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Top 6 Skills Required to Become a Successful Data Engineer [2023]

Top 6 Skills Required to Become a Successful Data Engineer [2023]

Updated on 19 February, 2024

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• 13 min read

Table of Contents

Tech Skills for Data Engineering
Frameworks for Data Engineering
How to Learn The Skills Required for Data Engineering?
Challenges and Solutions
Conclusion

As a seasoned data engineer, I’ve learned firsthand the importance of mastering critical data engineering skills to thrive in this dynamic field. In today’s data-driven world, the demand for skilled professionals who can navigate complex datasets and extract valuable insights is ever-growing. This article shares insights into the tech skills required to become a successful data engineer in 2024. Whether you’re just starting your journey in this field or looking to enhance your existing skill set, understanding and honing these abilities will be instrumental in your success as a data engineer. Let’s delve into the world of data engineering and explore the skills that will set you apart in this competitive landscape.

Tech Skills for Data Engineering

If you are looking for the data engineer required skills then this is the correct place to seek in-depth knowledge of the data engineer skills required to become a recognisable name in the industry. Big data skills are essentially required for becoming an efficient data engineer with exceptional data engineer skills.

Building, designing, creating along with maintaining data pipelines for the purpose of collating raw data from a plethora of sources are a few of the many data engineer skills required to flourish in the industry. Amongst data engineer technical skills it is important to be acquainted with developing data infrastructures, databases, data frameworks and so on.

Other data engineer technical skills such as Excel, Python, HPCC, Pig, Docker, Hadoop, Scala, SAS, SPSS, and Strom are also demanded. Besides, in order to develop a career in big data, data engineer technical skills that will facilitate are as follows:

1. Data Warehousing

Data warehouses enable you to store large amounts of data for query and analysis. From heterogeneous sources, the tool streamlines data. The said tools accumulate data from distinctive sources while converting it for analysis. Further, it loads data into the warehouse.

The significant tool facilitates industries to enhance big data in meaningful tactics. The data can come from multiple sources such as ERP software, accounting software, or a CRM solution. Organizations use this data to generate reports, perform analytics, and data mining to generate valuable insights.

You must be familiar with the basic concept of data warehousing and the tools related to this field, Amazon Web Services, and Microsoft Azure. Data warehousing is among the fundamental skills required for data engineering professionals.

2. Machine Learning

Machine learning has become one of the most popular technologies in the last few years. A machine learning algorithm helps you predict future results by using historical and present data.

As a data engineer, you only need to be familiar with the basics of machine learning and its algorithms. Understanding machine learning essentially requires better foundation skills, especially in statistics and mathematics. Being familiar with machine learning will help you in understanding your organization’s requirements and collaborate with the data scientist more efficiently.

This, in turn, enables the unveiling of trends and accelerates the process alongside patterns. Apart from these benefits, learning about machine learning will help you in building better data pipelines and produce better models.

3. Data Structures

Among data engineer required skills, it is crucial to managing databases along with having sound knowledge of database design and architecture. Although a data engineer usually performs data optimization and filtering, it would benefit you to know about the basics of data structures.

This information comes in raw format alongside cannot be used directly. Therefore, it is needed to be transformed into a consumable form to process. It would assist you in understanding the various aspects of your organization’s goals and help you to cooperate well with other teams and members.

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4. ETL Tools

ETL stands for Extract, Transfer, Load, and denotes how you extract data from a source, transform it into a format, and store it into a data warehouse. ETL uses batch processing to ensure users can analyze relevant data according to their specific business problems.

It gets data from multiple sources, applies particular rules to the same, and then loads the data into a database where anyone in the organization can use or view it. As you may have realized, ETL tools are among the most important skills for data engineering professionals.

5. Programming Languages (Python, Scala, Java)

Python, Java, and Scala are some of the most popular programming languages. Python is a must-have for a data engineer as it helps you perform statistical analysis and modelling. On the other hand, Java helps you work with data architecture frameworks and Scala is simply an extension of the same.

You should note that nearly 70% of job descriptions for this field require Python as a skill. As a data engineer, you must have strong coding skills as you’d need to work with multiple programming languages. Apart from Python, other popular programming skills include .NET, R, Shell Scripting, and Perl.

Java and Scala are vital as they let you work with MapReduce, a vital Hadoop component. Similarly, Python helps you in performing data analysis. You must master at least one of these programming languages.

Another language to watch out for is C++. It can compute vast amounts of data in the absence of a predefined algorithm. Moreover, it’s the only programming language that lets you more than one GB of data within a second. Apart from these advantages, C++ lets you apply predictive analytics in real-time and retrain the algorithm. It’s among the most important skills required for data engineers.

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6. Distributed Systems

Distributed systems have become widely popular as they reduce storage and operation costs for organizations. They let organizations store large amounts of data in a distributed network of smaller storages. Before the arrival of distributed systems, the cost of data storage and analysis was quite high as organizations had to invest in larger storage solutions.

