Subscription

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Learning Hub

Conferences

Free Learning

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

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781789535365

Length 482 pages

Edition 1st Edition

Languages

Python

Tools

Pygame

Concepts

Application Development

Authors (2):

Philipp Kats

David Katz

View More author details

Table of Contents (26) Chapters

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

Leave a review - let other readers know what you think

Section 2: Hands-On with Data

This section introduces us to the world of scientific (or data-oriented) Python, including its own IDE—Jupyter—and a suite of packages that assist in working with data. We will perform exploratory data analysis, draw some charts, and train a number of models. All of this will be implemented through our three projects, utilizing the data we collected in the first section.

This section comprises the following chapters:

Chapter 10, Python for Data Applications
Chapter 11, Data Cleaning and Manipulation
Chapter 12, Data Exploration and Visualization
Chapter 13, Training a Machine Learning Model
Chapter 14, Improving Your Model – Pipelines and Experiments

The rest of the chapter is locked

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at €18.99/month. Cancel anytime

Authors (2)

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.

See other products by Kats

Katz

Doron Katz, originally from Sydney, Australia, completed his bachelor's in internet science (the University of Wollongong), before pursuing a master's in management (Charles Sturt University), including a certification in Microsoft Solutions Development. On moving to San Francisco, Doron has worked with various companies, from start-ups to larger organizations, as a software engineer and project manager. Additionally, he is a regular contributor to various distinguished technical publications and has published numerous white papers and also coauthored Developing an iOS Edge.

See other products by Katz