- Google’s Python Intro: https://developers.google.com/edu/python/
- Another Introductory Python Resource: http://introtopython.org
- Crash Course in Python for Scientists: http://nbviewer.jupyter.org/gist/rpmuller/5920182
- Visualize Python Code via Python Tutor: http://pythontutor.com
- CodeWars: http://www.codewars.com/
- Code Eval: https://www.codeeval.com/
- Coding Bat: http://codingbat.com/python
- Codin Game: https://www.codingame.com/start
- Checkio: https://www.checkio.org/
- Hacker Rank Data Challenges: https://www.hackerrank.com/domains/data-structures/arrays
- Coder Byte: https://coderbyte.com/
- Top Coder Data Science: https://www.topcoder.com/
- http://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free
- https://www.import.io/post/20-questions-to-detect-fake-data-scientists/
- http://www.datatau.com/
- https://www.yhat.com/
- http://slendermeans.org/ml4h-ch6.html
- https://humancomputation.com/blog/
- https://perplex.city/parallel-thinking-b4076461ff60#.ut0jfngkv
- http://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/
- http://canworksmart.com/using-mean-absolute-error-forecast-accuracy/
- https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
- https://github.com/Mithers/Portfolio <— found some other GA grad’s github
- http://www.visiondummy.com/2014/04/geometric-interpretation-covariance-matrix/
- http://www.visiondummy.com/2014/03/eigenvalues-eigenvectors/
- http://www.visiondummy.com/2014/03/divide-variance-n-1/#Parameter_variance
- http://scott.fortmann-roe.com/docs/BiasVariance.html
- http://machinelearningmastery.com/time-series-forecasting-supervised-learning/?__s=vu4zbwvwhtewqsso99ny
- https://www.analyticsvidhya.com/blog/2016/03/practical-guide-principal-component-analysis-python/
- https://onlinecourses.science.psu.edu/stat505/node/54
- http://www.ats.ucla.edu/stat/sas/output/principal_components.htm
- https://www.datascience.com/blog/introduction-to-bayesian-inference-learn-data-science-tutorials
- https://climateecology.wordpress.com/2014/01/27/pystan-a-basic-tutorial-of-bayesian-data-analysis-in-python/
- http://nedbatchelder.com/text/unipain.html
- http://docs.statwing.com/interpreting-residual-plots-to-improve-your-regression/#nonlinear-header
- https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/
- https://blog.remibergsma.com/2012/07/10/setting-locales-correctly-on-mac-osx-terminal-application/
- http://www.techpoweredmath.com/spark-dataframes-mllib-tutorial/
- https://maryrosecook.com/blog/post/git-from-the-inside-out <— Interesting stuff on functional programming
- Data Camp
- Model Comparison Pro/Con Chart, by Kevin Markham
- A Visual Introduction to Machine Learning
- Metacademy - Machine Learning / Stat Encyclopedia
- Python Machine Learning Textbook (Text + Code on Github)
- Quora Discussion on (Outcomes) Differences in the Data Science Field
- Curriculum Review Notes w Labeled Visuals from Andrew Ng’s Coursera Course (similar topics)
- Aggregated / Crowdsourced list of Data Science Resources - DataTau
- Practical Business Python (blog)
- Introduction to Supervised Learning w Scikit Learn
- Introduction to Unsupervised Learning w Scikit Learn
- Additional Examples Divided by Data Science Topic
- http://earthpy.org/
- https://www.dataquest.io/blog/images/cheat-sheets/pandas-cheat-sheet.pdf
- Introduction to Pandas in 11 Steps: https://bitbucket.org/hrojas/learn-pandas
- Additional Pandas Tutorial: Pandas Head to Tail
- https://community.modeanalytics.com/python/tutorial/pandas-groupby-and-python-lambda-functions/
- https://stackoverflow.com/questions/30679467/pivot-tables-or-group-by-for-pandas
https://elitedatascience.com/feature-engineering-best-practices https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/ https://www.quora.com/What-are-some-best-practices-in-Feature-Engineering http://www.jmlr.org/
