Are data science bootcamps worth it to get into the world of Analytics?

Upon seeing this extremely intriguing question, we asked the same to Sarita Digumarti, Co-founder, Jigsaw AcademyWith over 20 years of experience in analytics and consulting across diverse domains including retail, healthcare and financial services, Sarita has worked both in India and the US, helping clients tackle complex business problems by applying analytical techniques. Read on to see what she has to say.


Data Science is a complex field. Increasingly it requires a person to master multiple skills – programming, mathematics, statistics, business operations and process understanding, domain understanding, algorithms, databases and data management, data visualization and so on. Given all of these skill requirements, and the level of mastery required in each of these skills, is it possible to attend a short bootcamp program (10 – 16 weeks) and really learn enough to land a data science job?

The answer depends on a few things – what you learn, what you can leverage from your past academic and industry background, and the type and level of the data science role you aim for.

1. You should start with what you learn. A bootcamp program is typically designed to be an intensive and immersive program, which means that a good bootcamp will include multiple hours (6+) per day and include teaching time and practice time. And this schedule will be followed most days of the week (or every weekend if you choose a weekend program). The large number of contact hours and practice hours allow bootcamp programs to cover a lot of material in a relatively short period of time and ensures that you are logging in plenty of practice to stay on track with the program as it progresses.

2. Second, review how you can leverage your past qualifications and experience. If you are a fresher, a data science bootcamp is a good idea because it will cover enough material for entry level jobs.

If you are not a fresher, then you need to look at how your past experience can help. IT experience on the application development areas could help with programming and product development in analytics, whereas database management skills can help in roles that need heavy duty data processing requirements. Similarly, if you have experience with financial services, then you should be able to leverage that experience for data science in fin-tech companies or banks.

3. The third and most important thing is the type of data science role you are aiming for.  At the risk of repetition, data science is a vast and complex field, and there is no way a short program, however well designed, is going to help you master the topic.

However, a good bootcamp program will allow you to master the fundamentals of applied math and statistics required, will ensure you get enough practice with important tools, will expose you to enough business cases for you to gain broad understanding of multiple implementations, and ideally, allow you to work on at least one live or near live project.

There are many roles within the broad ambit of data science – data analysts, business analysts, data science managers, data science program managers, machine learning developers, data engineers and so on. For example, a machine learning developer role will require a lot of programming expertise, as well as some ML expertise, so if you are very strong in programming, a bootcamp program will certainly give you enough knowledge to aim for a role like this

Finally, don’t forget that learning is a continuous process. Once you finish your program, you should continue the learning by practicing on sites like Kaggle, and reading on sites like Data Science Central. You will certainly be able to build a strong skill set over time.

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