ABCDEFGHIJKLMNOPQRSTUVWXYZ
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Computer Science/Programming
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Stanford introduction to CShttp://see.stanford.edu/see/materials/icspacs106b/assignments.aspx http://web.stanford.edu/class/cs106a/faq.shtml
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Harvard CS205 foundations for computational sciencehttp://iacs-courses.seas.harvard.edu/courses/cs205/syllabus.html
38 lectures,5 HW assignments, over 15 weeks
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Learn R in a dayhttp://www.amazon.co.uk/gp/product/B00GC2LKOK/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=B00GC2LKOK&linkCode=as2&tag=datsciwee-20&linkId=IVRNKKRTXRFRI63V
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The little schemer (book)http://www.amazon.co.uk/The-Little-Schemer-Daniel-Friedman/dp/0262560992
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The Structure and Implementation of Computer Programmes (Book)http://www.amazon.co.uk/Structure-Interpretation-Computer-Electrical-Engineering/dp/0262510871/ref=wl_mb_wl_huc_mrai_2_dp
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Seven languages in seven weeks (Book)https://geneticmail.com/scott/library/text/seven-languages-in-seven-weeks_p1_0.pdf
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hackerrankhttps://www.hackerrank.com/domains
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Stanford CS 106 A (Java)http://web.stanford.edu/class/cs106a/
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Problem solving with algorithms and data structureshttp://interactivepython.org/runestone/static/pythonds/index.html
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command line crash coursehttp://cli.learncodethehardway.org/book/
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websites in Jekyll
http://www.smashingmagazine.com/2014/08/01/build-blog-jekyll-github-pages/ ; http://jekyllbootstrap.com/lessons/jekyll-introduction.html ; http://jekyllbootstrap.com/usage/jekyll-quick-start.html; https://www.andrewmunsell.com/tutorials/jekyll-by-example/tutorial ; http://hyde.getpoole.com/
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100 numpy exerciseshttp://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html
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Scientific python lectures (in iPython NB format)https://github.com/jrjohansson/scientific-python-lectures
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R programming free e-book (to accompany John Hopkins Coursera course)https://leanpub.com/rprogramming
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Plotly intro to numpyhttps://plot.ly/numpy/
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Pandas cheatsheets (Enthought)https://www.enthought.com/services/training/pandas-mastery-workshop/#pandas-cheat-sheet-download
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nand2tetris (build a computer)http://www.nand2tetris.org/
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Understanding computation: From simple machines to impossible programs (ruby)http://computationbook.com/
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Visualisation
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Harvard CS 171http://www.cs171.org/2015/index.html
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Math/Stats
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Why learn linear algebra?http://machinelearningmastery.com/linear-algebra-machine-learning/
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Udacity introduction to statshttps://www.udacity.com/course/viewer#!/c-st0951 month (6hrs/day)Calculus
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Harvard 110: Introduction to probabilityhttp://isites.harvard.edu/icb/icb.do?keyword=k104821&pageid=icb.page676263Graph theory
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MIT: Probabilistic Systems Analysis and Applied Probabilityhttp://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/12h/week/16 weeks
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as abovehttps://www.edx.org/course/introduction-probability-science-mitx-6-041x-0#.VSAlA_nF_SE
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MIT: Multivariable calculus http://ocw.mit.edu/courses/mathematics/18-02-multivariable-calculus-fall-2007/35 lectures
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Khan academy: Linear algebrahttps://www.khanacademy.org/math/linear-algebra
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MIT open courseware: statistical thinking and data analysishttp://ocw.mit.edu/courses/sloan-school-of-management/15-075j-statistical-thinking-and-data-analysis-fall-2011/index.htm
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sliderule
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Bayesian inference for hackershttp://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb
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Think stats oreilly [Python heavy]http://www.greenteapress.com/thinkstats/
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Think bayes oreillyhttp://www.greenteapress.com/thinkbayes/
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Simple stats with Scipy https://oneau.wordpress.com/2011/02/28/simple-statistics-with-scipy/
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All of statisticshttp://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/0387402721
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ipython notebooks for linear regression, logistic regression, random forests, k means
http://nborwankar.github.io/LearnDataScience/
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linear regression in Pythonhttp://www.dataschool.io/linear-regression-in-python/
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statstics in a nutshellhttp://www.amazon.co.uk/Statistics-Nutshell-Desktop-Reference-OReilly/dp/0596510497
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probability cheat sheethttps://github.com/wzchen/probability_cheatsheet
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Linear algebra (MIT open courseware, Gilbert Strang)http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
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Penn State Intro to Probability (good intro notes)https://onlinecourses.science.psu.edu/stat414/node/287
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Implementing a neural network from scratchhttp://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/
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Nice conceptual tutorials for glm and glme in Rhttp://www.bodowinter.com/tutorials.html
