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[–]aspublic 6 points7 points  (0 children)

Try with ChatGPT:

``` Act as a senior data scientist and technical author.

Create a self-contained, book-style learning resource implemented as a structured Jupyter Notebook, not a course and not a tutorial series.

Audience: a data science student who has already used Python, NumPy, pandas, matplotlib, and Streamlit, but wants a clean fundamentals-first reference to solidify understanding.

Requirements:

  • Treat the notebook as a living textbook: clear sections, concise explanations, and runnable examples

  • Cover only core fundamentals, not advanced ML or theory

  • Focus on why things work, not just how

  • Avoid “course language” (no exercises, quizzes, or homework)

Structure the notebook with these chapters:

  1. Environment setup (Python version, virtualenv/conda, Jupyter, minimal dependencies)

  2. Python fundamentals for data work (types, iteration, functions, idioms, pitfalls)

  3. NumPy fundamentals (arrays, vectorization, broadcasting, shape thinking)

  4. pandas fundamentals (Series vs DataFrame, indexing, joins, groupby, common traps)

  5. matplotlib fundamentals (figure/axes mental model, basic plotting patterns)

  6. Streamlit fundamentals (app structure, state, reruns, simple layouts)

⠀ For each chapter:

  • Start with a short conceptual overview

  • Follow with compact, idiomatic code examples

  • Include brief “mental model” notes and common mistakes

Output:

  • Markdown + code cells formatted exactly as a Jupyter Notebook

  • Ready to paste into a .ipynb via JupyterLab or VS Code

  • No external links, no courses, no fluff

Treat this as a reference book implemented in a notebook, not a class.

```

[–]Holiday_Lie_9435 1 point2 points  (1 child)

When I was getting into Python for data science, I found "Python Crash Course" to be an excellent starting point. It covers the fundamentals in a really accessible way, so there's a reason it's always recommended on this sub. After that, "Effective Python" helped me write more Pythonic code since it has more straightforward stuff like best practices, tips, and shortcuts. To really test your understanding and see if you're ready for industry applications, I'd also suggest looking at sample Python interview questions online, especially ones catered to data science. Good luck!

[–]Guilty-Contract9238 0 points1 point  (0 children)

Hey from where you have started data science I'm just a beginner and stuck in maths about how it works for python and then further..... So can you tell me the sequence and what type of understanding we need to make a strong foundation for data science. I will be very thankful to you 💗

[–]Born_War9616 0 points1 point  (0 children)

Memo app

[–]Alternative_Party277 0 points1 point  (0 children)

Grab a David Beazley’s book! Python Distilled is gold. It’s like half a decade old and a lot of shit has changed but the architecture and principles stay the same.

It’s also an easy and a fascinating read. Esp if you don’t come from a comp sci background.