Resource Focus
Programming & environments
Return to Python syntax, packages, virtual environments, Jupyter, Git and reproducible project setup.
Mathematics
Use algebra, linear algebra, calculus, probability and statistics chapters as the mathematical reference layer for later AI work.
Data work
Revisit data collection, privacy, cleaning, transformations, exploratory analysis and visualisation.
Official documentation
Use first-party Python, NumPy, pandas, Matplotlib, Jupyter and Git documentation alongside the NIYAMA-VIDYĀ explanations.
Chapter Resource Map
Every chapter in this Module already contains its own Key Terms and References & Further Reading collections. These links open the relevant chapter and automatically expand the requested resource section.
Computing, Python & Data Work
Chapter 1 — Computing and Problem-Solving Foundations
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 2 — Python Programming Fundamentals
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 3 — Python for Data Work
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Mathematics & Statistics
Chapter 4 — Algebra and Functions
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 5 — Linear Algebra
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 6 — Calculus and Optimisation
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 7 — Probability Foundations
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 8 — Statistics Foundations
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Data Quality, EDA & Reproducibility
Chapter 9 — Data Collection and Data Quality
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 10 — Data Cleaning and Transformation
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 11 — Exploratory Data Analysis and Visualisation
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Chapter 12 — Development Tools, Reproducibility and Foundation Project
Use the chapter resource layer to revise terminology or open the source list used for deeper verification and study.
Curated External Reference Shelf
This shelf surfaces a small set of high-value references already used by the Module. The complete chapter-specific source lists remain inside the individual chapters.
| Resource | Why it is useful | Used in |
|---|---|---|
| Python Documentation | Language and standard-library reference | Chapters 1–3, 12 |
| Python Packaging User Guide | pip, virtual environments and dependency practice | Chapters 2, 12 |
| NumPy User Guide | Array computing and numerical foundations | Chapters 3, 5 |
| pandas User Guide | Tabular data manipulation, missing data and transformation | Chapters 3, 10–11 |
| Matplotlib Documentation | Data visualisation reference | Chapter 11 |
| Project Jupyter Documentation | Notebook and interactive-computing environment | Chapter 12 |
| Git Documentation | Version-control command and concept reference | Chapters 1, 12 |
| NIST/SEMATECH Engineering Statistics Handbook | Probability, statistics and exploratory-analysis reference | Chapters 4–11 |
| OpenStax — Algebra and Trigonometry | Open mathematics reference for algebraic foundations | Chapter 4 |
Continue the Learning Journey
Return to Technical Foundations
Continue the full 12-Chapter learning sequence rather than using the resource directory as a substitute for the lessons.
Open the Module →Next: Machine Learning
Continue into the next logical learning area when you are ready.
Continue Learning →NIYAMA-VIDYĀ Resources
Return to the permanent site-wide directory for Learn, Explore, Applied AI and transparency resources.
Back to Resources →Reference Transparency
External documentation and publications remain the property of their respective owners and are provided for attribution, verification and further learning. See Third-Party Rights & Attribution and the Website Disclaimer for the site-wide transparency framework.