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

Downloading materials for running the code

Installing Python

Working with VS Code

Beginning with Jupyter

First Steps in Coding - Variables and Data Types

Converting the data types

Assigning variables

Naming the variable

Understanding data types

Functions

Refactoring the temperature conversion

Understanding a function

Defining the function

Understanding anonymous (lambda) functions

Understanding recursion

Data Structures

What are data structures?

More data structures

Using generators

Useful functions to use with data structures

Loops and Other Compound Statements

Understanding if, else, and elif statements

Running code many times with loops

Handling exceptions with try/except and try/finally

Understanding the with statements

First Script – Geocoding with Web APIs

Working with the Nominatim API

Geocoding as a service

Learning about web APIs

Caching with decorators

Reading and writing data

Moving code to a separate module

Collecting NYC Open Data from the Socrata service

Scraping Data from the Web with Beautiful Soup 4

When there is no API

Scraping WWII battles

Beyond Beautiful Soup

Simulation with Classes and Inheritance

Using classes in simulation

Understanding classes

Shell, Git, Conda, and More – at Your Command

Section 2: Hands-On with Data

Python for Data Applications

Introducing Python for data science

Exploring NumPy

Beginning with pandas

Trying SciPy and scikit-learn

Understanding Jupyter

Data Cleaning and Manipulation

Getting started with pandas

Working with real data

Getting to know regular expressions

Parsing locations

Time

Belligerents

Understanding casualties

Data Exploration and Visualization

Declarative visualization with vega and altair

Exploring the dataset

Big data visualization with datashader

Training a Machine Learning Model

Understanding the basics of ML

Improving Your Model – Pipelines and Experiments

Understanding cross-validation

Exploring feature engineering

Optimizing the hyperparameters

Tracking your data and metrics with version control

Section 3: Moving to Production

Packaging and Testing with Poetry and PyTest

A few ways to build your package

Building a package

Testing the code so far

Automating the process with CI services

Generating documentation generation with sphinx

Installing a package in editable mode

Data Pipelines with Luigi

Introducing the ETL pipeline

Building our first task in Luigi

Understanding time-based tasks

Exploring the different output formats

Expanding Luigi with custom template classes

Let's Build a Dashboard

Building a dashboard – three types of dashboard

Understanding dynamic dashboards

Serving Models with a RESTful API

Building a basic API service

What is a RESTful API?

Building a web page

Speeding up with asynchronous calls

Deploying and testing your API loads with Locust

Serverless API Using Chalice

Getting started with Chalice

Understanding serverless

Setting up a simple model

Building a serverless API for an ML model

Building a serverless function as a data pipeline

Best Practices and Python Performance