Contents

Introduction

Introduction

Practical Python for Data Science by Jill Cates

Python is the “swiss army knife” of programming. There are several factors that contribute to its versatility:

  • it has clean and human-readable syntax so it’s easy to learn

  • it’s an interpreted object-oriented scripting language

  • it has a strong open-source community and a large repository of Python packages

Because of its versatility, Python can be applied to both software development (e.g., building web applications and API’s) and data science (e.g., scientific computing, creating end-to-end data science pipelines). However, writing Python for data science is very different than writing Python for software devleopment. A huge part of the learning curve is getting familiar with the syntax of Python’s data science packages including but not limited to Pandas, NumPy, and scikit-learn.

In this book, we will focus on how to use Python in the context of data science. We will work with a real-life dataset and explore it using the following data science Python packages:

Prerequisites

This book is designed to be accessible for people without a strong technical background. In order to make the most of this book, the suggested requirements are:

  • Basic knowledge of Python

  • Some familiarity with Jupyter Notebooks, Pandas, and Seaborn

  • Googling skills and ability to read documentation

Open a Github Issue

Did you spot an error in this book? Have an idea on how to make the book better? I’m always open to feedback and new ideas. You can contribute by opening a Github issue or creating a pull request with the proposed fix.

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