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General Assembly's Data Science coursehttps://generalassemb.ly/education/data-science/washington-dc/
Data School bloghttp://www.dataschool.io/
email newsletterhttp://www.dataschool.io/subscribe/
YouTube channelhttps://www.youtube.com/user/dataschool
http://mybinder.org/repo/justmarkham/DAT8
Introduction to Data Sciencehttps://github.com/code-ram/DAT8#class-1-introduction-to-data-science
Command Line, Version Controlhttps://github.com/code-ram/DAT8#class-2-command-line-and-version-control
Data Reading and Cleaninghttps://github.com/code-ram/DAT8#class-3-data-reading-and-cleaning
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Machine Learninghttps://github.com/code-ram/DAT8#class-6-machine-learning
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Naive Bayes and Text Datahttps://github.com/code-ram/DAT8#class-14-naive-bayes-and-text-data
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https://github.com/code-ram/DAT8#python-resources
Codecademy's Python coursehttp://www.codecademy.com/en/tracks/python
Dataquesthttps://www.dataquest.io
Google's Python Classhttps://developers.google.com/edu/python/
Introduction to Pythonhttp://introtopython.org/
Python for Informaticshttp://www.pythonlearn.com/book.php
slideshttps://drive.google.com/folderview?id=0B7X1ycQalUnyal9yeUx3VW81VDg&usp=sharing
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https://github.com/code-ram/DAT8#course-project
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https://github.com/code-ram/DAT8#comparison-of-machine-learning-models
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https://github.com/code-ram/DAT8#comparison-of-model-evaluation-procedures-and-metrics
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https://github.com/code-ram/DAT8#advice-for-getting-better-at-data-science
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Win-Vectorhttp://www.win-vector.com/blog/2012/09/on-being-a-data-scientist/
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Data Science at the Command Linehttp://shop.oreilly.com/product/0636920032823.do
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Want to understand Python's comprehensions? Think in Excel or SQLhttp://blog.lerner.co.il/want-to-understand-pythons-comprehensions-think-like-an-accountant/
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What I do when I get a new data set as told through tweetshttp://simplystatistics.org/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/
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Python for Data Analysishttp://shop.oreilly.com/product/0636920023784.do
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similar notebookhttps://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb
Overview of Python Visualization Toolshttp://pbpython.com/visualization-tools-1.html
Choosing a Good Charthttp://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf
The Graphic Continuumhttp://www.coolinfographics.com/storage/post-images/The-Graphic-Continuum-POSTER.jpg
R Graph Cataloghttp://shiny.stat.ubc.ca/r-graph-catalog/
PowerPoint presentationhttp://www2.research.att.com/~volinsky/DataMining/Columbia2011/Slides/Topic2-EDAViz.ppt
Harvard's Data Science coursehttp://cs109.github.io/2014/
Visualization Goals, Data Types, and Statistical Graphshttp://cm.dce.harvard.edu/2015/01/14328/L03/screen_H264LargeTalkingHead-16x9.shtml
slideshttps://docs.google.com/file/d/0B7IVstmtIvlHLTdTbXdEVENoRzQ/edit
https://github.com/code-ram/DAT8#class-6-machine-learning
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/05_pandas_visualization.ipynb
Iris datasethttp://archive.ics.uci.edu/ml/datasets/Iris
Iris photohttp://sebastianraschka.com/Images/2014_python_lda/iris_petal_sepal.png
Notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/06_human_learning_iris.ipynb
slideshttps://github.com/code-ram/DAT8/blob/master/slides/06_machine_learning.pdf
human learning notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/06_human_learning_iris.ipynb
requestshttp://www.python-requests.org/en/latest/user/install/
Beautiful Soup 4http://www.crummy.com/software/BeautifulSoup/bs4/doc/#installing-beautiful-soup
What is machine learning, and how does it work?https://www.youtube.com/watch?v=elojMnjn4kk
associated notebookhttps://github.com/justmarkham/scikit-learn-videos/blob/master/01_machine_learning_intro.ipynb
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
Learning Paradigmshttp://work.caltech.edu/library/014.html
Caltech's Learning From Data coursehttp://work.caltech.edu/telecourse.html
Real-World Active Learninghttps://beta.oreilly.com/ideas/real-world-active-learning
overview of the supervised learning processhttps://github.com/rasbt/pattern_classification/blob/master/machine_learning/supervised_intro/introduction_to_supervised_machine_learning.md
