and, most importantly, belief in yourself. Here are some tips to help you succeed in the interview: – 1. Understand the Role Familiarize yourself with the specific responsibilities and skills required for the data analyst position you’re interviewing for. This will help you to tailor your preparation accordingly so that the result will be positive.  2. Review Basics The next step is to brush up on fundamental statistics, data manipulation, and visualization concepts. On top of that, be prepared to discuss concepts like mean, median, standard deviation, correlation, and basic data visualization techniques. 3. Master Data Tools Additionally, you must be proficient in data analyst tools like Excel and SQL and data visualization tools like Tableau and Power BI or Python libraries like Pandas and Matplotlib. 4. Practice Problem-Solving Solve sample data analysis problems and case studies for best results. This demonstrates your ability to work with real-world data scenarios and showcase your analytical skills. 5. Technical Questions Be ready to answer data analyst interview questions related to data cleaning, transformation, querying databases, and interpreting results from statistical analyses. 6. Portfolio Review Prepare examples of past projects that highlight your analytical abilities. Explain the problem, your approach, the techniques you used, and the results achieved. 7. Domain Knowledge Understand the industry or domain the company operates in. If applicable, familiarize yourself with relevant terminology and challenges. 8. Communication Skills Work on how you explain complex concepts and stuff to others. Make sure the recipient understands what you are saying clearly and concisely. Communication is crucial for effectively presenting your findings and is the key to success. 9. Behavioral Questions Be ready to answer behavioral questions that assess your teamwork, problem-solving, and communication skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses. 10. Ask Questions Prepare thoughtful data analyst interview questions to ask the interviewer about the company’s data environment, projects, team structure, and expectations for the role. 11. Data Ethics Be prepared to discuss ethical considerations related to data analysis, including privacy, bias, and data security. 12. Mock Interviews Practice mock interviews with peers, mentors or through online platforms to simulate the interview experience and receive feedback. This will help you to answer the data analyst interview questions with confidence.  13. Stay Updated Ensure to be aware of the latest trends and developments in data analysis, such as machine learning, AI, and big data. 14. Confidence and Positivity Approach the interview with confidence, a positive attitude, and a willingness to learn. 15. Time Management During technical assessments or case studies, manage your time well and prioritize the most important aspects of the problem. Career as a Data Analyst Topping as one of the most widely sought-after jobs in the current market and bagging a place in The Future of Jobs report 2020, the data analyst role is significant to brands dealing with and aiming to grow in the digital environment. Thanks to rapid digitization, an enormous amount of data demands a skilled set of hands, a data analyst being one of them.  With every brand leveraging digital interactions to fuel its growth, continuous data flow and profitable usage are necessary. Data analysts work in the same role to deal with heaps of unstructured data and extract value from it. Considering the ongoing digitization, demand for skilled data analysts in the IT market is nowhere going down in the near future. The above analytics questions allow data analyst aspirants a glance at what they can expect and what they must prepare for the analyst interview questions.  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? Conclusion With that, we come to the end of our list of data analyst interview questions and answers guide. Although these data analyst interview questions are selected from a vast pool of probable questions, these are the ones you are most likely to face if you’re an aspiring data analyst. In addition, data analysts must demonstrate curiosity to learn new data technologies and trends continuously. Business acumen allows them to apply data skills to create organizational value. Other critical areas highlighted in interviews include data governance, ethics, privacy, and security.  Overall, top data analyst candidates have technical expertise, communication ability, business sense, and integrity in managing data properly. The key is showcasing your hard and soft skills and how you’ve used data analytics to drive impact. These questions set the base for any data analyst interview, and knowing the answers to them is sure to take you a long way! If you are curious about learning in-depth data analytics, data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Executive PG Program in Data Science.

