Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

by Philipp Kats, David Katz


View All Available Formats & Editions
Members save with free shipping everyday! 
See details


Understand the constructs of the Python programming language and use them to build data science projects

Key Features
  • Learn the basics of developing applications with Python and deploy your first data application
  • Take your first steps in Python programming by understanding and using data structures, variables, and loops
  • Delve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in Python
Book Description

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.

What you will learn
  • Code in Python using Jupyter and VS Code
  • Explore the basics of coding – loops, variables, functions, and classes
  • Deploy continuous integration with Git, Bash, and DVC
  • Get to grips with Pandas, NumPy, and scikit-learn
  • Perform data visualization with Matplotlib, Altair, and Datashader
  • Create a package out of your code using poetry and test it with PyTest
  • Make your machine learning model accessible to anyone with the web API
Who this book is for

If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.

Product Details

ISBN-13: 9781789535365
Publisher: Packt Publishing
Publication date: 08/30/2019
Pages: 482
Product dimensions: 7.50(w) x 9.25(h) x 0.97(d)

About the Author

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. 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.

Table of Contents

Table of Contents

  1. Preparing the workspace
  2. First Steps in coding variables and data types
  3. Functions
  4. Data Structures
  5. Loops and other compound statements
  6. First script: Geocoding with Web APIs
  7. Scraping Data from the Web with Beautiful Soup 4
  8. Simulation with Classes and inheritance
  9. Shell, Git, Conda, and More at Your Command
  10. Python for Data Applications
  11. Data cleaning and manipulation
  12. Data Exploration and Visualization
  13. Training a Machine Learning model
  14. Improving your Models Metrics pipelines and experiments
  15. Packaging and testing with poetry and pytest
  16. Data Pipelines with Luigi
  17. Lets build a dashboard
  18. Serving models with Rest API
  19. Serverless API using Chalice
  20. Best practices and Python performance

Customer Reviews