orakle / My-Data-Science-Resources

My Data Science Resources

This is a list of resources I found useful on my learning path. This a living list and is continuosly updated with new material.

Collection of IPython Notebooks and Examples:

• Projects:
  • ANALYZING A NHL PLAYOFF GAME WITH TWITTER: http://www.danielforsyth.me/analyzing-a-nhl-playoff-game-with-twitter/
  • NBA TWITTER, EMOJIS, AND WORD EMBEDDINGS: http://www.danielforsyth.me/nba-twitter-emojis-and-word-embeddings/
• Clean Up / Data Munging:
  • New York Noise Analysis: http://nbviewer.ipython.org/github/jvns/talks/blob/master/pydatanyc2013/PyData%20NYC%202013%20tutorial.ipynb
  • Cleaning up data: http://nbviewer.ipython.org/github/ResearchComputing/Meetup-Fall-2013/blob/master/python/lecture_21_pandas_processing.ipynb
  • GroupBy: http://wesmckinney.com/blog/?p=125
• Regression:
  • Linear regression and curve fitting: http://nbviewer.ipython.org/github/mbakker7/exploratory_computing_with_python/blob/master/notebook_s4/py_exp_comp_s4_sol.ipynb
  • 2D: http://nbviewer.ipython.org/github/temporaer/tutorial_ml_gkbionics/blob/master/3a%20-%20Linear%20regression%201D.ipynb
  • 3D: http://nbviewer.ipython.org/github/temporaer/tutorial_ml_gkbionics/blob/master/3b%20-%20Linear%20regression%202D.ipynb
  • Linear Regression Models with Python: http://mpastell.com/2013/04/19/python_regression/
  • Logistic Regression: http://nbviewer.ipython.org/github/justmarkham/gadsdc1/blob/master/logistic_assignment/kevin_logistic_sklearn.ipynb
  • Logistic Regression ML: http://jvns.ca/blog/2014/11/17/fun-with-machine-learning-logistic-regression/
• Clustering:
  • Kmeans: http://nbviewer.ipython.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb
  • Introduction to Machine Learning: Clustering and Regression: http://nbviewer.ipython.org/github/amplab/datascience-sp14/blob/master/hw2/HW2.ipynb
  • Diluting Whiskey Data With Python Pandas: http://www.bearrelroll.com/2014/01/scottish-whiskey-and-python-k-means-clustering/
  • k Nearest Neighbors: http://nbviewer.ipython.org/github/temporaer/tutorial_ml_gkbionics/blob/master/5%20-%20k%20Nearest%20Neighbors.ipynb
• Visualization:
  • Heatmap: http://nbviewer.ipython.org/gist/louisryan/1a014b9c5d63fbdfc9cc
  • Heatmap StackOverflow: http://stackoverflow.com/questions/20754072/is-there-a-tutorial-for-creating-a-hexbin-heat-map-using-matplotlib
  • Financial Analysis w/ Heatmap: http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb
  • Exploratory Graphs: http://nbviewer.ipython.org/github/herrfz/dataanalysis/blob/master/week3/exploratory_graphs.ipynb
  • Making a map with Python: http://sensitivecities.com/so-youd-like-to-make-a-map-using-python-EN.html#.VGmQIHIn3Qo
  • Normal Distribution: http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb
• Other Collections:
  • Full Stack Data Analysis: http://nbviewer.ipython.org/github/jackgolding/FullStackDataAnalysis/tree/master/
  • Starting with Data Science and Python: http://twiecki.github.io/blog/2014/11/18/python-for-data-science/
  • Learn Data Science: http://nborwankar.github.io/LearnDataScience/
  • Python for Exploratory Computing: http://mbakker7.github.io/exploratory_computing_with_python/
  • Mapping:
  • Mapping Data with Python: https://wrobstory.github.io/2013/10/mapping-data-python.html

Books:

• 16 Free Data Science Books: http://www.wzchen.com/data-science-books
• Python Cookbook: http://chimera.labs.oreilly.com/books/1230000000393
• Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/
• Practical Data Science with R: http://www.amazon.com/Practical-Data-Science-Nina-Zumel/dp/1617291560
• A Programmer's Guide to Data Mining: http://guidetodatamining.com/
• Doing Bayesian Data Analysis: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/
• Think Stats: Probability and Statistics for Programmers: http://www.greenteapress.com/thinkstats/html/index.html
• Probabilistic Programming & Bayesian Methods for Hackers: https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
• Frequentism and Bayesianism: A Practical Introduction: http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/

Pandas:

• Sklearn: https://github.com/jseabold/depy/blob/master/pandas_sklearn_rendered.ipynb
• Pandas Basics: http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/things_in_pandas.ipynb
• Useful Pandas Snippets: http://www.swegler.com/becky/blog/2014/08/06/useful-pandas-snippets/
• Sentiment Analysis: http://blog.kimonolabs.com/2014/12/17/guest-blog-sentiment-analysis-on-web-scraped-data-with-kimono-and-monkeylearn/

