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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

Beginning with Jupyter

Another development environment we'll use is Jupyter. If you have installed Anaconda, then Jupyter is already on your machine, as it is one of the tools that come with Anaconda. To start using Jupyter, we need to run it from the Terminal (you might need to open a new Terminal to update the paths). The following code will run a newer version of the tool's frontend face, and that is what we'll use:

$ jupyter lab

Alternatively, it also supports an older version of the frontend via Jupyter Notebook. The two have their differences, but we'll stick with the lab.

The app's behavior depends on the folder from which it was started; it is more convenient to run it directly from the project's root folder. That's why it is so handy that VS Code's Terminal opens in a workspace folder by itself, as we don't need to navigate...

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