by Abhinav Rai

24 Jan 2024

Blogs

5711

What comes to your mind when you hear the term “Deep Learning?” You probably think of smart robots and machines that will take over our world in the near future, right? Well, that’s not at all what deep learning is. In a layman’s term, deep learning is an AI approach that aims to imitate the workings of the human brain to process large amounts of data and extract meaningful patterns from it to foster data-driven decision making. Today, data rules all – it is the new King of the digital world that we live in. Artificial Intelligence, Machine Learning, and Deep Learning are all focused on one thing – leveraging Big Data to power innovation. The interest in AI technology is soaring by the minute, and deep learning is the cutting-edge approach that is disrupting every industry. According to a recent research report by Tractica, the AI market is estimated to grow from 3.2 billion in 2016 to $89.8 billion by 2025. These figures only reinforce the fact that AI, ML, and Deep Learning will play an even bigger role in the development and transformation of the business and IT sector. Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What is Deep Learning? Deep learning is deeply intertwined with Artificial Intelligence and Machine Learning. How, you ask? As you can see, deep learning is a subset of ML which in turn is a subset of AI. Thus, while Artificial Intelligence is the broader umbrella that focuses on teaching machines how to think independently and intelligently, ML is an AI approach that aims to create such algorithms that can extract valuable information from large datasets. Deep Learning, on the other hand, is a branch of ML that uses a specific algorithm – Neural Nets – to achieve the end purpose of ML. What is Machine Learning and Why it matters Deep learning is an exclusive technique for developing and training neural networks. The structure of a neural network draws inspiration from the structure of the human brain, more precisely, the cerebral cortex. Thus, similar to a cerebral cortex, an artificial neural network also has many layers of interconnected perceptrons. Unlike traditional data approaches that analyze data in the linear method, deep learning relies on the non-linear approach of training machines to process data. The data that is fed into the deep learning system passes through the interconnected network of hidden layers. These hidden layers of the neural net process, analyze, modify, and manipulate the data to determine its relationship with the target variable. Each node of the net bears a specific weight, and every time the data passes through a node, it multiplies the input value by its weight. This process continues until it reaches the output layer, with the final output transforming into a valuable information. Deep learning, thus, eliminates the process of manual identification of patterns hidden in data. How does Deep Learning work? Now that you have a deep learning introduction, let us understand its working. At its core, deep learning operates by using large amounts of labeled data and feeding it into neural networks. The neural networks then iteratively learn from this data by adjusting their internal parameters through a process known as backpropagation. This iterative learning process allows the networks to progressively improve their performance and accuracy over time. Deep learning architectures are typically composed of an input layer, one or more hidden layers, and an output layer. Each layer consists of numerous artificial neurons that receive input, process it using weighted connections, and produce an output. The layers are densely connected, meaning that each neuron in one layer is connected to every neuron in the subsequent layer. This interconnectedness allows the network to capture complex relationships and dependencies within the data. Career Opportunities in Deep Learning Anyone in the IT world must have heard about deep learning at some point of his/her career. With AI progressing by leaps and bounds, the field of deep learning is also skyrocketing. Since deep learning is a rapidly growing field of research, it is creating massive job opportunities for individuals who specialize in AI and ML technologies. Today, the demand for skilled and trained professionals in deep learning, particularly for deep learning engineers and deep learning researchers, has increased by manifold across the various parallels of the industry. According to a 2017 report by Grand View Research, Inc., the deep learning market in the US is projected to reach $10.2 billion by 2025. Deep learning market revenues in the US (2014-25) According to the latest stats on Indeed, the average salary for deep learning professionals in the US ranges anywhere between $71,935/year for a Deep Learning Research Scientist to $140,856/year for Deep Learning Computer Vision Engineer.   Skills Required for a Successful Deep Learning Career Since deep learning is a subset of ML, the skills required for deep learning are pretty much the same as required for ML. By now you’ve already guessed that programming knowledge is a must here. Most popular deep learning libraries are written in R and Python. Hence, if you are well-versed in any one of these two languages, it will suffice. Apart from possessing extensive knowledge of the fundamentals of Computer Science and programming, you must also have a solid foundation in Mathematics, Statistics & Probability, and Data Modeling. A significant part of a deep learning engineer’s job is to design algorithms and systems that can seamlessly communicate with as well as integrate other software components that already exist. Thus, software design skills are a must in this field. You also need to be comfortable in working with standard ML libraries and algorithms including MLib, TensorFlow, and CNTK. