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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

3 (3)
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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Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Summary

In this chapter, we learned how to form our code into production-level data pipelines that can be scheduled and re-run on demand. Building good pipelines is an important skill, as it enables you to have the data up to date and work on your business logic (for example, parsing the information), rather than running and re-running pipeline scripts or building your own bicycle solution. This reliable and robust solution is a good way to deploy and schedule your code as a deliverable. In the later part of this chapter, we learned about the different output formats and custom templates in luigi.

In the next chapter, we'll build on top of the pipeline we set up. We will use the data we collected to build a couple of interactive dashboards, allowing us to monitor the process and analyze ongoing trends in the data.

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Learn Python by Building Data Science Applications
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