6. All Learning On Your Own Opportunities¶

6.1. From Chapter 1 - Introduction to Data Science ¶

File Explorers and Shell Commands
Numerical Analysis

6.2. From Chapter 2 - Mathematical Foundations ¶

(None)

6.3. From Chapter 3 - Jupyter ¶

Problems with Notebooks
Math in Notebooks

6.4. From Chapter 4 - Review of Python and pandas ¶

Basic pandas work in Excel

6.5. From Chapter 5 - Before and After ¶

Technical Writing Tips

6.6. From Chapter 6 - Single-Table Verbs ¶

Mito
xlwings

6.7. From Chapter 7 - Abstraction ¶

Writing Python modules
Jupyter %run magic

6.8. From Chapter 8 - Version Control ¶

VS Code’s git features
Deepnote’s git features

6.9. From Chapter 9 - Mathematics and Statistics in Python ¶

Pingouin

6.10. From Chapter 10 - Visualization ¶

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

6.11. From Chapter 11 - Processing the Rows of a DataFrame ¶

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

6.12. From Chapter 12 - Concatenating and Merging DataFrames ¶

(None)

6.13. From Chapter 13 - Miscellaneous Munging Methods (ETL)¶

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

6.14. From Chapter 14 - Dashboards ¶

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

6.15. From Chapter 15 - Relations as Graphs - Network Analysis ¶

Centrality Measures
Gephi
Cytoscape

6.16. From Chapter 16 - Relations as Matrices ¶

(None)

6.17. From Chapter 17 - Introduction to Machine Learning ¶

(None)

5. GB213 Review in Python 7. All Big Picture Concepts