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the elements of statistical learning (free PDF)http://statweb.stanford.edu/~tibs/ElemStatLearn/*
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Visual information theory blog posthttp://colah.github.io/posts/2015-09-Visual-Information/
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A tutorial on PCAhttps://arxiv.org/abs/1404.1100
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Data visualisations in d3 explaining basic stats conceptshttp://students.brown.edu/seeing-theory/
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Data science
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CS109 Harvard Data sciencehttp://cs109.github.io/2014/pages/syllabus.html
22 lectures, 5 HW over 12 weeks
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as abovehttp://www.quora.com/What-is-it-like-to-take-CS-109-Statistics-121-Data-Science-at-Harvard
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Introduction to Big Datahttps://www.edx.org/course/v2/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x
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Udacity Intro to Data Sciencehttps://www.udacity.com/course/ud359
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The Open Source Data Science Mastershttp://datasciencemasters.org/
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Metacedemyhttps://www.metacademy.org/
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Data science from scratchhttp://shop.oreilly.com/product/0636920033400.do
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General advice
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How to land your first jobhttps://www.linkedin.com/pulse/landing-your-first-real-data-benjamin-taylor
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Open Source DS Masters couplehttps://datascientistjourney.wordpress.com/about/
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80,000 hours exploratory profile on data sciencehttps://80000hours.org/career-guide/top-careers/profiles/data-science/
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Udacity data science skills checklisthttp://blog.udacity.com/data-analyst-skills-checklist-eguide
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Zipfian academy's list of training resourceshttps://github.com/zipfian/data-science-primer
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Machine learning
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Stanford Intro to machine learning (Coursera)https://www.coursera.org/course/ml
18 lectures, 7 hours a week = 2.5 weeks of work
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Amazon machine learninghttp://docs.aws.amazon.com/machine-learning/latest/mlconcepts/
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15 hours of machine learning videos in Rhttp://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
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Machine learning for hackershttp://shop.oreilly.com/product/0636920018483.do
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Machine learning: an algorithmic perspectivehttps://www.crcpress.com/Machine-Learning-An-Algorithmic-Perspective-Second-Edition/Marsland/9781466583283
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Easy datasets for MLhttps://archive.ics.uci.edu/ml/datasets.html
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Machine learning cheat sheethttps://github.com/soulmachine/machine-learning-cheat-sheet
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A few useful things to know about ML (paper)http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
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Applied ML process (PDF, $11)https://machinelearningmastery.com/applied-machine-learning-process/
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Best ML resources for getting startedhttp://machinelearningmastery.com/best-machine-learning-resources-for-getting-started/
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Intro to deep learning with tensorflowhttps://www.udacity.com/course/deep-learning--ud730
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Openai requests for researchhttps://openai.com/requests-for-research/
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Stanform Neural networks CS231n (image recognition)http://cs231n.github.io/
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RNNs in TensorFlowhttps://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html
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Blogpost explaining LSTMhttps://r2rt.com/written-memories-understanding-deriving-and-extending-the-lstm.html
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Data viz for developing intuition about Neural Networkshttp://colah.github.io/posts/2014-10-Visualizing-MNIST/
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Michael Nielson's introduction to Neural Networks and deep learning (free online book)
http://neuralnetworksanddeeplearning.com/
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Practical machine learning for codershttps://course18.fast.ai/ml?fbclid=IwAR0DoMMjH9Wv2rfqDrKyRFKV2evTmF7OE7I_hcimYDr2C6ftb1FeSG0ohTY
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Misc
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A gallery of interesting ipython notebookshttps://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#natural-language-processing
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Zipfian academyhttps://docs.google.com/document/d/1GI3oVas8yswhqPk_8-VIANHR1uJ6R19HNhl7GiH9vq4/pub
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Insight recommendationshttp://insightdatascience.com/blog/preparing_for_insight.html
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software carpentryhttp://software-carpentry.org/lessons.html
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100 interesting data sets for statisticshttp://rs.io/100-interesting-data-sets-for-statistics/
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Interview questionshttp://www.datasciencequestions.com/
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A cool blog by an aspiring data scientisthttps://proquestionasker.github.io/
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Lots and lots of free ML and stats books
https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189?fbclid=IwAR1PkC-LmzedZbr_HNX_FOlc5FTlgW6N0nJ18TEdgoKL_AY9yddw0Uj5Bbg&gi=cef7377cacde
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Databases
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SQL tutorialhttp://sqlzoo.net/w/index.php?title=SQL_Tutorial&redirect=no