Data Science, Machine Learning, and Statistics: What is in a Name?http://www.win-vector.com/blog/2013/04/data-science-machine-learning-and-statistics-what-is-in-a-name/
The Emoji Translation Projecthttps://www.kickstarter.com/projects/fred/the-emoji-translation-project
characteristics of your zip codehttp://www.esri.com/landing-pages/tapestry/
67 distinct segmentshttp://doc.arcgis.com/en/esri-demographics/data/tapestry-segmentation.htm
scikit-learn and the IPython Notebookhttps://www.youtube.com/watch?v=IsXXlYVBt1M
associated notebookhttps://github.com/justmarkham/scikit-learn-videos/blob/master/02_machine_learning_setup.ipynb
Notebook tutorialshttps://github.com/jupyter/notebook/blob/master/docs/source/examples/Notebook/Examples%20and%20Tutorials%20Index.ipynb
Reddit discussionhttps://www.reddit.com/r/Python/comments/3be5z2/do_you_prefer_ipython_notebook_over_ipython/
https://github.com/code-ram/DAT8#class-7-getting-data
solutionhttps://github.com/code-ram/DAT8/blob/master/code/05_pandas_homework_imdb.py
solutionhttps://github.com/code-ram/DAT8/blob/master/notebooks/06_human_learning_iris.ipynb
codehttps://github.com/code-ram/DAT8/blob/master/code/07_api.py
OMDb APIhttp://www.omdbapi.com/
codehttps://github.com/code-ram/DAT8/blob/master/code/07_web_scraping.py
IMDb: robots.txthttp://www.imdb.com/robots.txt
Example web pagehttps://github.com/code-ram/DAT8/blob/master/data/example.html
IMDb: The Shawshank Redemptionhttp://www.imdb.com/title/tt0111161/
web scraping codehttps://github.com/code-ram/DAT8/blob/master/code/07_web_scraping.py
install Seabornhttp://stanford.edu/~mwaskom/software/seaborn/installing.html
query the U.S. Census APIhttps://github.com/laurakurup/census-api
Mashapehttps://www.mashape.com/explore
Apigeehttps://apigee.com/providers
Python API wrapperhttp://www.pythonforbeginners.com/api/list-of-python-apis
Data Science Toolkithttp://www.datasciencetoolkit.org/
API Integration in Pythonhttps://realpython.com/blog/python/api-integration-in-python/
Face Detection APIhttps://www.projectoxford.ai/demo/face#detection
How-Old.nethttp://how-old.net/
Beautiful Soup documentationhttp://www.crummy.com/software/BeautifulSoup/bs4/doc/
specifying a parserhttp://www.crummy.com/software/BeautifulSoup/bs4/doc/#specifying-the-parser-to-use
Web Scraping 101 with Pythonhttp://www.gregreda.com/2013/03/03/web-scraping-101-with-python/
scraping Craigslisthttps://github.com/Alexjmsherman/DataScience_GeneralAssembly/blob/master/Final_Project/1.%20Final_Project_Data%20Scraping.ipynb
notebookhttp://web.stanford.edu/~zlotnick/TextAsData/Web_Scraping_with_Beautiful_Soup.html
notebookhttps://github.com/cs109/2014/blob/master/lectures/2014_09_23-lecture/data_scraping_transcript.ipynb
videohttp://cm.dce.harvard.edu/2015/01/14328/L07/screen_H264LargeTalkingHead-16x9.shtml
Web Scraping with Pythonhttps://www.youtube.com/watch?v=p1iX0uxM1w8
slideshttps://docs.google.com/presentation/d/1uHM_esB13VuSf7O1ScGueisnrtu-6usGFD3fs4z5YCE/edit#slide=id.p
codehttps://github.com/kjam/python-web-scraping-tutorial
Scrapyhttp://scrapy.org/
documentationhttp://doc.scrapy.org/en/1.0/index.html
tutorialhttps://github.com/rdempsey/ddl-data-wrangling
robotstxt.orghttp://www.robotstxt.org/robotstxt.html
import.iohttps://import.io/
Kimonohttps://www.kimonolabs.com/
How a Math Genius Hacked OkCupid to Find True Lovehttp://www.wired.com/2014/01/how-to-hack-okcupid/all/
How Netflix Reverse Engineered Hollywoodhttp://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/?single_page=true
https://github.com/code-ram/DAT8#class-8-k-nearest-neighbors
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/08_pandas_review.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/08_knn_sklearn.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/08_nba_knn.ipynb
datahttps://github.com/justmarkham/DAT4-students/blob/master/kerry/Final/NBA_players_2015.csv
data dictionaryhttps://github.com/justmarkham/DAT-project-examples/blob/master/pdf/nba_paper.pdf
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/08_bias_variance.ipynb
bias-variance tradeoffhttps://github.com/code-ram/DAT8/blob/master/homework/09_bias_variance.md
introduction to reproducibilityhttp://www.dataschool.io/reproducibility-is-not-just-for-researchers/
guide to creating a reproducible analysishttps://github.com/jtleek/datasharing
Colbert Report videohttp://thecolbertreport.cc.com/videos/dcyvro/austerity-s-spreadsheet-error
Getting started in scikit-learn with the famous iris datasethttps://www.youtube.com/watch?v=hd1W4CyPX58
Training a machine learning model with scikit-learnhttps://www.youtube.com/watch?v=RlQuVL6-qe8
distance metricshttp://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
