Table of Contents

1. Introduction
2. Python, R, SAS, and SQL
3. Matplotlib, Seaborn, tqdm
4. sklearn, NumPy, and pandas
5. Jupyter nbextensions
6. Tableau and Google Data Studio
7. Summary
8. References

Introduction

The goal of this article is to give a general overview of the top Data Science tools and languages. I have either used these the most frequently out of others or have worked with others who have commonly used them as well. There are a few unique tools that are quite beneficial that not everyone may not know about additionally that I will be discussing later on. I will give some use cases for my examples so you can see why these tools and languages are so valuable. I have previously written about some of these tools and languages, so in this article, I will add more information as well as new information.

Keep on reading if you want to know more about the top tools and languages for Data Scientist, as well as why you should be using them.

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