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Deep Learning in the Real World Deep learning has penetrated almost all the significant aspects of our lives. Whether we realize it or not, deep learning technologies are everywhere around us. Organizations and companies across the world are leveraging deep learning technology to power innovations like self-driving cars and chatbots to developing useful services like fraud prevention, predictive analytics, task automation, and much more. Deep Learning Applications Deep learning has found applications in various domains, revolutionizing industries and enabling breakthrough advancements.  Now that you know what is deep learning in AI, explore some prominent applications of deep learning: Computer Vision: Deep learning has significantly enhanced computer vision tasks, such as image classification, object detection, and facial recognition. By analyzing pixel-level data, deep learning models can accurately identify and classify objects within images and videos. Natural Language Processing (NLP): Deep learning has greatly improved NLP tasks, including speech recognition, language translation, and sentiment analysis. Deep learning models can understand and generate human language, enabling advancements in virtual assistants, chatbots, and language-based applications. Healthcare: Deep learning is making substantial contributions to the healthcare sector. It is being utilized for medical image analysis, disease diagnosis, drug discovery, and personalized treatment recommendations. Deep learning models can analyze medical images like X-rays, MRIs, and CT scans, assisting doctors in accurate diagnosis and treatment planning. Autonomous Vehicles: Deep learning plays a vital role in enabling autonomous vehicles to perceive and understand their surroundings. Deep learning algorithms can process sensor data, such as images and LiDAR readings, to identify pedestrians, vehicles, and obstacles, allowing autonomous vehicles to navigate safely. Importance of Deep Learning The importance of deep learning lies in its ability to extract meaningful insights from complex and unstructured data. Traditional machine learning algorithms often struggle to handle high-dimensional data with intricate patterns. Deep learning, with its hierarchical representations and sophisticated neural networks, can capture and utilize these patterns effectively. This capability has opened up new possibilities in various fields, ranging from healthcare and finance to retail and entertainment. Let us now look at some of the best use cases of deep learning in the real world! One of the most excellent examples of deep learning tech is the personalized recommendation lists on online platforms such as Netflix, Amazon, and Facebook. The online and social media giants have access to a treasure trove of user-generated data. Using deep learning techniques, they are able to extract useful information from the user-generated data which is then used to create a customized and personalized list of suggestions for individual users according to their tastes and preferences. Deep learning networks are capable of successfully analyzing behaviors in real-time. DeepGlint is a deep learning solution that can fetch real-time insights about the behavior of any object, be it humans or inanimate objects like cars. Image recognition is another application of deep learning. Image recognition aims to recognize and identify objects within images while also understanding the content and context of the image. AlchemyAPI has been developing image recognition technology for quite a while now. CamFind is a mobile app that utilizes AlchemyVision API – it can not only inform the users about the objects in an image but can also tell them where they can purchase those objects from. Deep learning applications have also found their way in the world of advertising. Ad networks and marketers leverage deep learning tech to build data-driven predictive advertising, targeted display advertising, and real-time bidding (RTB) advertising, to name a few. For instance, Baidu, a Chinese search engine uses deep learning to predict such advertising content and methods that the users can relate to. This helps increase the revenue of the company. Pattern recognition powered by deep learning is being used by many companies to detect and prevent fraud. PayPal has been successful in preventing fraudulent payment transactions and purchases. It has achieved this with the help of H2O (an open-source predictive analytics platform) that uses advanced ML algorithms to analyze data in real-time to check for any anomalies that hint at fraudulent activities and security threats. Artificial Intelligence Engineers: Myths vs. Realities These are only a few use cases of deep learning from a vast pool of other innovative real-world projects. Deep learning, like AI and ML, is still emerging and developing. In the future, deep learning together with AI and ML will pave the way for more such groundbreaking innovations that’ll completely transform our lives in ways we cannot yet imagine. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Deep Learning Limitations Despite its impressive capabilities, deep learning does have certain limitations. One of the major challenges is the requirement for large amounts of labeled data for training. Deep learning models thrive on data, and obtaining a significant volume of high-quality labeled data can be time-consuming and expensive. Additionally, deep learning models are often considered “black boxes,” as their decision-making processes are not easily interpretable, raising concerns regarding transparency and accountability. Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning Before diving deeper into deep learning, it’s important to understand the distinction between artificial intelligence (AI), machine learning, and deep learning. While AI encompasses the broader field of creating intelligent systems, machine learning is a subset of AI that focuses on algorithms that learn from data. Deep learning, on the other hand, is a specific approach within machine learning that utilizes neural networks with multiple layers.