https://github.com/jmschrei/pomegranate
https://students.brown.edu/seeing-theory/ ** HAS BASEBALL DATA! (‘homerun’ and ‘hitter’)
https://github.com/AllenDowney/BayesSeminar
https://www.youtube.com/watch?v=VVbJ4jEoOfU&t=1151s&list=PL0eRwZHmE_S_vLkhXktls0PXSZIHJZ3Fb&index=2 https://www.youtube.com/watch?v=rZvro4-nFIk&index=3&list=PL0eRwZHmE_S_vLkhXktls0PXSZIHJZ3Fb
- http://compneurosci.com/wiki/images/4/42/Intro_to_PCA_and_ICA.pdf http://cs229.stanford.edu/notes/cs229-notes11.pdf
- https://github.com/dustinstansbury/stacked_generalization
- https://mlwave.com/kaggle-ensembling-guide/
- https://rasbt.github.io/mlxtend/user_guide/classifier/StackingClassifier/
- http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/
- http://sebastianraschka.com/Articles/2014_ensemble_classifier.html
- http://machinelearningmastery.com/implementing-stacking-scratch-python/
- http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/ - R
- http://www.kdnuggets.com/2015/06/ensembles-kaggle-data-science-competition-p3.html
https://github.com/ultimatist/ODSC17.git https://www.youtube.com/watch?v=JNfxr4BQrLk&t=3012s http://earthpy.org/pandas-basics.html https://github.com/agconti/trading/blob/master/GOOG%20V.%20AAPL%20Correlation%20Arb.ipynb
https://grisha.org/blog/2016/01/29/triple-exponential-smoothing-forecasting/
http://machinelearningmastery.com/how-to-tune-algorithm-parameters-with-scikit-learn/
More complex http://fa.bianp.net/blog/2016/hyperparameter-optimization-with-approximate-gradient/
https://github.com/kwartler/text_mining https://github.com/diegonogare/DataScience/tree/master/Text%20Mining http://juliasilge.com/blog/ https://www.good.is/articles/can-yelp-help-independent-restaurants-drive-chains-out-of-business https://www.springboard.com/blog/eat-rate-love-an-exploration-of-r-yelp-and-the-search-for-good-indian-food/ http://www.theatlantic.com/business/archive/2011/10/how-yelp-helps-steer-people-away-fast-food-chains/337181/ http://cs109.joeong.com/ <— cool MIT project https://www.canva.com/design/DACJbaSfIMY/jf93l6bhZr1WO1CgVXX0DA/edit https://blog.insightdatascience.com/super-donor-detecting-hidden-matches-in-a-public-sperm-donor-registry-a687fe6e05a0#.rvxktifgh http://www.dailydot.com/layer8/fake-news-sites-list-facebook/ https://journals.agh.edu.pl/csci/article/viewFile/1339/1311 https://priceonomics.com/our-fixation-on-terrorism/ http://dlab.berkeley.edu/blog/scraping-new-york-times-articles-python-tutorial https://aqibsaeed.github.io/2016-07-26-text-classification/ http://people.cs.vt.edu/naren/papers/sdm2016.pdf http://www.kdnuggets.com/2015/01/text-analysis-101-document-classification.html http://blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/ https://pdfs.semanticscholar.org/aa96/9114cf6e4d77c5bb3dd62a20bee3446f33ab.pdf http://nlp.stanford.edu/courses/cs224n/2011/reports/nccohen-aatreya-jameszjj.pdf http://nlp.stanford.edu/courses/cs224n/2012/reports/kat_busch_writeup.pdf https://www.cs.sfu.ca/~anoop/papers/pdf/anoop_maryam-canvas-2013.pdf http://hint.fm/papers/wordtree_final2.pdf https://bl.ocks.org/mbostock/4339083 http://bbengfort.github.io/tutorials/2016/05/19/text-classification-nltk-sckit-learn.html <— Teresa’s most used NLTK tutorial for capstone https://rud.is/b/2013/03/12/visualizing-risky-words-part-4-d3-word-trees/ http://peekaboo-vision.blogspot.de/2012/11/a-wordcloud-in-python.html http://blancosilva.github.io/post/2016/08/24/bokeh.html http://streamhacker.com/2010/06/16/text-classification-sentiment-analysis-eliminate-low-information-features/ http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/ http://fjavieralba.com/basic-sentiment-analysis-with-python.html http://www.nytimes.com/interactive/2012/09/06/us/politics/convention-word-counts.html?_r=0 http://people.csail.mit.edu/azar/wp-content/uploads/2011/09/thesis.pdf
