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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

Data Exploration and Visualization

In the previous chapter, we went deep into data cleaning and preparation. But what is inside this dataset? What story does it tell about the war, and how can we make those stories clear? Knowing how to dissect data, understand it, and extract insights is one of the crucial skills for data analysis and is a mandatory step before building anything driven by this data. In this chapter, we'll learn how to explore a dataset, compute aggregate statistics, and understand outliers and general trends through data visualization. The skills we'll learn are essential to any data analysis and are used throughout the industry and academia.

In particular, the following topics will be covered in this chapter:

  • Descriptive statistics
  • Aggregation and resampling
  • The ecosystem of modern visualizations using matplotlib with pandas, altair, and datashader...
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