- This is a list of resources that I like or want to check out (in other words, I have too many tabs open in my browser and need to close them + I want to keep track of stuff I like).
- We will see what topics emerge as resources are collected, but any topic of interest to me could be included.
- https://www.hackerrank.com/
- https://www.codewars.com/
- http://codingforinterviews.com/
- https://www.interviewcake.com/
- https://projecteuler.net/
- https://leetcode.com/
- https://modeanalytics.com/
- https://speakerdeck.com/jakevdp/statistics-for-hackers
- http://allendowney.blogspot.com/2016/06/there-is-still-only-one-test.html
- https://www.openintro.org/stat/textbook.php?stat_book=os
- http://blog.efpsa.org/2015/08/03/bayesian-statistics-why-and-how/
- http://web.stanford.edu/~hastie/CASI/contents.html
- https://github.com/open-source-society/data-science
- http://www.wsj.com/articles/what-data-scientists-do-all-day-at-work-1457921541
- http://varianceexplained.org/r/year_data_scientist/
- https://www.jstatsoft.org/article/view/v059i10 (tidy data)
- https://data-8.appspot.com/sp16/course
- http://blog.kaggle.com/2016/09/13/what-were-reading-data-science-resources/
- http://www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html
- http://www.edvancer.in/87-effective-data-analytics-interview-questions/
- http://blogs.gartner.com/martin-kihn/machine-learning-for-marketing-cheat-sheet/
- https://github.com/justmarkham/DAT8/blob/master/other/model_comparison.md
- https://datafloq.com/read/12-algorithms-every-data-scientist-should-know/2024
- https://hackerlists.com/free-machine-learning-books/
- https://pythonprogramming.net/machine-learning-tutorial-python-introduction/
- http://statweb.stanford.edu/~tibs/ElemStatLearn/
- http://www.fatml.org
- http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/
- http://scikit-learn.org/stable/tutorial/machine_learning_map/
- https://arogozhnikov.github.io/2016/06/24/gradient_boosting_explained.html
- https://www.youtube.com/watch?v=aA3qdegi8Vw (A Quick Tour of Machine Learning and Statistical Tools)
- http://mlpy.sourceforge.net/
- https://www.tensorflow.org/versions/r0.8/tutorials/word2vec/index.html
- https://www.tensorflow.org/
- http://deeplearning.net/software/theano/
- http://keras.io/
- https://spacy.io/
- https://www.quora.com/What-are-the-advantages-of-Spacy-vs-NLTK
- https://en.wikipedia.org/wiki/Latent_semantic_analysis
- http://www.sciencedirect.com/science/article/pii/S1877050915017895
- https://www.quora.com/Is-there-any-free-project-on-big-data-and-Hadoop-which-I-can-download-and-do-practice
- https://lagunita.stanford.edu/courses/DB/SQL/SelfPaced/about
- http://postgresguide.com/tips/window.html
- https://community.modeanalytics.com/sql/tutorial/introduction-to-sql/
- https://en.wikipedia.org/wiki/Agile_software_development
- https://www.owasp.org/index.php/OWASP_Proactive_Controls#tab=Main
- https://www.owasp.org/index.php/OWASP_Proactive_Controls?refresh=123#tab=OWASP_Proactive_Controls_2016
- http://columbia-applied-data-science.github.io/pages/lowclass-python-style-guide.html
- http://treycausey.com/software_dev_skills.html
- https://www.kevinlondon.com/2015/05/05/code-review-best-practices.html
- https://jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing/
- https://semaphoreci.com/community/tutorials/testing-python-applications-with-pytest
- http://engineering.pivotal.io/post/test-driven-development-for-data-science/
- https://blog.dominodatalab.com/unit-testing-data-science/
- https://docs.python.org/3/library/stdtypes.html#types-set
- https://wiki.python.org/moin/PythonDecorators
- https://github.com/audreyr/cookiecutter
- http://www.dabeaz.com/python/UnderstandingGIL.pdf
- http://holoviews.org/
- http://geo.holoviews.org/
- http://dask.pydata.org/en/latest/
- http://dask.pydata.org/en/latest/spark.html
- http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3
- https://github.com/cesium-ml/cesium/blob/master/doc/examples/EEG_Example.md
- https://github.com/LLNL/conduit
- https://github.com/jupyter/nbdime
- https://github.com/mbmilligan/scipy2016-jupyterhub/
- https://jupyterhub.readthedocs.io/en/latest/
- https://demo.use.yt/user/8ZMzRjIhRy7X/tree
- http://www.vtk.org/
- https://github.com/wlattner/SciPy_2016
- https://hackpad.com/SciPy-Diversity-Reading-List-blTUBb2u8xO
- https://www.youtube.com/watch?v=0xJofTfAHWw
- https://www.youtube.com/watch?v=iMi60eBIOkg
- https://www.youtube.com/watch?v=3TpcSPN8u-U
- https://www.coursera.org/learn/progfun1
- https://www.coursera.org/learn/hadoop
- https://www.coursera.org/learn/predictive-analytics/
- https://www.coursera.org/learn/data-results/
- https://www.edx.org/course/introduction-apache-spark-uc-berkeleyx-cs105x
- http://idibon.com/
- https://www.qordoba.com/
- http://wayblazer.com/
- https://www.clarifai.com/
- http://torch.ch/
- https://en.wikipedia.org/wiki/Retainer_agreement
- https://creativeclass.io/retainers-how-to/
- https://www.docracy.com/9jsoij4itk/retainer-agreement
- https://blog.bidsketch.com/sales/freelance-retainer-agreement/
- http://business.tutsplus.com/articles/offering-services-on-retainer--fsw-33797
- https://www.certifiedanalytics.org/
- http://www.cisco.com/c/en/us/training-events/training-certifications/overview.html
- https://www.microsoft.com/en-us/learning/sql-training.aspx
- http://sl8r000.github.io/ab_testing_statistics/
- https://code.facebook.com/posts/181565595577955/introducing-deeptext-facebook-s-text-understanding-engine/?ref=producthunt
- https://github.com/rasbt/watermark
- https://en.wikipedia.org/wiki/Data_model
- https://en.wikipedia.org/wiki/Cyc
- http://betterexplained.com/articles/adept-method/
- https://en.wikipedia.org/wiki/Code_generation_(compiler)
- https://arrow.apache.org/
- https://en.wikipedia.org/wiki/K-d_tree
- https://en.wikipedia.org/wiki/Quadtree
- https://en.wikipedia.org/wiki/Octree
- https://en.wikipedia.org/wiki/Construct_validity
- Walach (Medium 2014)
- http://flexx.readthedocs.io/en/stable/
- https://github.com/binder-project/binder
- http://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/
- http://arxiv.org/pdf/1607.00376v1.pdf ("Men set their own cites high: Gender and self-citation across fields and over time")
- https://www.oreilly.com/ideas/practical-machine-learning-techniques-for-building-intelligent-applications