- https://www.twinword.com/blog/interpreting-the-score-and-ratio-of-sentiment/
- http://stackoverflow.com/questions/26569478/performing-grid-search-on-sklearn-naive-bayes-multinomialnb-on-multi-core-machin
- https://www.rosette.com/ - Text analytics company with free api calls to test (entities, names, topics, relationships, linguistics)
https://github.com/danromuald?tab=repositories
https://dspace.mit.edu/handle/1721.1/9044 https://www.smashingmagazine.com/2017/01/algorithm-driven-design-how-artificial-intelligence-changing-design/#comments-algorithm-driven-design-how-artificial-intelligence-changing-design
https://medium.com/generative-design/design-optimization-2ec2ba3b40f7 https://www.datadvance.net/ https://www.formtrends.com/algorithms-design/ https://www.grasshopper3d.com/page/download-1 https://www.rhino3d.com/download
https://people.eecs.berkeley.edu/~russell/aima1e/chapter01.pdf http://dilab.gatech.edu/test/wp-content/uploads/2014/11/AI-GoelDavies2011-Final.pdf http://courses.csail.mit.edu/6.034f/ai3/rest.pdf
https://classroom.udacity.com/courses/ud409
- http://groupvisual.io/work/
- https://preinventedwheel.com/easy-python-time-series-plots-with-matplotlib/
http://www.delimited.io/blog/2013/12/8/chord-diagrams-in-d3
https://github.com/WeatherGod/interactive_mpl_tutorial https://github.com/matplotlib/AnatomyOfMatplotlib
https://elitedatascience.com/python-seaborn-tutorial
https://github.com/morganecf/imdb-odsc
http://www.datasciencemanifesto.org/ https://drivendata.github.io/cookiecutter-data-science/#cookiecutter-data-science https://www.slideshare.net/srikanthps/scrum-in-15-minutes-presentation https://www.slideshare.net/joelhorwitz/agile-data-science-36258963 https://www.slideshare.net/srogers74/agile-software-development-overview-presentation/11-Introduction_to_Agile_Methodologies_contd https://www.slideshare.net/katemats/manage-datascience-2013strata/17-After_For_the_top_search gm-spacagna/datasciencemanifesto-copy#1
http://www.kdnuggets.com/2017/06/dataiku-checklist-data-science-implemented-production.html
http://vrl.cs.brown.edu/color - generates categorical color palettes http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3 - generates categorical color palettes http://algorithms-tour.stitchfix.com/#data-platform - storytelling with d3 https://students.brown.edu/seeing-theory/regression/index.html#first - visually seeing statistics theories
http://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html
http://www1.cmc.edu/pages/faculty/BHunter/
https://github.com/CaptainKanuk https://songyao21.github.io/Research_Papers/Risk%20Transfer%20versus%20Cost%20Reduction.pdf
https://github.com/jdwittenauer/kaggle https://www.slideshare.net/markpeng/general-tips-for-participating-kaggle-competitions
- http://generalassembly.github.io/prework/cl/#/
- https://www.codecademy.com/learn/learn-the-command-line
- https://www.learnenough.com/command-line-tutorial
- https://bootcamp.chartio.com/intro-data-analysis/data-storage
- https://www.codecademy.com/learn/sql-analyzing-business-metrics
- https://www.codecademy.com/learn/sql-table-transformation
- http://www.w3schools.com/sql/sql_intro.asp
- http://sql.learncodethehardway.org/book/introduction.html
- https://www.khanacademy.org/computing/computer-programming/sql
- https://academy.vertabelo.com
http://kawahara.ca/how-to-debug-a-jupyter-ipython-notebook/
https://github.com/drivendata/data-science-is-software https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/
https://github.com/amueller/advanced_training
https://blog.udacity.com/data-analyst-skills-checklist-eguide