Book Image

Learn Python by Building Data Science Applications

By : Philipp Kats, David Katz
Book Image

Learn Python by Building Data Science Applications

By: Philipp Kats, David 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)
Free Chapter
1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Git

As you may have observed, the complexity of our code grows exponentially from chapter to chapter, it would be a pity to lose or break any code due to the incident. Of course, for any real-world business or service, this would be a disaster. That's why organizations make sure the code is kept safe via version control. Any time we need, we can revert the code to any of the previous versions—or even keep multiple versions of the same code, in parallel. Historically, there were a few technological solutions that allowed this, the most popular being mercurial, subversion, and Git systems. Currently, however, Git is by far the most popular – it is open source, fast, and distributed. You don't have to have the main server for the team to cooperate, but even for a single user, Git could be a life-saver!

In the following section, we will briefly discuss how...