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Learn Python by Building Data Science Applications

You're reading from   Learn Python by Building Data Science Applications A fun, project-based guide to learning Python 3 while building real-world apps

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789535365
Length 482 pages
Edition 1st Edition
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Authors (2):
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Philipp Kats Philipp Kats
Author Profile Icon Philipp Kats
Philipp Kats
David Katz David Katz
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David Katz
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Table of Contents (26) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Preparing the Workspace FREE CHAPTER 3. First Steps in Coding - Variables and Data Types 4. Functions 5. Data Structures 6. Loops and Other Compound Statements 7. First Script – Geocoding with Web APIs 8. Scraping Data from the Web with Beautiful Soup 4 9. Simulation with Classes and Inheritance 10. Shell, Git, Conda, and More – at Your Command 11. Section 2: Hands-On with Data
12. Python for Data Applications 13. Data Cleaning and Manipulation 14. Data Exploration and Visualization 15. Training a Machine Learning Model 16. Improving Your Model – Pipelines and Experiments 17. Section 3: Moving to Production
18. Packaging and Testing with Poetry and PyTest 19. Data Pipelines with Luigi 20. Let's Build a Dashboard 21. Serving Models with a RESTful API 22. Serverless API Using Chalice 23. Best Practices and Python Performance 24. Assessments 25. Other Books You May Enjoy

Preface

There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion.
Donella H. Meadows, Thinking in Systems: A Primer

Python has become one of the most popular programming languages in the world, according to multiple polls and metrics. This popularity is, to no small extent, a direct result of the simplicity of the language, its power, and scalability, allowing it to run even large-scale applications, such as Dropbox, YouTube, and many others. It becomes even more valuable with the rise in the adoption of machine learning techniques and algorithms, including state-of-the-art algorithms on the edge of scientific advancements.

Consequently, there are hundreds of books, courses, and online tutorials on different aspects of programming, machine learning, data processing, and more. Many sources highlight the importance of learning-by-doing and building your own projects. Connecting the dots and structuring all this vast knowledge into one big picture is not an easy task. Seeing the big picture, in our opinion, is critical for the completion of any project. Indeed, there are plenty of options and decisions to take at every step. It is the grand schema of a project as a whole that helps you make those decisions, focus on what matters, and spend your time wisely.

This book is designed to be an entry point for any newcomer or novice developer, aiming to cover the whole life cycle of a data-driven application. By the end of it, you will be able to write arbitrary Python code, collect and process data, explore it, and build your own packages, dashboards, and APIs. Multiple notes and tips point to alternative solutions or decisions, allowing you to alternate code for your specific needs.

This book will be a useful resource if any of the following apply to you:

  • You have just started to code.
  • You know the basics but struggle to build something handy.
  • You know your specific domain well—whether it be statistics, machine learning, or development—but lack experience in other parts of building a project.
  • You're an experienced developer with little exposure to Python, trying to learn about the Python package's ecosystem.

If you feel you fall into any of those categories, or want to build a project from scratch for other reasons, please join us on this journey.

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