mrmimic/data-scientist-roadmap

★ 7,353Jupyter NotebookAudience · dataComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((data-scientist-roadmap))
    What it does
      Practical tutorials
      Roadmap coverage
      Community contributions
    Tech Stack
      Python
      Jupyter Notebook
      Poetry
    Topics Covered
      Statistics
      Machine learning
      Domain knowledge
    Audience
      Self-learners
      Beginners
      Career changers
    Setup
      Install Poetry
      Run notebooks locally

mindmap root((data-scientist-roadmap)) What it does Practical tutorials Roadmap coverage Community contributions Tech Stack Python Jupyter Notebook Poetry Topics Covered Statistics Machine learning Domain knowledge Audience Self-learners Beginners Career changers Setup Install Poetry Run notebooks locally

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Things people build with this

USE CASE 1

Work through hands-on coding tutorials structured around every topic on the Swami Chandrasekaran data science roadmap.

USE CASE 2

Fork the repo and add your own notebooks to contribute practical examples missing from a topic area.

USE CASE 3

Use the curated README links to find external resources for any skill on the roadmap you want to study more deeply.

Tech stack

PythonJupyter NotebookPoetry

Getting it running

Difficulty · easy Time to first run · 30min

Requires Python and Poetry, run the install command and all libraries load automatically.

In plain English

This repository pairs with a well-known data science skills roadmap image originally drawn by Swami Chandrasekaran on his personal blog. The image charts the broad territory a person needs to cover to work as a data scientist, from statistics and programming to machine learning and domain knowledge. The goal of this project is to fill in that chart with practical tutorials, giving learners a path through the subjects the diagram lays out. The tutorials are written as Jupyter Notebooks, which are documents that combine written explanations with runnable code in one file. Each main directory in the repository has its own README pointing to relevant resources and links. The contributing rules ask that all code be commented, that file naming stay consistent with the existing structure, and that useful outside links be added to README files alongside any notebooks. Running the examples requires Python and Poetry, a tool that manages package dependencies. After installing Poetry and running the install command, all the necessary libraries load automatically. The code examples are written by hand, though the README notes that some explanatory text was drawn from Wikipedia or generated by a language model. The project is open to contributions. Anyone can fork the repository, add tutorials, and submit a pull request. The README frames this as a community effort to flesh out a roadmap that already exists as a diagram but needs practical worked examples to be usable for self-study.

Copy-paste prompts

Prompt 1

Using the mrmimic/data-scientist-roadmap repo as a guide, write a Python Jupyter notebook that walks me through a beginner statistics tutorial with runnable examples for mean, median, variance, and standard deviation.

Prompt 2

Help me set up Poetry and install all dependencies for the mrmimic/data-scientist-roadmap repo so I can run the example notebooks locally.

Prompt 3

I want to contribute a new Jupyter Notebook tutorial to mrmimic/data-scientist-roadmap covering decision trees. Show me how to structure the notebook with commented code and a README entry following the repo conventions.

Prompt 4

Generate a study plan using the mrmimic/data-scientist-roadmap structure, listing which notebooks to work through in what order for someone who already knows Python but is new to machine learning.

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