Learn Python by Building Data Science Applications: A fun, project-based guide to learning Python 3 while building real-world apps
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.
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
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