Mahalanobis distancehttp://stats.stackexchange.com/questions/62092/bottom-to-top-explanation-of-the-mahalanobis-distance
takes the scale of the data into accounthttp://blogs.sas.com/content/iml/2012/02/15/what-is-mahalanobis-distance.html
A Detailed Introduction to KNNhttps://saravananthirumuruganathan.wordpress.com/2010/05/17/a-detailed-introduction-to-k-nearest-neighbor-knn-algorithm/
Image Classificationhttp://cs231n.github.io/classification/
object recognitionhttp://vlm1.uta.edu/~athitsos/nearest_neighbors/
satellite image enhancementhttp://land.umn.edu/documents/FS6.pdf
document categorizationhttp://www.ceng.metu.edu.tr/~e120321/paper.pdf
gene expression analysishttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.993
detailed tutorialshttp://web.stanford.edu/~mwaskom/software/seaborn/tutorial.html
example galleryhttp://web.stanford.edu/~mwaskom/software/seaborn/examples/index.html
Data visualization with Seabornhttps://beta.oreilly.com/learning/data-visualization-with-seaborn
Visualizing Google Forms Data with Seabornhttp://pbpython.com/pandas-google-forms-part2.html
How to Create NBA Shot Charts in Pythonhttp://savvastjortjoglou.com/nba-shot-sharts.html
https://github.com/code-ram/DAT8#class-9-basic-model-evaluation
solutionhttps://github.com/code-ram/DAT8/blob/master/code/07_web_scraping.py#L136
introductionhttp://www.dataschool.io/reproducibility-is-not-just-for-researchers/
Colbert Report videohttp://thecolbertreport.cc.com/videos/dcyvro/austerity-s-spreadsheet-error
cabs articlehttp://iquantny.tumblr.com/post/107245431809/how-software-in-half-of-nyc-cabs-generates-5-2
Tweethttps://twitter.com/jakevdp/status/519563939177197571
creating a reproducible analysishttps://github.com/jtleek/datasharing
Classic rockhttps://github.com/fivethirtyeight/data/tree/master/classic-rock
student project 1https://github.com/jwknobloch/DAT4_final_project
student project 2https://github.com/justmarkham/DAT4-students/tree/master/Jonathan_Bryan/Project_Files
bias-variance tradeoffhttps://github.com/code-ram/DAT8/blob/master/homework/09_bias_variance.md
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/09_model_evaluation.ipynb
module referencehttp://scikit-learn.org/stable/modules/classes.html
user guidehttp://scikit-learn.org/stable/user_guide.html
Data science in Pythonhttps://www.youtube.com/watch?v=3ZWuPVWq7p4
associated notebookhttps://github.com/justmarkham/scikit-learn-videos/blob/master/06_linear_regression.ipynb
The Easiest Introduction to Regression Analysishttps://www.youtube.com/watch?v=k_OB1tWX9PM
Comparing machine learning models in scikit-learnhttps://www.youtube.com/watch?v=0pP4EwWJgIU
estimating prediction errorhttps://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s
visualizing bias and variancehttp://work.caltech.edu/library/081.html
Random Test/Train Split is Not Always Enoughhttp://www.win-vector.com/blog/2015/01/random-testtrain-split-is-not-always-enough/
What We've Learned About Sharing Our Data Analysishttps://source.opennews.org/en-US/articles/what-weve-learned-about-sharing-our-data-analysis/
Software development skills for data scientistshttp://treycausey.com/software_dev_skills.html
Data science done well looks easy - and that is a big problem for data scientistshttp://simplystatistics.org/2015/03/17/data-science-done-well-looks-easy-and-that-is-a-big-problem-for-data-scientists/
https://github.com/code-ram/DAT8#class-10-linear-regression
articlehttp://blog.dominodatalab.com/10-interesting-uses-of-data-science/
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/10_linear_regression.ipynb
Capital Bikeshare datasethttps://github.com/code-ram/DAT8/blob/master/data/bikeshare.csv
Data dictionaryhttps://www.kaggle.com/c/bike-sharing-demand/data
Predicting User Engagement in Corporate Collaboration Networkhttps://github.com/mikeyea/DAT7_project/blob/master/final%20project/Class_Presention_MYea.ipynb
homework assignmenthttps://github.com/code-ram/DAT8/blob/master/homework/10_yelp_votes.md
Yelp datahttps://github.com/code-ram/DAT8/blob/master/data/yelp.csv
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
related videoshttp://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
quick reference guidehttp://www.dataschool.io/applying-and-interpreting-linear-regression/
introduction to linear regressionhttp://people.duke.edu/~rnau/regintro.htm
assumptions of linear regressionhttp://pareonline.net/getvn.asp?n=2&v=8
interactive visualizationhttp://setosa.io/ev/ordinary-least-squares-regression/
Statsmodelshttp://statsmodels.sourceforge.net/
DAT7 lesson on linear regressionhttps://github.com/justmarkham/DAT7/blob/master/notebooks/10_linear_regression.ipynb
confidence intervalshttp://www.quora.com/What-is-a-confidence-interval-in-laymans-terms/answer/Michael-Hochster
Hypothesis Testing: The Basicshttp://20bits.com/article/hypothesis-testing-the-basics
