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How to become a data scientist in 2017

How to become a data scientist in 2017

Data scientists are in huge demand and we tell you how to pick up the tools of this very popular trade

It is one of the hottest fields in information technology what with Glassdoor and LinkedIn citing it as the hottest jobs to watch out for in 2017. In this article, we tell you how to become a data scientist in 2017. If you need any more validation, a recent article authored by Thomas Davenport and DJ Patil in the Harvard Business Review pegged ‘data scientist’ the sexiest job of the 21st century.

A career in data science requires a thorough understanding of mathematics and statistics. At the heart of data science is machine learning, analytics and statistical skills to draw meaningful insights. Some of the most commonly asked for skills in data scientist in 2017 are — R, Python, Hadoop, Apache Spark, Scala, machine learning among others.  Data scientists are also skilled in knowledge of different data mining techniques such as regression, clustering, decision trees and support vector machines.

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What exactly is data science and how can one become a data scientist?

At its heart, data science is the art of analyzing petabytes and terabytes of data in a short span of time and extracting useful information from huge volumes of data. Over the years, data scientists have successfully created new fields of knowledge such as predictive analytics which is used extensively in manufacturing, retail and healthcare and helps in streamlining operations and bringing down costs significantly.  Many believe data scientists are statisticians first who are simply up to speed with technology. But call it what you want, data scientists are in high demand and there is a glaring skill gap that needs to be filled.

Want to become a data scientist: here’s the ultimate guide to becoming a data scientist in 2017

  • The first step is to get good at statistics, if possible take up a crash course in statistics.
  • While an advanced degree in computer science is usually preferred, it is not a must.
  • Get hands on knowledge of programming skills in R, Scala and Python
  • Learn Visualization tools such as Tableau, Microsoft Power BI
  • Understanding of Hadoop and Apache Spark is a must
  • Learn the fundamentals of machine learning algorithms
  • Learn the important tools of the trade – R programming, Python, SPSS, Apache Spark, SQL

Which skills are most required in a data scientist?

Some of the prerequisites in a data scientist and we list it priority wise are — machine learning, backed by statistics, data analysis and in programming languages we have R and Python followed in SQL. So if you have a background in statistics and engineering, you are off to a wonderful start. However, you must brush up your machine learning skills because as a data scientist, you would be working primarily with ML libraries and data visualization libraries. Simply put, the job of the data scientist is to work on analysis and modeling of data.

Domain expertise and soft skills are equally crucial as technical skills:

However, besides technical skills, a deep industry knowledge and problem-solving skills are definitely a must have.  You cannot find insights in real life datasets if you are not asking the right questions. Finding patterns and insights is dependent on variables which are assigned by data scientist. Ask any data scientist and they will tell you that after data cleansing, you apply domain knowledge to decide which algorithm is the best fit for seeking results.  Data scientists are meant to resolve business problems by addressing the right problem that will yield most value to enterprises.

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How is the role of Data Scientist different from a Data Analyst?

Undoubtedly, the terms are sometimes used interchangeably and even job descriptions sometimes refer data analyst as junior data scientist. Though in skillset, some of the skills of data analyst and data scientists are similar, data scientists usually have a background in applied statistics and their sophisticated skills come in demand in dealing with huge volumes of data. A data scientist will have knowledge of Big data technology such as Hadoop and apache-spark as well.

Meanwhile, data analysts are the guys who slice and dice data, are more customer facing and well-versed in BI – Tableau or other analytics solutions. They are the people tasked with bringing data to life with reports.

Pay Packages

When it comes to pay packages, data scientists take home a fat package.

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Salary range of data scientist at entry level: $110,000

Salary range of data analyst at entry level: $50,000 – $75,000

To get more information on how to become a data scientist in 2017, refer this study.

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