How do I become a data scientist?

Gaurav Vohra | May 27,2013 01:00 pm IST

Data scientists are the new astronauts. Everyone wants to become one.

And it is not difficult to understand the reason for this.

In this age of Big data, more and more businesses are relying on people who can make sense of the vast amounts of information generated around us people who can use sophisticated tools and complex-sounding statistical techniques to derive insights from larger and larger mounds of data.

Businesses have started to understand the power of data. They realize they can use it to make better and faster decisions, outwit their competitors and be more successful. More and more, they are reaching out for people who have the skills to do this.

It is no wonder that there is a huge demand for trained analytics professionals. A recent report suggested there will be a shortfall of over 150000 analytics resources in the US, another study suggested a similar shortfall in India as well.

The gap between demand and supply is increasingly rapidly and this is reflected in the increasing salaries data scientists can command. In the US, data scientists are already commanding higher salaries than MBAs. In India, starting salaries range from 4 lakhs to 8 lakhs.

So how does one go about becoming a data scientist? What are the skills required to succeed in this field?

Well, there is no simple answer to this. Data scientists are a curious breed. They need to possess not just one skill but a combination of multiple skills. Let us examine the skill set requirement in more detail.

Technical Skills
Understanding of Statistics Data scientists need to have a good understanding of basic and advanced statistical concepts. These concepts form the basis for most predictive modelling techniques and therefore one needs to understand them well. Knowledge of concepts like measures of central tendency and dispersion, probability distributions, hypothesis testing and probability are essential for most sophisticated analyses.

Knowledge of predictive modelling techniques Predictive modelling techniques like regression, clustering and decision trees are applied on historical data to predict the future. It is these predictions that guide a businesss strategy. Thus a knowledge of common predictive modelling techniques, their application, best practices involving their use etc. is a must in this field.


Proficiency on analytic tools Analytic tools are specialized tools that are used to analyse large amounts of data. These tools allow the data scientists to perform descriptive as well as predictive analytics. A data scientist needs to be proficient in one or more analytic tools in order to do her job effectively. Microsoft Excel is the most popular analytic tool in the world. It does an excellent job of performing descriptive analytics on limited amounts of data. SAS is also an extremely powerful and popular tool. It allows users to build many kinds of predictive models on huge amounts of data.
For a more detailed discussion on various analytic tools available in the market, click here.

To find out which is the best tool for you, click here.

Inherent Qualities
Quantitative aptitude Data scientists need to have a strong quantitative aptitude. They should be comfortable dealing with numbers, large excel sheets or even larger databases.

Inquisitive Nature Call it thirst for knowledge, intellectual curiosity or inquisitive nature a data scientist needs to work like an investigator. She has to sift through mounds of data to find useful things. She needs to know where to look and what to look for. She needs to have the ability to ask the right questions and the persistence to find the answers.

Stakeholder Management
Analytics is always applied in the context of a business situation usually a problem or an opportunity. A data scientist cannot work in isolation. She has to work with multiple business stakeholders. A good data scientist will have the ability to explain the results of an analysis in non-technical terms in order to build consensus amongst the business stakeholders.

These are all the skills and qualities that are needed in order to succeed in the exciting and high growth field of analytics. If you feel you have the inherent traits to become a data scientist, you should seriously think about equipping yourself with the requisite technical knowledge as well.
At Jigsaw Academy, our Foundation course has been designed by industry veterans and covers all the 3 required technical skills. Our course has helped thousands of people move into analytics.


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Gaurav is an IIM alumnus with over 10 years of experience in the field of analytics and has worked across multiple verticals including financial services, retail, FMCG, telecom, pharmaceuticals and leisur...