Statistics Without the Agonizing Painhttps://www.youtube.com/watch?v=5Dnw46eC-0o
summaryhttp://www.scientificamerican.com/article/scientists-perturbed-by-loss-of-stat-tools-to-sift-research-fudge-from-fact/
responsehttp://www.nature.com/news/statistics-p-values-are-just-the-tip-of-the-iceberg-1.17412
paperhttp://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf
Science Isn't Brokenhttp://fivethirtyeight.com/features/science-isnt-broken/
Accurately Measuring Model Prediction Errorhttp://scott.fortmann-roe.com/docs/MeasuringError.html
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
visualizations of the bikeshare datahttps://www.kaggle.com/c/bike-sharing-demand/scripts?outputType=Visualization
https://github.com/code-ram/DAT8#class-11-first-project-presentation
probabilityhttps://www.youtube.com/watch?v=o4QmoNfW3bI
oddshttps://www.youtube.com/watch?v=GxbXQjX7fC0
An Intuitive Guide To Exponential Functions & ehttp://betterexplained.com/articles/an-intuitive-guide-to-exponential-functions-e/
Demystifying the Natural Logarithm (ln)http://betterexplained.com/articles/demystifying-the-natural-logarithm-ln/
brief summaryhttps://github.com/code-ram/DAT8/blob/master/notebooks/12_e_log_examples.ipynb
https://github.com/code-ram/DAT8#class-12-logistic-regression
solutionhttps://github.com/code-ram/DAT8/blob/master/notebooks/10_yelp_votes_homework.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/12_logistic_regression.ipynb
Glass identification datasethttps://archive.ics.uci.edu/ml/datasets/Glass+Identification
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/12_titanic_confusion.ipynb
datahttps://github.com/code-ram/DAT8/blob/master/data/titanic.csv
data dictionaryhttps://www.kaggle.com/c/titanic/data
slideshttps://github.com/code-ram/DAT8/blob/master/slides/12_confusion_matrix.pdf
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/12_titanic_confusion.ipynb
Intuitive sensitivity and specificityhttps://www.youtube.com/watch?v=U4_3fditnWg
The tradeoff between sensitivity and specificityhttps://www.youtube.com/watch?v=vtYDyGGeQyo
ROC curves and AUChttps://github.com/code-ram/DAT8/blob/master/homework/13_roc_auc.md
cross-validationhttps://github.com/code-ram/DAT8/blob/master/homework/13_cross_validation.md
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
first three videoshttp://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
machine learning coursehttps://www.coursera.org/learn/machine-learning/home/info
related lecture noteshttp://www.holehouse.org/mlclass/06_Logistic_Regression.html
guidehttp://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm
lecture noteshttp://www.unm.edu/~schrader/biostat/bio2/Spr06/lec11.pdf
explanationhttp://scikit-learn.org/stable/modules/calibration.html
Supervised learning superstitions cheat sheethttp://ryancompton.net/assets/ml_cheat_sheet/supervised_learning.html
simple guide to confusion matrix terminologyhttp://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
Amazon Machine Learninghttps://aws.amazon.com/blogs/aws/amazon-machine-learning-make-data-driven-decisions-at-scale/
graphichttps://media.amazonwebservices.com/blog/2015/ml_adjust_model_1.png
how to calculate "expected value"https://github.com/podopie/DAT18NYC/blob/master/classes/13-expected_value_cost_benefit_analysis.ipynb
https://github.com/code-ram/DAT8#class-13-advanced-model-evaluation
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/13_advanced_model_evaluation.ipynb
video/reading assignmenthttps://github.com/code-ram/DAT8/blob/master/homework/13_roc_auc.md
slideshttps://github.com/code-ram/DAT8/blob/master/slides/13_drawing_roc.pdf
video/reading assignmenthttps://github.com/code-ram/DAT8/blob/master/homework/13_cross_validation.md
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/13_cross_validation.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/13_bank_exercise.ipynb
datahttps://github.com/code-ram/DAT8/blob/master/data/bank-additional.csv
data dictionaryhttps://archive.ics.uci.edu/ml/datasets/Bank+Marketing
spam filteringhttps://github.com/code-ram/DAT8/blob/master/homework/14_spam_filtering.md
Introduction to Probabilityhttps://docs.google.com/presentation/d/1cM2dVbJgTWMkHoVNmYlB9df6P2H8BrjaqAcZTaLe9dA/edit#slide=id.gfc3caad2_00
OpenIntro Statistics textbookhttps://www.openintro.org/stat/textbook.php?stat_book=os
visualizationhttp://setosa.io/conditional/
wealth and happinesshttp://www.quora.com/What-is-an-intuitive-explanation-of-Bayes-Rule/answer/Michael-Hochster
duckshttps://planspacedotorg.wordpress.com/2014/02/23/bayes-rule-for-ducks/
legoshttp://www.countbayesie.com/blog/2015/2/18/bayes-theorem-with-lego
ROC Curveshttps://www.youtube.com/watch?v=21Igj5Pr6u4
An introduction to ROC analysishttp://people.inf.elte.hu/kiss/13dwhdm/roc.pdf
