Data science training resources
 Share
The version of the browser you are using is no longer supported. Please upgrade to a supported browser.Dismiss

 
View only
 
 
ABCDEFGHIJKLMNOPQRSTUVWXYZ
1
Computer Science/Programming
2
Stanford introduction to CShttp://see.stanford.edu/see/materials/icspacs106b/assignments.aspx http://web.stanford.edu/class/cs106a/faq.shtml
3
Harvard CS205 foundations for computational sciencehttp://iacs-courses.seas.harvard.edu/courses/cs205/syllabus.html
38 lectures,5 HW assignments, over 15 weeks
4
Learn R in a day
http://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
5
The little schemer (book)http://www.amazon.co.uk/The-Little-Schemer-Daniel-Friedman/dp/0262560992
6
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
7
Seven languages in seven weeks (Book)https://geneticmail.com/scott/library/text/seven-languages-in-seven-weeks_p1_0.pdf
8
hackerrankhttps://www.hackerrank.com/domains
9
Stanford CS 106 A (Java)http://web.stanford.edu/class/cs106a/
10
Problem solving with algorithms and data structureshttp://interactivepython.org/runestone/static/pythonds/index.html
11
command line crash coursehttp://cli.learncodethehardway.org/book/
12
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/
13
100 numpy exerciseshttp://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html
14
Scientific python lectures (in iPython NB format)https://github.com/jrjohansson/scientific-python-lectures
15
R programming free e-book (to accompany John Hopkins Coursera course)https://leanpub.com/rprogramming
16
Plotly intro to numpyhttps://plot.ly/numpy/
17
Pandas cheatsheets (Enthought)https://www.enthought.com/services/training/pandas-mastery-workshop/#pandas-cheat-sheet-download
18
nand2tetris (build a computer)http://www.nand2tetris.org/
19
Understanding computation: From simple machines to impossible programs (ruby)
http://computationbook.com/
20
21
Visualisation
22
Harvard CS 171http://www.cs171.org/2015/index.html
23
24
Math/Stats
25
Why learn linear algebra?http://machinelearningmastery.com/linear-algebra-machine-learning/
26
Udacity introduction to statshttps://www.udacity.com/course/viewer#!/c-st0951 month (6hrs/day)Calculus
27
Harvard 110: Introduction to probabilityhttp://isites.harvard.edu/icb/icb.do?keyword=k104821&pageid=icb.page676263Graph theory
28
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
29
as abovehttps://www.edx.org/course/introduction-probability-science-mitx-6-041x-0#.VSAlA_nF_SE
30
MIT: Multivariable calculus http://ocw.mit.edu/courses/mathematics/18-02-multivariable-calculus-fall-2007/35 lectures
31
Khan academy: Linear algebrahttps://www.khanacademy.org/math/linear-algebra
32
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
33
sliderule
34
Bayesian inference for hackershttp://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb
35
Think stats oreilly [Python heavy]http://www.greenteapress.com/thinkstats/
36
Think bayes oreillyhttp://www.greenteapress.com/thinkbayes/
37
Simple stats with Scipy https://oneau.wordpress.com/2011/02/28/simple-statistics-with-scipy/
38
All of statisticshttp://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/0387402721
39
ipython notebooks for linear regression, logistic regression, random forests, k means
http://nborwankar.github.io/LearnDataScience/
40
linear regression in Pythonhttp://www.dataschool.io/linear-regression-in-python/
41
statstics in a nutshellhttp://www.amazon.co.uk/Statistics-Nutshell-Desktop-Reference-OReilly/dp/0596510497
42
probability cheat sheethttps://github.com/wzchen/probability_cheatsheet
43
Linear algebra (MIT open courseware, Gilbert Strang)http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
44
Penn State Intro to Probability (good intro notes)https://onlinecourses.science.psu.edu/stat414/node/287
45
Implementing a neural network from scratchhttp://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/
46
Nice conceptual tutorials for glm and glme in Rhttp://www.bodowinter.com/tutorials.html
47
the elements of statistical learning (free PDF)http://statweb.stanford.edu/~tibs/ElemStatLearn/*
