What is datasciencepython?

ujjwalkarn/datasciencepython — explained in plain English

Analysis updated 2026-06-26

★ 5,767PythonAudience · dataComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((datasciencepython))
    What it does
      Curated link collection
      Data science roadmap
      Learning resource index
    Topics Covered
      Python basics
      Pandas and NumPy
      Machine learning
      NLP and neural nets
    Libraries Referenced
      scikit-learn
      Matplotlib Seaborn
      SciPy
    Use Cases
      Self-study roadmap
      Beginner onboarding
      Library quick lookup
    Audience
      Data scientists
      Python beginners
      ML learners

mindmap root((datasciencepython)) What it does Curated link collection Data science roadmap Learning resource index Topics Covered Python basics Pandas and NumPy Machine learning NLP and neural nets Libraries Referenced scikit-learn Matplotlib Seaborn SciPy Use Cases Self-study roadmap Beginner onboarding Library quick lookup Audience Data scientists Python beginners ML learners

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What do people build with it?

USE CASE 1

Use the structured topic list as a self-study roadmap, working from Python fundamentals through to building machine learning models.

USE CASE 2

Find curated tutorial links for a specific data science library like Pandas, NumPy, or Matplotlib without searching Google.

USE CASE 3

Share the repository as an onboarding resource with someone new to data science who needs a clear starting point.

What is it built with?

PythonPandasNumPyscikit-learnMatplotlib

How does it compare?

	ujjwalkarn/datasciencepython	hect0x7/jmcomic-crawler-python	aidlearning/aidlearning-framework
Stars	5,767	5,764	5,773
Language	Python	Python	Python
Setup difficulty	easy	easy	moderate
Complexity	1/5	2/5	3/5
Audience	data	developer	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

So what is it?

This repository is a curated collection of links to tutorials, articles, courses, and reference materials for learning data science and machine learning using Python. There is no runnable code in the repository itself. It is an organized reading list pointing outward to other resources on the web. The list is organized by topic. It starts with Python language fundamentals, pointing to beginner guides, style references, and common questions from Stack Overflow. It then moves into data science topics, covering libraries used frequently in that field: Pandas for working with tables of data, NumPy and SciPy for numerical calculations, Matplotlib and Seaborn for making charts, and scikit-learn for building machine learning models. There are sections on natural language processing (teaching computers to work with text), neural networks, web scraping (pulling data from websites automatically), and SQL databases. Each section is a list of links, usually with short descriptions, pointing to blog posts, video series, Jupyter notebooks, and online courses from places like MIT, Coursera, and DataCamp. The repository also links to a companion list for people who prefer to do similar work in the R programming language, and to a broader machine learning tutorials list maintained by the same author. Someone completely new to data science could use this repository as a map, working through the linked materials roughly in order, from the Python language basics through to building and evaluating machine learning models. The README is the entire content of the repository. There are no scripts, notebooks, or packages included here, just a structured index of external learning material.

Copy-paste prompts

Prompt 1

I am following the datasciencepython reading list and finished the Python basics section. Give me a hands-on Pandas exercise: load a CSV, filter rows, group by a column, and compute a summary statistic.

Prompt 2

Based on the topics in the datasciencepython list, create a 4-week study plan for someone learning Python for data science who can study 1 hour per day.

Prompt 3

I am at the scikit-learn section of the datasciencepython list. Walk me through training a logistic regression classifier on the iris dataset and evaluating it with a confusion matrix.

Prompt 4

Summarize the natural language processing resources in the datasciencepython list and suggest a practical first NLP project I can build after studying them.

Frequently asked questions