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Official content for Harvard CS109
https://github.com/cs109/content

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Official content for Harvard CS109

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README

        

Welcome to CS109: Data Science
=======

## Assignments

* [Homework 0](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW0.ipynb): Hello, world ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW0_solutions.ipynb))
* [Homework 1](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW1.ipynb): Which of two things is larger? ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW1_solutions.ipynb))
* [Homework 2](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW2.ipynb): Desperately Seeking Silver ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW2_solutions.ipynb))
* [Homework 3](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW3.ipynb): Bayesian Tomatoes ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW3_solutions.ipynb))
* [Homework 4](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW4.ipynb): Do We Really Need Chocolate Recommendations? ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW4_solutions.ipynb))
* [Homework 5](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW5.ipynb): Networks and Congress ([solutions](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/HW5_solutions.ipynb))

## Lecture Supplements

* [A gallery of statistical graphs with matplotlib](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/lec_03_statistical_graphs.ipynb) (see also the version with [default matplotlib styles](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/lec_03_statistical_graphs_mpl_default.ipynb))
* [A rubric for data wrangling and exploratory data analysis](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/lec_04_wrangling.ipynb)
* [Web Scraping and Parsing Demo](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/lec_04_scraping.ipynb)
* [Cross Validation: The Right and Wrong Way](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/lec_10_cross_val.ipynb)

## Labs

* [Lab 2: Web Scraping](https://github.com/cs109/content/tree/master/labs/lab2)
* [Lab 3: EDA, Pandas, Matplotlib](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab3/lab3full.ipynb)
* [Lab 4: Scikit-Learn, Regression, PCA](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab4/Lab4full.ipynb)
* [Lab 5: Bias, Variance, Cross-Validation](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab5/Lab5.ipynb)
* [Lab 6: Bayes, Linear Regression, and Metropolis Sampling](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab6/BayesLinear.ipynb)
* [Lab 7: Gibbs Sampling](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab7/GibbsSampler.ipynb)
* [Lab 8: MapReduce](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab8/lab8_mapreduce.ipynb)
* [Lab 9: Networks](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab9/lab_9.ipynb)
* [Lab 10: Support Vector Machines](http://nbviewer.ipython.org/urls/raw.github.com/cs109/content/master/labs/lab10/Lab_10.ipynb)

## Other Resources

* [Setting up Python](https://github.com/cs109/content/wiki/Installing-Python)