In Part 1 of How to Become a Data Scientist, we learned about the role of a data scientist, the kinds of problems that data scientists solve, and the approach a data scientist takes to solve a business problem. In this article we will look at the essential tools that a data scientist must learn to succeed in his job.

• R Programming: R is by far the most popular programming language used f r data analysts. It has been the programming language of choice for many chief data scientists in some of the largest organizations. R has huge community support and has libraries for doing almost any activity such as statistics, and data visualization.
• Python: Over the past couple of years, as data is moving mainstream, the adoption of Python has gone up in the data science world. Python again has huge community support and has libraries for performing almost any task. Proponents of Python believe that R is not really a programming language, but an interactive environment for performing statistical analysis. R, on the other hand, is a complete programming language, with library support for data analysis. It is easy to learn, and can be used to build enterprise scale data products as well.

• Pandas, Numpy, and Scipy: These are the three python libraries that are essential to perform data analysis in python. Pandas is a python library designed for data manipulation and analysis. It provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. Numpy is a library for scientific computing in python. It makes operations such as matrix multiplication very easy to perform. Scipy extends Numpy and contains modules for statistics, optimization, integration, linear algebra and other mathematical operations.
• Visualization libraries: For data visualization, you should be familiar with R and python libraries such as Matplotlib and Ggplot2. Both Matplotlib and Ggplot help you create static graphs and visualizations. However, if you want you graphs to be interactive, then you should get familiar with D3.js which is a javascript library for creating interactive data visualizations.
• SQL and NoSQL databases: Most of the data resides in different kinds of databases and one of the data scientist’s jobs is to retrieve data from these databases. Therefore, it is important that a data scientist is familiar with both SQL and NoSQL databases and how to extract data from them.
• Scikit-Learn: If you are dealing with huge amounts of data, and want to use it to predict the future or suggest calculated behaviours, then you need to learn the techniques for machine learning. Scikit-Learn is a machine learning library for python and features various machine learning algorithms, such as regression, Bayes, random forests, gradient boost, and k-Means.

To learn these skills, you don’t necessarily have to go to a college. With a little bit of motivation and love for data, you can learn and apply these skills yourself using online tutorials and books and build a successful career as a data scientist.