by Abhinav Rai

02 Jun 2023

Blogs

6349

We’re officially a part of a digitally dominated world where our lives revolve around technology and its innovations. Each second the world produces an incomprehensible amount of data, a majority of which is unstructured. And ever since Big Data and Data Science have started gaining traction both in the IT and business domains, it has become crucial to making sense of this vast trove of raw, unstructured data to foster data-driven decisions and innovations. But how exactly are we able to give coherence to the unstructured data? The answer is simple – through Natural Language Processing (NLP). Natural Language Processing (NLP) In simple terms, NLP refers to the ability of computers to understand human speech or text as it is spoken or written. In a more comprehensive way, natural language processing can be defined as a branch of Artificial Intelligence that enables computers to grasp, understand, interpret, and also manipulate the ways in which computers interact with humans and human languages. It draws inspiration both from computational linguistics and computer science to bridge the gap that exists between human language and a computer’s understanding. Deep Learning: Dive into the World of Machine Learning! The concept of natural language processing isn’t new – nearly seventy years ago, computer programmers made use of ‘punch cards’ to communicate with the computers. Now, however, we have smart personal assistants like Siri and Alexa with whom we can easily communicate in human terms. For instance, if you ask Siri, “Hey, Siri, play me the song Careless Whisper”, Siri will be quick to respond to you with an “Okay” or “Sure” and play the song for you! How cool is that? Nope, it is not magic! It is solely possible because of NLP powered by AI, ML, and Deep Learning technologies. Let’s break it down for you – as you speak into your device, it becomes activated. Once activated, it executes a specific action to process your speech and understand it. Then, very cleverly, it responds to you with a well-articulated reply in a human-like voice. And the most impressive thing is that all of this is done in less than five seconds! Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Career Opportunities in Natural Language Processing As we mentioned above, natural language processing allows computers to interact with humans in their own language. Through NLP, computers can hear speech and read a text, and simultaneously interpret and measure the sentiment behind it to respond accordingly. Since Big Data is being leveraged by most of the companies around the globe, organizations and institutions across the various sectors of the industry are resorting to NLP techniques and tools to extract meaningful information from massive datasets. Natural Language Toolkit (NLTK), Stanford NLP, MALLET, and Apache OpenNLP are some of the popular open-source NLP libraries used in real-world cases and applications. The rising interest in the field of natural language processing is creating new career opportunities for professionals specializing in Data Science, Machine Learning, and Computational Linguistics. Reputed organizations like Facebook, Google, Sony Ericsson, British Airways, J.P. Morgan, Forte Group, Ernst & Young, American Express, Merrill Lynch, Shell, Celtic, and Sainsbury, to name a few, hire natural language processing experts and analysts. The job roles in NLP are quite varied and branched out such as NLP engineer, NLP scientist, NLP architect, Voice Over Artist, NLP applied research scientist, cognitive data scientist, and so on. Apart from these roles, one of the most prominent job roles in the field of natural language processing is that of a Coach. Numerous companies hire NLP experts for the purpose of executive performance coaching in their respective institutions. The salaries of NLP professionals are pretty decent. For instance, the average salary of a Machine Learning NLP engineer in the US ranges anywhere between $119,256 – $169,853 per year. An NLP Research Scientist, on the other hand, makes around $72,040 per year. 6 Interesting Machine Learning Project Ideas For Beginners Why is NLP important? The NLP helps in processing large-scale data. It enables computers to communicate with humans in the language which both know. For example, NLP allows computers to read, hear speech, interact and interpret important information. Another important factor of NLP is the structuring of highly complex and unstructured data. Not only there are hundreds of languages and dialects, but within each language there consists a unique set of rules of grammar and syntax, slang, and terms. Natural language processing in AI is used especially for human language with the help of supervised and unsupervised learning. The NLP helps to resolve ambiguities in language and adds numeric ability and structure to the data. Natural Language Processing Tokenization Tokenisation is nothing but a simple process which utilises the raw data and turns it into a useful data string. Although it is of great importance in the world of cybersecurity and NFT creations but it serves a big deal of importance in the NLP as well. It is used in NLP for splitting paragraphs and sentences into much smaller units.  