comparing different feature setshttp://research.microsoft.com/pubs/205472/aisec10-leontjeva.pdf
comparing different classifiershttp://www.cse.ust.hk/nevinZhangGroup/readings/yi/Bradley_PR97.pdf
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
K-fold and leave-one-out cross-validationhttps://www.youtube.com/watch?v=nZAM5OXrktY
cross-validation the right and wrong wayshttps://www.youtube.com/watch?v=S06JpVoNaA0
paperhttp://www.jcheminf.com/content/pdf/1758-2946-6-10.pdf
GridSearchCV and RandomizedSearchCVhttp://scikit-learn.org/stable/modules/grid_search.html
How to find the best model parameters in scikit-learnhttps://www.youtube.com/watch?v=Gol_qOgRqfA
associated notebookhttps://github.com/justmarkham/scikit-learn-videos/blob/master/08_grid_search.ipynb
model evaluationhttp://scikit-learn.org/stable/modules/model_evaluation.html
Counterfactual evaluation of machine learning modelshttps://www.youtube.com/watch?v=QWCSxAKR-h0
slideshttp://www.slideshare.net/MichaelManapat/counterfactual-evaluation-of-machine-learning-models
Visualizing Machine Learning Thresholds to Make Better Business Decisionshttp://blog.insightdatalabs.com/visualizing-classifier-thresholds/
https://github.com/code-ram/DAT8#class-14-naive-bayes-and-text-data
Slideshttps://github.com/code-ram/DAT8/blob/master/slides/14_bayes_theorem.pdf
Visualizing Bayes' theoremhttp://oscarbonilla.com/2009/05/visualizing-bayes-theorem/
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/14_bayes_theorem_iris.ipynb
Slideshttps://github.com/code-ram/DAT8/blob/master/slides/14_naive_bayes.pdf
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/14_naive_bayes_spam.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/14_text_data_sklearn.ipynb
CountVectorizerhttp://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
datahttps://github.com/code-ram/DAT8/blob/master/data/sms.tsv
data dictionaryhttps://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
homework assignmenthttps://github.com/code-ram/DAT8/blob/master/homework/14_yelp_review_text.md
Yelp datahttps://github.com/code-ram/DAT8/blob/master/data/yelp.csv
TextBlobhttps://textblob.readthedocs.org/
Naive Bayes and Text Classificationhttp://sebastianraschka.com/Articles/2014_naive_bayes_1.html
slideshttps://docs.google.com/presentation/d/1psUIyig6OxHQngGEHr3TMkCvhdLInnKnclQoNUr4G4U/edit#slide=id.gfc69f484_00
OpenIntro Statistics textbookhttps://www.openintro.org/stat/textbook.php?stat_book=os
airport securityhttp://www.quora.com/In-laymans-terms-how-does-Naive-Bayes-work/answer/Konstantin-Tt
Naive Bayes classifierhttp://en.wikipedia.org/wiki/Naive_Bayes_classifier
Naive Bayes spam filteringhttp://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering
Q&Ahttp://stats.stackexchange.com/questions/21822/understanding-naive-bayes
GaussianNBhttp://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
MultinomialNBhttp://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/14_types_of_naive_bayes.ipynb
descriptionhttps://en.wikipedia.org/wiki/Naive_Bayes_classifier#Gaussian_naive_Bayes
examplehttps://en.wikipedia.org/wiki/Naive_Bayes_classifier#Sex_classification
slideshttp://www.umiacs.umd.edu/~jbg/teaching/DATA_DIGGING/lecture_05.pdf
paperhttp://ai.stanford.edu/~ang/papers/nips01-discriminativegenerative.pdf
his follow-up articlehttp://www.paulgraham.com/better.html
related paperhttp://www.merl.com/publications/docs/TR2004-091.pdf
categorizing businesseshttp://engineeringblog.yelp.com/2011/02/towards-building-a-high-quality-workforce-with-mechanical-turk.html
https://github.com/code-ram/DAT8#class-15-natural-language-processing
solutionhttps://github.com/code-ram/DAT8/blob/master/notebooks/14_yelp_review_text_homework.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/15_natural_language_processing.ipynb
Kaggle competitionhttps://inclass.kaggle.com/c/dat8-stack-overflow
Kaggle: How it Workshttps://www.youtube.com/watch?v=PoD84TVdD-4
project presentation videohttps://www.youtube.com/watch?v=HGr1yQV3Um0
slideshttps://speakerdeck.com/justmarkham/allstate-purchase-prediction-challenge-on-kaggle
video lectureshttps://class.coursera.org/nlp/lecture
slideshttp://web.stanford.edu/~jurafsky/NLPCourseraSlides.html
Coursera coursehttps://www.coursera.org/course/nlp
key NLP termshttps://github.com/ga-students/DAT_SF_9/blob/master/16_Text_Mining/DAT9_lec16_Text_Mining.pdf
Natural Language Processing with Pythonhttp://www.nltk.org/book/
Natural Language Toolkithttp://www.nltk.org/
A Smattering of NLP in Pythonhttps://github.com/charlieg/A-Smattering-of-NLP-in-Python/blob/master/A%20Smattering%20of%20NLP%20in%20Python.ipynb
notebook from DAT5https://github.com/justmarkham/DAT5/blob/master/notebooks/14_nlp.ipynb
spaCyhttp://spacy.io/
Stanford CoreNLPhttp://nlp.stanford.edu/software/corenlp.shtml
