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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
2.8 (4)
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

2.8 (4)
By: Kats, Katz

Overview of this book

Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
Table of Contents (26 chapters)
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1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Shell, Git, Conda, and More – at Your Command

In this chapter, we will review a bunch of non-Pythonic tools that will make your life as a developer significantly easier, and that will help you communicate on the same terms with other developers. While this book is focused on teaching Python, nothing exists in a vacuum. Any developer (Python or not) uses multiple technologies and languages in their daily routine, knowing that at least some of them will improve their productivity significantly. Before we move on to building ever more complex substances, let's briefly review some tools beyond Python itself.

In this chapter, we will cover the following topics:

  • Shell
  • Running Python scripts and designing command-line interfaces
  • Git and GitHub
  • Reproducible virtual environments with conda
  • Structuring your workflow with a Cookiecutter
...

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