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Learn Python by Building Data Science Applications

By Philipp Kats , David Katz
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  1. Free Chapter
    Section 1: Getting Started with Python
About 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.
Publication date:
August 2019
Publisher
Packt
Pages
482
ISBN
9781789535365

 

Section 1: Getting Started with Python

This section focuses on becoming familiar with general-purpose Python, making use of existing libraries, writing our first scripts, learning the basics of Git, and using the IDE. In this section, we will also lay the foundation for our projects, building pipelines to process (project 1), collect (project 2), and simulate (project 3) data.

This section comprises the following chapters:

About the Authors
  • Philipp Kats

    Philipp Kats is a researcher at the Urban Complexity Lab, NYU CUSP, a research fellow at Kazan Federal University, and a data scientist at StreetEasy, with many years of experience in software development. His interests include data analysis, urban studies, data journalism, and visualization. Having a bachelor's degree in architectural design and a having followed the rocky path (at first) of being a self-taught developer, Philipp knows the pain points of learning programming and is eager to share his experience.

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  • David Katz

    David Katz is a researcher and holds a Ph.D. in mathematics. As a mathematician at heart, he sees code as a tool to express his questions. David believes that code literacy is essential as it applies to most disciplines and professions. David is passionate about sharing his knowledge and has 6 years of experience teaching college and high school students.

    Browse publications by this author
Latest Reviews (3 reviews total)
Great content! I learned a lot through the first chapters. I've done several courses and read books but this book is really thorough.
Really good content, as always.
Se descargo segun fecha. Solo le he dado una revision rapida y parece muy interesante.
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