Statistics:

• Introduction to Statistics with Python: http://work.thaslwanter.at/Stats/html/index.html
• The Little Handbook of Statistics: http://www.jerrydallal.com/LHSP/LHSP.htm
• Understanding Variance, Co-Variance, and Correlation: http://www.countbayesie.com/blog/2015/2/21/variance-co-variance-and-correlation
• Online Stat Book: http://onlinestatbook.com/index.html
• Philosophy of Statistics: http://plato.stanford.edu/entries/statistics/
• Stanford Statistical Learning: http://online.stanford.edu/course/statistical-learning
• Probability Course: http://www.probabilitycourse.com/
• Better Explained: http://betterexplained.com/articles/a-brief-introduction-to-probability-statistics/
• 10 FREE Resources to Learn Statistics: http://www.marketingdistillery.com/2014/09/06/10-free-resources-to-learn-statistics/

Linear Algebra:

• Linear Algebra Resources: http://www.itshared.org/2015/02/best-time-to-learn-linear-algebra-is-now.html

Datasets:

• New York Times: The Upshot: https://github.com/TheUpshot
• Stanford Datasets: http://snap.stanford.edu/data/index.html
• Chicago Crime (2001 to Present): https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2
• Five Thirty Eught: https://github.com/fivethirtyeight/data

Python:

• Computational Statistics in Python: http://people.duke.edu/~ccc14/sta-663/index.html
• Python 2 vs Python 3: http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb
• Useful Regual Expressions: http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/useful_regex.ipynb
• Naive Bayes: http://blog.yhathq.com/posts/naive-bayes-in-python.html
• Zen of Python Examples: http://stackoverflow.com/questions/228181/the-zen-of-python
• A Python course for the Humanities (great collection of IPython Notebooks): http://fbkarsdorp.github.io/python-course/ http://nbviewer.ipython.org/github/fbkarsdorp/python-course/tree/master/
• Problem Solving with Algorithms and Data Structures: http://interactivepython.org/courselib/static/pythonds/index.html
• Regular Expressions Tutorial: http://www.python-course.eu/re.php

R:

• Quick-R: http://www.statmethods.net/
• Communicating Results with R: http://rikturr.com/blog/communicating-experimental-results-with-r/
• R Tutorial with Bayesian Statistics: http://www.r-tutor.com/
• Introduction to R from Tiny Data: http://ramnathv.github.io/pycon2014-r/explore/tidy.html
• UCLA R Resources: http://statistics.ats.ucla.edu/stat/r/
• An Introduction to Statistical Learning with Applications in R: http://www-bcf.usc.edu/~gareth/ISL/getbook.html
• R Language for Programmers: http://www.johndcook.com/blog/r_language_for_programmers/

Unix:

• Data Analysis and Visualization using Command Line: http://blog.whatfettle.com/2014/10/13/one-csv-thirty-stories/
• Useful Unix commands for exploring data: http://datavu.blogspot.com/2014/08/useful-unix-commands-for-exploring-data.html

JavaScript:

• Eloquent JavaScript: http://eloquentjavascript.net/

Visualization:

• D3 Tutorial: https://www.dashingd3js.com/
• Matplotlib: http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb
• Intro to D3.js http://square.github.io/intro-to-d3/web-standards/
• Interactive Data Visualization with D3.js, DC.js, Python, and MongoDB: http://adilmoujahid.com/posts/2015/01/interactive-data-visualization-d3-dc-python-mongodb/
• Patterns for Information Visualization: http://www.targetprocess.com/articles/information-visualization/

Advanced Machine Learning:

• Advaced MAchine Learning with Python: https://github.com/ogrisel/parallel_ml_tutorial

Algorithms:

• Top 10 Data Mining Algorithms in Plain English: http://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/

List of Resources by Others:

• Data Science Ontology: http://www.datascienceontology.com/
• Collection of Data Science Resources: https://github.com/jonathan-bower/DataScienceResources
• A gallery of interesting IPython Notebooks: https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks
• The Open Source Data Science Masters: http://datasciencemasters.org/
• A Practical Intro to Data Science: http://diggdata.in/post/50410269207/a-practical-intro-to-data-science
• Materials for Learning Machine Learning: http://www.jacksimpson.co/2015/06/07/materials-for-learning-machine-learning/
• Statistical Data Minung Tutorials: http://www.autonlab.org/tutorials/list.html

Other

• How to Become a Data Scientist Inforgraphic: http://blog.datacamp.com/wp-content/uploads/2014/08/How-to-become-a-data-scientist.jpg
• What is code?: http://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/