# justmarkham/DAT8

General Assembly's 2015 Data Science course in Washington, DC

Repository: https://github.com/justmarkham/DAT8
Canonical: https://ross.abutalabs.com/products/dat8
Language: Jupyter Notebook
License Family: other
Topics: data-science, machine-learning, scikit-learn, data-analysis, pandas, jupyter-notebook, python, course, linear-regression, logistic-regression, model-evaluation, naive-bayes, natural-language-processing, decision-trees, ensemble-learning, clustering, regular-expressions, web-scraping, data-visualization, data-cleaning
Last push: 2024-06-05T12:04:22+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": 4044, "days_push": 819, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1620, forks 1053 (observed 2026-08-28T04:05:12.300099+00:00)

## What it is
Course materials for General Assembly's 2015 Data Science course in Washington, DC, taught by Kevin Markham. It contains Jupyter notebooks covering data cleaning, visualization, machine learning with scikit-learn, NLP, and model evaluation.

## Use cases
- learn data science with python and pandas
- intro to machine learning with scikit-learn
- learn model evaluation and cross-validation
- learn natural language processing basics
- practice data cleaning and exploratory data analysis
- self-study a full data science curriculum
- learn linear and logistic regression in python

## When to choose
- you want a structured, beginner-friendly data science course with runnable notebooks
- you learn well from classroom-style materials with exercises and projects
- you want free coverage of pandas, scikit-learn, and NLP fundamentals

## When to avoid
- you need up-to-date tooling or modern Python 3 practices (material dates to 2015 and uses Python 2)
- you want deep theoretical treatment rather than practical introductions
- you need actively maintained or supported course content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, nlp, data-visualization, web-scraping
- domain: data-science, machine-learning, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, scikit-learn, pandas, course-materials, general-assembly, kevin-markham, data-school, natural-language-processing

## Member repositories
- justmarkham/DAT8 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:12.300099+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 2026-08-30T03:49:18.834094+00:00, confidence not recorded.
  - readme: https://github.com/justmarkham/DAT8 (fetched 2026-08-28T04:05:12.300099+00:00, sha 723601cc1d58)
- Data as of 2026-08-30T08:39:29.467469+00:00.