48
Visual information theory blog posthttp://colah.github.io/posts/2015-09-Visual-Information/
49
A tutorial on PCAhttps://arxiv.org/abs/1404.1100
50
Data visualisations in d3 explaining basic stats conceptshttp://students.brown.edu/seeing-theory/
51
52
53
Data science
54
CS109 Harvard Data sciencehttp://cs109.github.io/2014/pages/syllabus.html
22 lectures, 5 HW over 12 weeks
55
as abovehttp://www.quora.com/What-is-it-like-to-take-CS-109-Statistics-121-Data-Science-at-Harvard
56
Introduction to Big Datahttps://www.edx.org/course/v2/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x
57
Udacity Intro to Data Sciencehttps://www.udacity.com/course/ud359
58
The Open Source Data Science Mastershttp://datasciencemasters.org/
59
Metacedemyhttps://www.metacademy.org/
60
Data science from scratchhttp://shop.oreilly.com/product/0636920033400.do
61
62
General advice
63
How to land your first jobhttps://www.linkedin.com/pulse/landing-your-first-real-data-benjamin-taylor
64
Open Source DS Masters couplehttps://datascientistjourney.wordpress.com/about/
65
80,000 hours exploratory profile on data sciencehttps://80000hours.org/career-guide/top-careers/profiles/data-science/
66
Udacity data science skills checklisthttp://blog.udacity.com/data-analyst-skills-checklist-eguide
67
Zipfian academy's list of training resourceshttps://github.com/zipfian/data-science-primer
68
69
Machine learning
70
Stanford Intro to machine learning (Coursera)https://www.coursera.org/course/ml
18 lectures, 7 hours a week = 2.5 weeks of work
71
Amazon machine learninghttp://docs.aws.amazon.com/machine-learning/latest/mlconcepts/
72
15 hours of machine learning videos in Rhttp://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
73
Machine learning for hackershttp://shop.oreilly.com/product/0636920018483.do
74
Machine learning: an algorithmic perspectivehttps://www.crcpress.com/Machine-Learning-An-Algorithmic-Perspective-Second-Edition/Marsland/9781466583283
75
Easy datasets for MLhttps://archive.ics.uci.edu/ml/datasets.html
76
Machine learning cheat sheethttps://github.com/soulmachine/machine-learning-cheat-sheet
77
A few useful things to know about ML (paper)http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
78
Applied ML process (PDF, $11)https://machinelearningmastery.com/applied-machine-learning-process/
79
Best ML resources for getting startedhttp://machinelearningmastery.com/best-machine-learning-resources-for-getting-started/
80
Intro to deep learning with tensorflowhttps://www.udacity.com/course/deep-learning--ud730
81
Openai requests for researchhttps://openai.com/requests-for-research/
82
Stanform Neural networks CS231n (image recognition)http://cs231n.github.io/
83
RNNs in TensorFlowhttps://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html
84
Blogpost explaining LSTMhttps://r2rt.com/written-memories-understanding-deriving-and-extending-the-lstm.html
85
Data viz for developing intuition about Neural Networkshttp://colah.github.io/posts/2014-10-Visualizing-MNIST/
86
Michael Nielson's introduction to Neural Networks and deep learning (free online book)
http://neuralnetworksanddeeplearning.com/
87
88
Misc
89
A gallery of interesting ipython notebookshttps://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#natural-language-processing
90
Zipfian academyhttps://docs.google.com/document/d/1GI3oVas8yswhqPk_8-VIANHR1uJ6R19HNhl7GiH9vq4/pub
91
Insight recommendationshttp://insightdatascience.com/blog/preparing_for_insight.html
92
software carpentryhttp://software-carpentry.org/lessons.html
93
100 interesting data sets for statisticshttp://rs.io/100-interesting-data-sets-for-statistics/
94
Interview questionshttp://www.datasciencequestions.com/
95
A cool blog by an aspiring data scientisthttps://proquestionasker.github.io/
96
97
Databases
98
SQL tutorialhttp://sqlzoo.net/w/index.php?title=SQL_Tutorial&redirect=no
99
Introduction to databaseshttps://www.coursera.org/course/db
100
sql cookbookhttp://shop.oreilly.com/product/9780596009762.do
Loading...
Main menu