Natural Language Processing in the Real World The real-life examples of Natural Language Processing are – Email filters, Smart Assistants, Search results,  Language Transaltion, Digital calls, Data analysis,  Text analytics Today, natural language processing is primarily used for text mining, machine translation, and automated question answering. In fact, NLP has found its applications in numerous real-world use cases including automatic text summarization, parts-of-speech tagging, topic extraction, sentiment analysis, named entity recognition, relationship extraction, stemming, and much more. Here’s how natural language processing is being leveraged by companies across the myriad parallels of the industry: The “Spell Check” feature of Microsoft Word is one of the most basic applications of NLP. then again, NLP techniques are in full swing in popular search engines namely Google and Bing. These search engines leverage NLP techniques to identify and extract keywords from text to parse search queries and populate search indexes on their site. Businesses are using the NLP technique, sentiment analysis, to understand and interpret how their clients are reacting to their products and services. By uncovering the emotional outlook and response of the customers, sentiment analysis allows companies to enhance their products and services according to the taste and preferences of their customers. The Royal Bank of Scotland has been one of the biggest proponents of Text Analysis. Using text analytics, the bank has been able to unravel important patterns and trends by diving into the customer feedback data from emails, surveys, as well as complaint calls. By analyzing and interpreting this data through text analytics, the bank is able to understand the grievances of its customers and improve upon them. In the financial sector, companies apply NLP techniques to extract meaningful and relevant information from plain texts and using the data thus obtained, they can carve out data-driven trading decisions and strategies. While these are basically text-based NLP techniques and applications, natural language processing has also extended to voice and speech recognition. Like we mentioned at the beginning of this post, NLP is used in smart personal assistants such as Apple’s Siri, Microsoft’s Cortana, and Amazon’s Alexa. These virtual assistants can perform all kinds of tasks – from simple tasks like changing the lighting of your room and providing weather updates to more complicated ones like shopping online for you. Skills Required to become an ML and NLP Expert Since natural language processing bridges the two worlds of linguistics and computers, it demands a certain degree of expertise in both the fields. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification Linguistics You need to be able to understand the basic aspects and concepts of linguistics like speech recognition, information extraction, sentence fragmentation, parts of speech, and so on. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Programming ML NLP engineers or NLP research scientists must possess good programming skills. You should be well-versed in at least one programming language, be it Python or Java or Ruby, or any other high-level language for that matter. Also, you should possess the fundamental ML (classification, regression, probability estimation, data integration, decision trees, etc.) and NLP (syntax, semantics, speech recognition, etc.) concepts. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Apart from these skills, you need to have a basic knowledge of Probability & Statistics and recursive neural networking (RNN). These are the essential components of many research fields and NLP is no exception. 6 Times Artificial Intelligence Startled The World As AI and ML technologies continue to progress, it is giving rise to new and exciting job prospects in the natural language processing sphere. In 2016, natural language processing featured as the hottest skill in the global jobs market on Upwork. This shows that the demand for skilled and trained professionals who can juggle both computer programming and natural language processing skills is bound to rise considerably in the near future.

