This is the code repository for Learn Python by Building Data Science Applications, published by Packt.
A fun, project-based guide to learning Python 3 while building real-world apps
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 book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter01.
The code will look like the following:
import pandas as pd
for word in 'Hello Word!'.split():
print(word)
Following is what you need for this book: This book is aimed at new Python developers with little to no prior programming skills beyond basic computer literacy. The book doesn't require any previous background in data science or statistics either. That being said, it covers a variety of topics, from data processing to visualization, to delivery—including dashboards, building APIs, Extract, Transform, Load (ETL) pipelines, or a standalone package. Thus, it is also suited to experienced data scientists interested in productizing their work. For a complete novice, this book aims to cover all major parts of the data application life cycle—from Python basics to scripts, data collection and processing, and the delivery of your work in different formats.
With the following software and hardware list you can run all code files present in the book (Chapter 1-20).
| Chapter | Software required | OS required |
|---|---|---|
| All | Python 3, Visual Studio Code, Anaconda | Windows, Linux, macOS |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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.
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