https://github.com/andrewstuart/data-science-from-scratch
From Joel Grus' book
https://github.com/andrewstuart/data-science-from-scratch
Last synced: 10 months ago
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From Joel Grus' book
- Host: GitHub
- URL: https://github.com/andrewstuart/data-science-from-scratch
- Owner: andrewstuart
- License: unlicense
- Created: 2015-08-29T01:20:02.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2015-08-29T01:21:16.000Z (almost 11 years ago)
- Last Synced: 2025-06-30T23:36:28.898Z (12 months ago)
- Language: Python
- Size: 273 KB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Data Science from Scratch
=========================
Here's all the code and examples from my book __[Data Science from Scratch](http://joelgrus.com/2015/04/26/data-science-from-scratch-first-principles-with-python/)__.
Each can be imported as a module, for example (after you cd into the /code directory):
```python
from linear_algebra import distance, vector_mean
v = [1, 2, 3]
w = [4, 5, 6]
print distance(v, w)
print vector_mean([v, w])
```
Or can be run from the command line to get a demo of what it does (and to execute the examples from the book):
```bat
python recommender_systems.py
```
Additionally, I've collected all the [links](https://github.com/joelgrus/data-science-from-scratch/blob/master/links.md) from the book.
## Table of Contents
1. Introduction
2. A Crash Course in Python
3. [Visualizing Data](code/visualizing_data.py)
4. [Linear Algebra](code/linear_algebra.py)
5. [Statistics](code/statistics.py)
6. [Probability](code/probability.py)
7. [Hypothesis and Inference](code/hypothesis_and_inference.py)
8. [Gradient Descent](code/gradient_descent.py)
9. [Getting Data](code/getting_data.py)
10. [Working With Data](code/working_with_data.py)
11. [Machine Learning](code/machine_learning.py)
12. [k-Nearest Neighbors](code/nearest_neighbors.py)
13. [Naive Bayes](code/naive_bayes.py)
14. [Simple Linear Regression](code/simple_linear_regression.py)
15. [Multiple Regression](code/multiple_regression.py)
16. [Logistic Regression](code/logistic_regression.py)
17. [Decision Trees](code/decision_trees.py)
18. [Neural Networks](code/neural_networks.py)
19. [Clustering](code/clustering.py)
20. [Natural Language Processing](code/natural_language_processing.py)
21. [Network Analysis](code/network_analysis.py)
22. [Recommender Systems](code/recommender_systems.py)
23. [Databases and SQL](code/databases.py)
24. [MapReduce](code/mapreduce.py)
25. Go Forth And Do Data Science