HashingVectorizerhttp://scikit-learn.org/stable/modules/feature_extraction.html#vectorizing-a-large-text-corpus-with-the-hashing-trick
Automatically Categorizing Yelp Businesseshttp://engineeringblog.yelp.com/2015/09/automatically-categorizing-yelp-businesses.html
Modern Methods for Sentiment Analysishttp://districtdatalabs.silvrback.com/modern-methods-for-sentiment-analysis
Identifying Humorous Cartoon Captionshttp://www.cs.huji.ac.il/~dshahaf/pHumor.pdf
DC Natural Language Processinghttp://www.meetup.com/DC-NLP/
https://github.com/code-ram/DAT8#class-16-kaggle-competition
slideshttps://github.com/code-ram/DAT8/blob/master/slides/16_kaggle.pdf
Predict whether a Stack Overflow question will be closedhttps://inclass.kaggle.com/c/dat8-stack-overflow
Complete code filehttps://github.com/code-ram/DAT8/blob/master/code/16_kaggle.py
Minimal code filehttps://github.com/code-ram/DAT8/blob/master/code/16_kaggle_minimal.py
Explanations of log losshttp://www.quora.com/What-is-an-intuitive-explanation-for-the-log-loss-function
peer review guidelineshttps://github.com/code-ram/DAT8/blob/master/project/peer_review.md
A Visual Introduction to Machine Learninghttp://www.r2d3.us/visual-intro-to-machine-learning-part-1/
Graphvizhttp://www.graphviz.org/
Specialist Knowledge Is Useless and Unhelpfulhttp://www.slate.com/articles/health_and_science/new_scientist/2012/12/kaggle_president_jeremy_howard_amateurs_beat_specialists_in_data_prediction.html
Getting in Shape for the Sport of Data Sciencehttps://www.youtube.com/watch?v=kwt6XEh7U3g
Learning from the besthttp://blog.kaggle.com/2014/08/01/learning-from-the-best/
Feature Engineering Without Domain Expertisehttps://www.youtube.com/watch?v=bL4b1sGnILU
passengers at a train stationhttps://medium.com/@chris_bour/french-largest-data-science-challenge-ever-organized-shows-the-unreasonable-effectiveness-of-open-8399705a20ef
fraudulent users of an online storehttps://docs.google.com/presentation/d/1UdI5NY-mlHyseiRVbpTLyvbrHxY8RciHp5Vc-ZLrwmU/edit#slide=id.p
bots in an online auctionhttps://www.kaggle.com/c/facebook-recruiting-iv-human-or-bot/forums/t/14628/share-your-secret-sauce
subscribe to the next season of an orchestrahttp://blog.kaggle.com/2015/01/05/kaggle-inclass-stanfords-getting-a-handel-on-data-science-winners-report/
quality of e-commerce search engine resultshttp://blog.kaggle.com/2015/07/22/crowdflower-winners-interview-3rd-place-team-quartet/
Our perfect submissionhttps://www.kaggle.com/c/restaurant-revenue-prediction/forums/t/13950/our-perfect-submission
public leaderboardhttps://www.kaggle.com/c/restaurant-revenue-prediction/leaderboard/public
https://github.com/code-ram/DAT8#class-17-decision-trees
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/17_decision_trees.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/17_bikeshare_exercise.ipynb
datahttps://github.com/code-ram/DAT8/blob/master/data/bikeshare.csv
data dictionaryhttps://www.kaggle.com/c/bike-sharing-demand/data
Human Ensemble Learninghttp://mlwave.com/human-ensemble-learning/
Do We Need Hundreds of Classifiers to Solve Real World Classification Problems?http://jmlr.csail.mit.edu/papers/volume15/delgado14a/delgado14a.pdf
commenthttps://news.ycombinator.com/item?id=8719723
decision treeshttp://scikit-learn.org/stable/modules/tree.html
Introduction to Data Mininghttp://www-users.cs.umn.edu/~kumar/dmbook/index.php
A Brief History of Classification and Regression Treeshttps://drive.google.com/file/d/0B-BKohKl-jUYQ3RpMEF0OGRUU3RHVGpHY203NFd3Z19Nc1ZF/view
The Science of Singing Alonghttp://www.doc.gold.ac.uk/~mas03dm/papers/PawleyMullensiefen_Singalong_2012.pdf
identifying psychosishttp://www.psychcongress.com/sites/naccme.com/files/images/pcn/saundras/psychosis_decision_tree.pdf
https://github.com/code-ram/DAT8#class-18-ensembling
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/17_decision_trees.ipynb
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/18_ensembling.ipynb
Major League Baseball player datahttps://github.com/code-ram/DAT8/blob/master/data/hitters.csv
Data dictionaryhttps://cran.r-project.org/web/packages/ISLR/ISLR.pdf
ensemble methodshttp://scikit-learn.org/stable/modules/ensemble.html
Kaggle Ensembling Guidehttp://mlwave.com/kaggle-ensembling-guide/
solution paperhttps://docs.google.com/viewer?url=https://raw.githubusercontent.com/ChenglongChen/Kaggle_CrowdFlower/master/Doc/Kaggle_CrowdFlower_ChenglongChen.pdf
CrowdFlower competitionhttps://www.kaggle.com/c/crowdflower-search-relevance
Interpretable vs Powerful Predictive Models: Why We Need Them Bothhttps://medium.com/@chris_bour/interpretable-vs-powerful-predictive-models-why-we-need-them-both-990340074979
Not Even the People Who Write Algorithms Really Know How They Workhttp://www.theatlantic.com/technology/archive/2015/09/not-even-the-people-who-write-algorithms-really-know-how-they-work/406099/