by Abhinav Rai

31 Mar 2023

Blogs

9255

Today organizations and business firms around the world are harnessing the power of data to transform the business scenario. The quantity of data being generated and collected is increasing exponentially with each passing minute. However, merely accumulating data cannot benefit an organization. It is only by transforming this data into a valuable asset that companies can add value to their core foundation. The key to success lies in converting the massive amount of data into actionable information that can drive profitability, scale-up revenue, and boost the overall efficiency of an organization. Hence, business analytics courses and jobs are in high demand in India.  Let’s look at what is analytics in-depth! What is Business Analytics? The simplest way to answer “what is analytics” would be that it is “the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of these results to customers, business partners, and college executives.”  Business Analytics leverages Big Data through quantitative techniques to promote enhanced business modeling and decision making. Although Business Analytics (BA) and Business Intelligence (BI) are often used synonymously, they are quite different. While BI focuses on gathering data from multiple sources and processing it for analyzation, BA analyzes all the relevant information provided by BI to foster data-driven decisions. Types of Business Analytics Descriptive Analytics It tracks KPIs (Key Performance Indicators) to determine the existing state of a business.  2. Predictive Analytics It analyzes the data trends to explore the odds of future outcomes. 3. Prescriptive Analytics It uses past performance data to create recommendations that help to deal with identical situations in the future. How does Business Analytics help in the present age? The digital era has conveyed a significant surge in several services like customer service, self-service, and multi-faceted connectivity. Digitalization has paid attention to the customer through all business analytics procedures. There has been a gradual shift from the conventional practice of seller-focused business to the one which is more sympathetic and prioritizes experience. Consequently, it creates a platform for product customization. The currently available digital tools and social media tools bridge the gap between customers and business analytics. Thus, BI tools bring them together. Now that you’re aware of what is analytics, let’s look at some of the most used analytics tools. Converting Business Problems to Data Science Problems   Benefits of Business Analytics The substantial growth in the IT sector has made business analytics applications much relevant than earlier. All the valuable data, computer-based models and statistical analysis combine to make Analytics. Making appropriate decisions for the growth of the company in the future and being ready to overcome the challenges you might face is the sole motto of Analytics. It’s becoming a huge aspect of the tech industry.  According to Forbes, 53% percent of the tech industry has already adopted this culture due to foreseeable benefits of Business Analytics. No kidding, it will be the shift to a more significant part of the enterprise and most problems will be already solved using this before the issue even arises. Business Analytics Tools The next stop after answering “what is analytics” is looking at some tools for the same: 1. Sisense Sisense is one of the most popular analytics tools in the market. In 2016, it won the Best Business Intelligence Software Award from FinancesOnline. Sisense is an excellent tool for simplifying complex data analyses and making Big Data insights viable for both small and medium-sized companies. It includes robust and dynamic text analysis features. These features allow users to convert unstructured text into useful business intelligence.  Sisense business analytics platform boasts the proprietary Sisense Crowd Accelerated BI. It utilizes open-source language for computational statistics. Moreover, it enables users to carry out broad analysis and visualization of complex data. Consequently, it encourages data-driven decisions and enhanced forecasting of forthcoming trends.  Hence, Sisense is one of the futuristic BI tools. Key features: Sisense’s in-chip technology can process data ten times faster than conventional systems. It accumulates data from multiple sources with complete accuracy. Read our Other Articles Related to Business Analytics What is Business Analytics? Career, Salary & Job Roles Top 7 Career Options in Business Analytics Business Analytics Free Online Course with Certification Business Analytics Vs Data Analytics: Difference Between Business Analytics and Data Analytics Top 7 Best Business Analytics Tools Recommended for every Business Analyst Top 11 Industry Applications of Business Analytics Future Scope of Business Analytics Business Analytics Eligibility or Requirement 8 Business Analytics Tips: Which Helps to Run Business Successfully 2. Clear Analytics Clear Analytics is an Excel-based intelligence tool that is loaded with many useful features such as reports scheduling, version control, administrative and sharing capabilities, and governance. For anyone who’s well-versed in Market Excel, using this tool will be very easy. It includes various BI-oriented features to help automate, analyze, and visualize all of the relevant data and information of a company. Key features: When using Clear Analytics, you don’t need a data warehouse as it pre-aggregates data through the Logical Data Warehouse (LDW) approach. Tracing and auditing data is particularly convenient with this tool, so there remains compliance on all levels of the company. 