Learn Python by Building Data Science Applications: A fun,project-based guide to learning Python 3 while building real-world apps
Authors: Philipp Kats – David Katz
ISBN-10: 1789535360
ISBN-13: 9781789535365
Publication Date 出版日期: 2019-08-30
Print Length 页数: 482 pages
Book Description
By finelybook
Understand the constructs of the Python programming language and use them to build data science projects
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
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 Model – Pipelines and Experiments
15 Packaging and Testing with Poetry and PyTest
16 Data Pipelines with Luigi
17 Let’s Build a Dashboard
18 Serving Models with a RESTful API
19 Serverless API Using Chalice
20 Best Practices and Python Performance