How do random forests work in layman's terms?http://www.quora.com/Random-Forests/How-do-random-forests-work-in-laymans-terms/answer/Edwin-Chen-1
Large Scale Decision Forests: Lessons Learnedhttp://blog.siftscience.com/blog/2015/large-scale-decision-forests-lessons-learned
Unboxing the Random Forest Classifierhttp://nerds.airbnb.com/unboxing-the-random-forest-classifier/
Understanding Random Forests: From Theory to Practicehttp://arxiv.org/pdf/1407.7502v3.pdf
https://github.com/code-ram/DAT8#class-19-advanced-scikit-learn-and-clustering
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/19_advanced_sklearn.ipynb
StandardScalerhttp://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
Pipelinehttp://scikit-learn.org/stable/modules/pipeline.html
slideshttps://github.com/code-ram/DAT8/blob/master/slides/19_clustering.pdf
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/19_clustering.ipynb
documentationhttp://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
visualization 1http://tech.nitoyon.com/en/blog/2013/11/07/k-means/
visualization 2http://www.naftaliharris.com/blog/visualizing-k-means-clustering/
documentationhttp://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html
visualizationhttp://www.naftaliharris.com/blog/visualizing-dbscan-clustering/
Understanding the Bias-Variance Tradeoffhttp://scott.fortmann-roe.com/docs/BiasVariance.html
guiding questionshttps://github.com/code-ram/DAT8/blob/master/homework/09_bias_variance.md
bias-variance tradeoffhttp://work.caltech.edu/library/081.html
regularizationhttp://work.caltech.edu/library/121.html
feature scalinghttps://github.com/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb
Practical Data Science in Pythonhttp://radimrehurek.com/data_science_python/
GridSearchCV and RandomizedSearchCVhttp://scikit-learn.org/stable/modules/grid_search.html
How to find the best model parameters in scikit-learnhttps://www.youtube.com/watch?v=Gol_qOgRqfA
associated notebookhttps://github.com/justmarkham/scikit-learn-videos/blob/master/08_grid_search.ipynb
tutorials and exampleshttps://github.com/rasbt/pattern_classification
tools and extensionshttp://rasbt.github.io/mlxtend/
bookhttps://github.com/rasbt/python-machine-learning-book
bloghttp://sebastianraschka.com/blog/
mailing listhttps://www.mail-archive.com/scikit-learn-general@lists.sourceforge.net/index.html
Introduction to Data Mininghttp://www-users.cs.umn.edu/~kumar/dmbook/index.php
types of clusteringhttp://scikit-learn.org/stable/modules/clustering.html
PowerPoint presentationhttp://www2.research.att.com/~volinsky/DataMining/Columbia2011/Slides/Topic6-Clustering.ppt
K-means clusteringhttps://www.youtube.com/watch?v=aIybuNt9ps4&list=PL5-da3qGB5IBC-MneTc9oBZz0C6kNJ-f2
hierarchical clusteringhttps://www.youtube.com/watch?v=Tuuc9Y06tAc&list=PL5-da3qGB5IBC-MneTc9oBZz0C6kNJ-f2
hierarchical clusteringhttps://joyofdata.shinyapps.io/hclust-shiny/
mean shift clusteringhttp://spin.atomicobject.com/2015/05/26/mean-shift-clustering/
K-modes algorithmhttp://www.cs.ust.hk/~qyang/Teaching/537/Papers/huang98extensions.pdf
Python implementationhttps://github.com/nicodv/kmodes
A Statistical Analysis of the Work of Bob Rosshttp://fivethirtyeight.com/features/a-statistical-analysis-of-the-work-of-bob-ross/
data and Python codehttps://github.com/fivethirtyeight/data/tree/master/bob-ross
How a Math Genius Hacked OkCupid to Find True Lovehttp://www.wired.com/2014/01/how-to-hack-okcupid/all/
characteristics of your zip codehttp://www.esri.com/landing-pages/tapestry/
https://github.com/code-ram/DAT8#class-20-regularization-and-regular-expressions
notebookhttps://github.com/code-ram/DAT8/blob/master/notebooks/20_regularization.ipynb
Ridgehttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html
RidgeCVhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html
Lassohttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html
LassoCVhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html
LogisticRegressionhttp://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
Pipelinehttp://scikit-learn.org/stable/modules/pipeline.html
GridSearchCVhttp://scikit-learn.org/stable/modules/grid_search.html
Baltimore homicide datahttps://github.com/code-ram/DAT8/blob/master/data/homicides.txt
Regular expressions 101https://regex101.com/#python
Reference guidehttps://github.com/code-ram/DAT8/blob/master/code/20_regex_reference.py
Exercisehttps://github.com/code-ram/DAT8/blob/master/code/20_regex_exercise.py
A Few Useful Things to Know about Machine Learninghttp://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
Generalized Linear Modelshttp://scikit-learn.org/stable/modules/linear_model.html
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