5 Reasons Why Marketers should Invest in Developing Data Skills 3. Pentaho BI Pentaho BI is one of the leading tools in open source business intelligence. It can gather data from across a variety of sources and transforms this data into helpful insights that can be formulated into well-articulated plans and campaigns. To be precise, Pentaho BI is the ideal tool for businesses looking to scale up profits through better, faster, and accurate decision-making. Key features: It offers an array of rich navigation features that can enhance data visualization when aided by web-based dashboards. The intuitive and interactive analytics of Pentaho BI is equipped with advanced features such as for lasso filtering, zooming, attribute highlight and drill down for improved functioning. 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? 4. MicroStrategy MicroStrategy is an efficient tool that allows companies to access all business data from one place effortlessly. Everything is integrated into a consolidated platform so that business organizations can leverage the data to create meaningful and compelling campaigns. MicroStrategy uses powerful dashboards and data analytics to boost productivity, reduce costs, optimize revenue, and predict new opportunities, all of which are crucial to a company’s growth. It assimilates outstanding analytics capabilities that facilitate the stress-free processing of unstructured text data. The data specialists can further analyze this data through the platform’s text analytics solutions. It is one of the most updated BI tools that incorporate insightful statistical and analytical capabilities. These capabilities facilitate trend forecasting in real-time. It also supports options for third-party data mining. Furthermore, it combines a range of business analytics and methods which allow users to create and share business analytics reports from any device and anywhere. Key features: It can be used both from mobile devices or desktops. MicroStrategy allows you to save data either on-site or in the cloud (powered by Amazon Web services). 5. QlikView upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 QlikView is a super user-friendly platform, incorporating best of both worlds – from tech-savvy business intelligence tools to traditional productivity apps, you’ll find it all here. This tool allows organizations to harness and process data in a way that fosters innovation. Whether you want to enhance, re-engineer, or provide support to the various business processes, Qlikview applications ensure that you come up with exciting and efficient solutions for all business requirements. It is one of the most favored tools for business analytics since it boasts unique features like in-memory processing and patented technology. So, it offers ultra-fast business analytics reports. Key features: QlikView offers a host of customized solutions for sectors such as banking, insurance, etc. It is a self-service tool through which businesses can analyze and manipulate data to gain useful insights.  6. Board Board is included in this list of leading business analyst software tools since it features a cutting-edge business analytics model. This model allows users to develop intuitive and interactive business analytics dashboards and reports. So, it is one of the widely used business intelligence tools for realizing the latest business models. It is also recognized as a highly-scalable business analytics platform. It incorporates top-notch analytical tools for different businesses. It can handle huge data volumes. Moreover, it also supports accurate scenario analysis and estimation by controlling the data fed to scenarios. Key features: “One View” feature combines data sources into a single logical view and conveys all its functionalities in one place. “Self-Service BI” facilitates self-sufficiency by finding answers to business questions without any assistance from IT. 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. Dundas BI It is one of the feature-rich business intelligence tools that provide top-notch business intelligence and business analytics solutions. It uses the R programming language. It uses robust business analytics tools. So, it offers trend forecasting, automated analytics, and an advanced dashboard. These functionalities assist users in visualizing data and developing business analytics reports via drag-and-drop features. Key features: It not just assesses the data but simplifies the multidimensional analytics for users. So users would be able to focus on their business’s crucial tasks. It is accessible via mobile devices to let users access its functionalities on the go. It uses an open API to enable the adjustment and customization of various industry-sector data. Benefits of Business Analytics Tools The valuable insight provided by BA tools allows organizations and companies to chalk out ways to optimize and automate business processes. Not only do BA tools help companies make data-driven decisions, but they also have many more clear-cut advantages: Business analytics make tracking and monitoring business processes extremely efficient and seamless, thereby allowing companies to handle even the most complex of business operations with ease. The market insights offered by BA and BI tools can give you a competitive edge over your competitors as you always get updates about your competitors, latest consumer trends, and also about potential markets. This is highly advantageous for businesses in a competitive environment. BA tools (predictive modeling and predictive analytics) can