Now, distributed systems such as Apache Hadoop are very popular and a data engineer needs to be familiar with them. You should know how a distributed system works and how you can use the same. Apart from the distributed system, you should know how to process information through the same.

Apache Hadoop is a widely popular distributed framework while Apache Spark is a programming tool for processing large amounts of data. You should be familiar with both of them as they are among the vital skills for data engineering professionals.

7. Tools For Data Ingestion

Data ingestion is among the most important parts of data engineer skills. It turns complex with the increment of data. Thus, requires experts or professionals for the purpose of effective ingestion.

8. Tools For Data Mining

Extraction of information for learning the patterns is difficult in huge data sets along with analysing those efficiently is among the utmost skills that are needed to be successful in this path.

9. Skills For Data Visualising And Cloud Computing

Insurance of high data availability alongside the development of cloud storage is an important skill to learn. Besides, it is necessary to provide insights and learnings in a consumable format for end users.

10. Frameworks For Real-Time Processing

Among the important data engineering skills of a data engineer is to make quick decisions while gaining deeper insights.

11. Data Buffering

Data engineering skills are necessary for processing data power. Data buffer stores data temporarily while streaming data is spontaneously generated from numerous sources.

Frameworks for Data Engineering

1. Apache Hadoop

Apache Hadoop is an open-source framework that lets you store and manage Big Data applications. These applications run within-cluster systems and Hadoop helps you manage the same. One of the most important data engineering skills is to create Hadoop applications and manage the same effectively. Since its arrival in 2006, Hadoop has become one of the must-haves for any data professional. It has a wide collection of tools that make data implementations easier and effective.

Hadoop lets you perform distributed processing of large datasets by using simple programming implementations. You can use R, Python, Java, and Scala with this tool. This framework makes it affordable for companies to store and process large amounts of data as it lets them perform the tasks through a distributed network. Apache Hadoop is an industry staple and you should be well-acquainted with it.

2. Apache Spark

Apache Spark is another must-have tool you must be familiar with if you want to become a data engineer. Spark is an open-source distributed general-purpose framework for cluster computing. It offers an interface that lets you program clusters with fault tolerance and data parallelism. Spark uses in-memory caching and optimized query implementation to process queries quickly against any data size. It’s an essential tool for large-scale data processing.

Apart from its capabilities of processing large amounts of data quickly, it is compatible with Apache Hadoop, making it quite a useful tool. Apache Spark lets you perform steam processing which has constant data input and output. Spark is more efficient than Hadoop which is why it has become such a popular tool for data engineers.

3. AWS

AWS stands for Amazon Web Service and it’s the most popular tool for data warehousing. A data warehouse is a relational database focused on analysis and query to help you get a long-range view of the data. Data warehouses are the primary repositories of integrated data from one (or multiple) sources.

As a data engineer, you’ll have to work with a lot of data warehouses so it’s necessary to be familiar with the various data warehousing applications. AWS and Redshift are the two tools you must be acquainted with as most data warehouses are based on these two.

AWS is a cloud-based platform that lets you access your data engineering tools as well, so learning it will certainly help you with other tools. Almost every data engineering job description requires you to be familiar with AWS.

4. Azure

Azure is a cloud-based technology that can help you with building large-scale analytics solutions. Like AWS, it’s a must-have for any data engineer. Azure automates the support of applications and servers with a packaged analytics system. Primarily, Azure is popular for building, deploying, testing, and managing services and applications through data centres. It has various solutions available as Iaas (Infrastructure as a Service), SaaS (Software as a Service), and PaaS (Platform as a Service).

Azure helps you set up Windows-based server applications quickly and efficiently. As Windows is widely popular, the demand for this tool is quite high.

5. Amazon S3 and HDFS

Amazon S3 (Amazon Simple Storage Service) is a part of AWS which offers you a scalable storage infrastructure. HDFS is the Hadoop Distributed File System and is a distributed storage system for Apache Hadoop. Both of these tools let you store and scale easily.

With the help of these two solutions, an organization can store virtually an unlimited quantity of data. Moreover, it offers cloud-based storage so you can access the data from anywhere and work on it. These solutions are popular for offering storage to mobile applications, IoT applications, enterprise applications, websites, and many others.

6. SQL and NoSQL

SQL and NoSQL are must-haves for any data engineer. SQL is the primary programming language for managing and creating relational database systems. Relational database systems are tables that contain rows and columns and are widely popular. On the other hand, NoSQL databases are non-tabular and are of various kinds according to the data model. Common examples of NoSQL databases are documents and graphs.

You should know how to work with Database Management Systems (DBMS) and for that, you’d need to be familiar with SQL and NoSQL. Some additional SQL skills include MongoDB, Cassandra, Big Query, and Hive. By learning about SQL and NoSQL, you can work with all kinds of database systems.

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How to Learn The Skills Required for Data Engineering?

