All Learning On Your Own Opportunities

From Chapter 1 - Introduction to Data Science

File Explorers and Shell Commands
Numerical Analysis

From Chapter 2 - Mathematical Foundations

(None)

From Chapter 3 - Jupyter

Problems with Notebooks
Math in Notebooks

From Chapter 4 - Review of Python and pandas

Basic pandas work in Excel

From Chapter 5 - Before and After

Technical Writing Tips

From Chapter 6 - Single-Table Verbs

Mito
xlwings

From Chapter 7 - Abstraction

Writing Python modules
Jupyter %run magic

From Chapter 8 - Version Control

VS Code's git features
Deepnote's git features

From Chapter 9 - Mathematics and Statistics in Python

Pingouin

From Chapter 10 - Visualization

Visual EDA Tools
SandDance
Plot with Less Code
Geographical Plots
Tableau
Charticulator
Visualization Design Principles

From Chapter 11 - Processing the Rows of a DataFrame

CuPy (fastest option)
NumExpr (easiest option)
Cython (most flexible)

From Chapter 12 - Concatenating and Merging DataFrames

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From Chapter 13 - Miscellaneous Munging Methods (ETL)

SQL in Jupyter
SQLite in Python
College Football Data Python API
NBA Data Processing Tutorials

From Chapter 14 - Dashboards

Alternative to Streamlit: Dash
Alternative to Streamlit: Voilà
Alternative to Streamlit: Gradio
Alternative to Streamlit: Deepnote Interactive Blocks

From Chapter 15 - Relations as Graphs - Network Analysis

Centrality Measures
Gephi
Cytoscape

From Chapter 16 - Relations as Matrices

(None)

From Chapter 17 - Introduction to Machine Learning

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