offer accurate and timely predictions about the market conditions while simultaneously allowing you to streamline your marketing strategies for the best possible outcomes. Using statistical and quantitative analysis, you can get a plausible explanation on why specific strategies fail and why others remain successful. Thus, you get a clearer idea of what kind of plans you should focus on and what to leave out. BA tools can efficiently measure the Key Performance Indicator (KPIs) which can further assist companies to make better and timely decisions. Other benefits of Business Analytics Tools: 1. Enhanced Accountability The best business intelligence tools benefit your company with improved productivity. Many employees don’t effectively respond to micromanagement. This aspect suggests that you have to use cutting-edge tools to assess their productivity. The choice of the best BI tools states the tasks to be done and the deadline by which the work must be completed. 2. Quick completion of projects The leading business analyst tools shorten the projects’ duration. You can use them to decrease the time taken between beginning a project and getting it approved. These tools notify team members when the deadline is near or if they are missed. Moreover, the team leaders inform employees about new projects, modifications in schedules, and policy updates. The time required to determine these concerns is vital in the long run. This means that you need to use cutting-edge business intelligence software to work on profitable projects. 3. Flawless Communication The well-known business analyst tools remove the dependency on telephones and paper chasing. This makes business operations more efficient via flawless communication.  Consequently, it decreases the organization’s turnover rates. One of the reasons for quitting jobs is poor communication in offices. You can use business analytics tracking software to provide employees with a better way of exploring what should be done currently and in the future. 4. Streamlines the business processes The business analyst tools provide valuable insight into the operation of your business. You can use automation software to understand which tasks are to be done concurrently instead of being finished sequentially. Moreover, you can determine which steps are superfluous. This means you will make informed decisions and choices using suitable business intelligence software. For instance, you would know which employees accomplish specific tasks effectively instead of who happens to be free at that specific time.  5. Decreases manual labor The best business analyst tools or best business intelligence software lets your team members work on monotonous tasks. For instance, they can improve their skills by working on facets that need more human input. Reliable work management tools guarantee swift completion of work without human errors. Rather than using these tools to substitute employees, you can use them to make the workplace more dynamic. Ultimately, it enhances the organization’s overall self-esteem. Data Science Summarized In One Picture 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 Applications Of Business Analytics The industries using Business Analytics on a day to day basis will help you understand what is analytics in a much practical fashion. Here are some of the industries: Marketing Business analytics is gaining ground in the field of marketing because it can reveal vital statistics about consumer behavior and market trends. Furthermore, it can help companies to identify their target customers as well as potential markets that promise significant growth. Finance Business analytics is crucial to the finance sector. Using BA tools finance companies can process the vast amounts of data available at their disposal to unravel valuable insights on the performance of stocks and provide advice to the client whether to hold on to it or sell it. Human Resources HR professionals are now using BA and BI tools to conduct relevant background checks on potential candidates. Using BA tools they can find out detailed information about employee attrition rate, high-performance candidates, and so on. Manufacturing Business analytics has also come to play a pivotal role in the manufacturing sector. It can use the data to offer meaningful insights into inventory management, supply chain management, the performance of targets, and risk mitigation plans. Also, BA tools can help companies scale up the efficiency of their operations. What is Customer Analytics and Why it matters? You can also check out IIT Delhi Certification Course in Business Analytics. IIT Delhi is one of the top institutes in India and also one of the oldest IIT’s and is always excelled in giving highly industry-relevant courses, Now IIT Delhi has partnered with upGrad to get these top IIT Delhi courses online. They have a variety of other programs like Machine Learning, Executive Management Programme in Strategic Innovation, Digital Marketing and Business Analytics etc. Today, Business Analytics has become an integral part of the business world. As data keeps on piling up by the minute, more and more organizations are relying on BA and BI tools to boost profitability and optimize business operations. And more students and professionals are rushing to pursue business analytics courses to brush up their knowledge and experience. And with the cut-throat competition that exists today, businesses that do not integrate business analytics within their framework are not only missing out on growth opportunities but also might fail to keep up with the market over time.

by Abhinav Rai

21 Nov 2022