As you can see, data engineering is quite an advanced field and requires learning a lot of skills. Learning all of these skills can be quite challenging and cumbersome. The best way to learn the various data engineering skills we discussed, you can check out upGrad’s data engineering course.

A course will help you get a structured and streamlined learning experience. Our data engineering course lets you learn from industry mentors who ensure you can get rid of your doubts quickly. The course will provide you with industry projects so you can test out your skills and see how far you’ve come.

Projects can be an excellent way to measure your progress and learn the applications of your skills. Our course comes with job placement assistance and learning support so you don’t face any issues.

Challenges and Solutions

Overcoming Skill Gaps

As a seasoned data engineer, I understand the importance of addressing skill gaps in the ever-evolving landscape of data engineering. To bridge these gaps, continuous learning and upskilling are essential. Whether through online courses, workshops, or hands-on projects, investing in education tailored to data engineer skills required is paramount. Additionally, seeking mentorship and networking with experienced professionals can provide valuable insights and guidance on areas needing improvement.

Balancing Multiple Skills

For data engineering, proficiency in a diverse range of skills is necessary. However, balancing these skills can be challenging, especially for newcomers. Prioritizing and focusing on mastering essential data engineer skills for freshers is key. By breaking down the data engineer skills list into manageable components and dedicating time to each, one can gradually build proficiency. Furthermore, seeking opportunities to apply these skills in real-world projects can accelerate the learning process and enhance overall competency.

Conclusion

If you’re interested in pursuing a career in data engineering, you should learn all the skills we listed in this article. They are the fundamental skills required for data engineering professionals.

We hope that you found our article on data engineering skills useful. If you have any questions or suggestions regarding this article, do let us know through the comment section below. We’d be happy to help you!

Frequently Asked Questions (FAQs)

1. What are the core responsibilities of a data engineer?

Data is said to be the heart of every organization. Therefore, the role of data engineers becomes much more crucial for the company’s growth. A data engineer is responsible for the following major tasks:
1. Handling, organizing, and preparing raw data for further analysis.
2. Maintaining various business-oriented data architectures that suit their company’s requirements.
3. Researching about obstacles and solutions to business problems and their objectives.
4. Comparing past performances and patterns in data and implementing necessary modifications in the data models used.
5. Analyzing data and visualizing it with the help of reports, dashboards, and plots.
6. Extracting data and preparing it for various further processes including analysis, modelling.

2. What is the career path of a data engineer?

Data Engineer is one of the most trending technical sectors that rewards you almost better than any other field but asks you to follow a certain career path to be a deserving data engineer. The following career path will help you to pursue data engineering:
1. Bachelor’s degree - First of all, you have to acquire a bachelor’s degree in Computer Science (CS), Information Technology (IT), or Mathematics. For that, you must opt PCM as your stream for higher education or you can take Mathematics as an additional subject.
2. Entry-Level Job - After completing your bachelor’s degree, you should get an entry-level job as a data analyst or a junior data scientist for gaining experience before getting into the big games.
3. Master’s degree - Data Engineering is a field that requires at least a master’s degree or a PhD to get bigger opportunities. You can get your master’s parallel with your entry-level job too.
4. Get a Promotion - Once you are done with your studies, there is no one stopping you from applying for higher opportunities.

3. How much does a data engineer earn on average?

Data Engineers in India earn handsomely. A data engineer with experience of 1-4 years earns somewhere around ₹7,37,257 lacs per annum. Further, as your experience increases, the rise in salary drastically increases. The salary of a data engineer is directly proportional to the salary he/she is getting paid.
The mid-level data engineers with 5-9 years of experience get paid around ₹1,218,983 lacs per annum. Engineers with 15 years or above experience get a lavishing package of ₹1,579,282 lacs per annum.

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by Prof. S. Sadagopan

11 May'16

5.19K+

Enlarge the analytics & data science talent pool

Note: The articlewas originally written by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. A Better Talent acquisition Framework Although many articles have been written lamenting the current talent shortage in analytics and data science, I still find that the majority of companies could improve their success by simply revamping their current talent acquisition processes. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. We’re all well aware that strong quantitative professionals are few and far between, so it’s in a company’s best interest to be doing everything in their power to land qualified candidates as soon as they find them. It’s a candidate’s market, with strong candidates going on and off the market lightning fast, yet many organizational processes are still slow and outdated. These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent. Here are the key areas I would change to make hiring processes more competitive: Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids. Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal. Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks. Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t. Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts. Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback. Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis Redesigned Interview Process In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation. So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process: Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time. Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience. In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial. At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills. Read between the resume lines Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy. Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately. 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 Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 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? Our learners also read: Free Python Course with Certification And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?

by upGrad

14 May'16

5.69K+

UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees 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 Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. 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? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself

by Omkar Pradhan

09 Oct'16

5.69K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 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? 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 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 “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Apoorva Shankar

07 Dec'16