Learn Python by Building Data Science Applications
- Paperback: 482 pages
- Publisher: WOW! eBook (August 30, 2019)
- Language: English
- ISBN-10: 1789535360
- ISBN-13: 978-1789535365
Learn Python by Building Data Science Applications: Understand the constructs of the Python programming language and use them to build data science projects
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.
- Code in Python using Jupyter and VS Code
- Explore the basics of coding – loops, variables, functions, and classes
- Deploy continuous integration with Git, Bash, and DVC
- Get to grips with Pandas, NumPy, and scikit-learn
- Perform data visualization with Matplotlib, Altair, and Datashader
- Create a package out of your code using poetry and test it with PyTest
- Make your machine learning model accessible to anyone with the web API
By the end of the Learn Python by Building Data Science Applications 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.