Data Science is combination of computer science, statistics, mathematics and a pinch of creativity. Further sub-domains of data science are machine learning, classification, cluster analysis, data mining and databases. According to Harvard Business Review, it is the “Sexiest Job of the Twentieth Century”. Mckinsey & Company projects a demand of 1.5 Million data scientists in the coming years.

So what does data science necessarily do?

It’s simple. In the past, there were two core functions of any organization.

• Data Mining
• Data Analysis

Data Mining focused on extracting data from data sources from their raw forms, and giving it to data analysts to solve day to day problems. But with problems related to large data sets of big data, hardware and software restraints, and lack of expertise on the part of data analysts, emerged a new field “Data Science”.

Let’s look at it this way. If you want to answer the following question:

What is the probability that Joe would buy a Starbucks Cappucino on a Tuesday morning, if he is taking the Route 66 highway.?

This question itself has around 4-5 variables. In Data Science, the question takes into account more than a 100 variables, therefore data scientists have the ability to decipher the information in less than 10-20 minutes if they constructed data models beforehand.

In terms of salary, data scientists are highly paid with a median mid-salary income of U.S $135,000 and a starting salary of $92,000. But one must not look at the salary figures but at a more important question.

Why should a Marketing and Finance/Economic Major pursue Data Science?

Apart from the basic requirement of sales, marketing majors need to adjust demand of a certain product and know their consumer behavior, while finance majors are required to calculate the volatility of the unpredictable Pakistani Stock Exchange, NASDAQ etc. Say you graduate from University of Wisconsin with a major in Marketing. You have acquired an understanding of different marketing concepts over the years but haven’t developed a certain skill-set which could be used to actually grasp the customer/consumer demand and/or the behavior.

A BBA/MBA Degree is not designed to facilitate your technical skill-set but a theoretical one. This can also be said for an economics degree. To build a collective skill-set, using your knowledge of the degree, and data science, you can build that technical skill-set to answer questions that couldn’t be answered before, or took longer to answer, with limited tools. So what are the tools that are used in data science?

There are many tools that are used by a Data Science. Some common tools are:

• R
• Python
• Hadoop
• MatLab
• Kaggle
• Stata

So, how does one become a data scientist?

There is no single path, which one can follow to become a data scientist since the term is relatively new and heavily debated on. Universities across the globe has started to offer data science/analytics programs, but based on loose curriculum that can’t be standardised. There are many micro-master programs online that can give you certifications on coursera, edx in data science, but these certifications won’t get you the market value deal that a degree would get you. Here are some courses you can start taking to get your pre-requisites for data science ramped up, if you decide to pursue the field.

• C++
• Python
• Linear Algebra
• Calculus I, II (recommended)
• Statistics and Probability

It is to be noted that no coursework or degree determines the magnitude of whether you have become a data scientist but the composition of your expertise at the tools you utilize and the mathematical/statistical models you use.