# amitkaps/hackermath

Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way

Repository: https://github.com/amitkaps/hackermath
Canonical: https://ross.abutalabs.com/products/hackermath
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, linear-algebra, statistics, calculus, python
Last push: 2017-11-26T06:23:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3754, "days_push": 3202, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1452, forks 280 (observed 2026-08-28T04:04:46.466827+00:00)

## What it is
A workshop-style collection of Jupyter notebooks teaching the math behind data science (statistics, linear algebra, calculus) through hands-on Python coding. It covers hypothesis testing, supervised learning, and unsupervised learning with a code-first 'hacker's way' approach.

## Use cases
- learn statistics for data science
- brush up on linear algebra for machine learning
- understand the math behind regression and PCA
- learn hypothesis testing with Python code
- prepare for a data science career as a programmer
- teach a data science math workshop

## When to choose
- you learn best by writing code rather than reading proofs
- you're a programmer transitioning into data science
- you want a practical, application-driven intro to ML math
- you want runnable notebooks you can explore in Binder

## When to avoid
- you need a rigorous, proof-based mathematics textbook
- you want comprehensive coverage of modern deep learning
- you need actively maintained course content - the material dates from 2017
- you're an absolute beginner with no programming background

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, math
- domain: machine-learning, data-science, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, statistics, linear-algebra, workshop, hypothesis-testing

## Member repositories
- amitkaps/hackermath (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:46.466827+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:35:47.305015+00:00, confidence not recorded.
  - readme: https://github.com/amitkaps/hackermath (fetched 2026-08-28T04:04:46.466827+00:00, sha 4f55babb25dc)
- Data as of 2026-08-30T08:39:29.467469+00:00.