ridge regressionhttps://www.youtube.com/watch?v=cSKzqb0EKS0&list=PL5-da3qGB5IB-Xdpj_uXJpLGiRfv9UVXI&index=6
lasso regressionhttps://www.youtube.com/watch?v=A5I1G1MfUmA&index=7&list=PL5-da3qGB5IB-Xdpj_uXJpLGiRfv9UVXI
original paperhttp://statweb.stanford.edu/~tibs/lasso/lasso.pdf
machine learning coursehttps://www.coursera.org/learn/machine-learning/
related lecture noteshttp://www.holehouse.org/mlclass/07_Regularization.html
notebookhttps://github.com/luispedro/PenalizedRegression/blob/master/PenalizedRegression.ipynb
Building Machine Learning Systems with Pythonhttps://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python
Cross Validated Q&Ahttps://stats.stackexchange.com/questions/69568/whether-to-rescale-indicator-binary-dummy-predictors-for-lasso
blog posthttp://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models
introductory lessonhttps://developers.google.com/edu/python/regular-expressions
videohttps://www.youtube.com/watch?v=kWyoYtvJpe4&index=4&list=PL5-da3qGB5IA5NwDxcEJ5dvt8F9OQP7q5
chapterhttp://www.pythonlearn.com/html-270/book012.html
mbox.txthttp://www.py4inf.com/code/mbox.txt
mbox-short.txthttp://www.py4inf.com/code/mbox-short.txt
Breaking the Ice with Regular Expressionshttps://www.codeschool.com/courses/breaking-the-ice-with-regular-expressions/
RexEgghttp://www.rexegg.com/
5 Tools You Didn't Know That Use Regular Expressionshttp://blog.codeschool.io/2015/07/30/5-tools-you-didnt-know-that-use-regular-expressions/
Exploring Expressions of Emotions in GitHub Commit Messageshttp://geeksta.net/geeklog/exploring-expressions-emotions-github-commit-messages/
Emojineeringhttp://instagram-engineering.tumblr.com/post/118304328152/emojineering-part-2-implementing-hashtag-emoji
https://github.com/code-ram/DAT8#class-21-course-review-and-final-project-presentation
Data science reviewhttps://docs.google.com/document/d/19gBCkmrbMpFFLPX8wa5daMnyl7J5BXhMV8JNJwgp1pk/edit?usp=sharing
machine learning maphttp://scikit-learn.org/stable/tutorial/machine_learning_map/
Choosing a Machine Learning Classifierhttp://blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier/
Classifier comparisonhttp://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
Comparing supervised learning algorithmshttp://www.dataschool.io/comparing-supervised-learning-algorithms/
Supervised learning superstitions cheat sheethttp://ryancompton.net/assets/ml_cheat_sheet/supervised_learning.html
Machine Learning Done Wronghttp://ml.posthaven.com/machine-learning-done-wrong
Machine Learning Gremlinshttps://www.youtube.com/watch?v=tleeC-KlsKA
Clever Methods of Overfittinghttp://hunch.net/?p=22
Common Pitfalls in Machine Learninghttp://danielnee.com/?p=155
Practical machine learning tricks from the KDD 2011 best industry paperhttp://blog.david-andrzejewski.com/machine-learning/practical-machine-learning-tricks-from-the-kdd-2011-best-industry-paper/
Advice for applying machine learninghttp://cs229.stanford.edu/materials/ML-advice.pdf
An Empirical Comparison of Supervised Learning Algorithmshttp://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf
talkhttp://videolectures.net/solomon_caruana_wslmw/
https://github.com/code-ram/DAT8#class-22-final-project-presentation
What's next?https://github.com/code-ram/DAT8/blob/master/other/advice.md
https://github.com/code-ram/DAT8#additional-resources-1
https://github.com/code-ram/DAT8#tidy-data
Good Data Management Practices for Data Analysishttps://www.prometheusresearch.com/good-data-management-practices-for-data-analysis-tidy-data-part-2/
Hadley Wickham's paperhttp://www.jstatsoft.org/article/view/v059i10
Bob Rosshttps://github.com/fivethirtyeight/data/blob/master/bob-ross/elements-by-episode.csv
NFL ticket priceshttps://github.com/fivethirtyeight/data/blob/master/nfl-ticket-prices/2014-average-ticket-price.csv
airline safetyhttps://github.com/fivethirtyeight/data/blob/master/airline-safety/airline-safety.csv
Jets ticket priceshttps://github.com/fivethirtyeight/data/blob/master/nfl-ticket-prices/jets-buyer.csv
Chipotle ordershttps://github.com/TheUpshot/chipotle/blob/master/orders.tsv
unreadable by computershttps://bosker.wordpress.com/2014/12/05/the-government-statistical-services-terrible-spreadsheet-advice/
tips for releasing data in spreadsheetshttp://www.clean-sheet.org/
answerhttp://stats.stackexchange.com/questions/83614/best-practices-for-creating-tidy-data/83711#83711
https://github.com/code-ram/DAT8#databases-and-sql
GA slide deckhttps://github.com/justmarkham/DAT5/blob/master/slides/20_sql.pdf
Python scripthttps://github.com/justmarkham/DAT5/blob/master/code/20_sql.py
SQL Bootcamphttps://github.com/brandonmburroughs/sql_bootcamp
GA notebookhttps://github.com/podopie/DAT18NYC/blob/master/classes/17-relational_databases.ipynb
SQLZOOhttp://sqlzoo.net/wiki/SQL_Tutorial
Mode Analyticshttp://sqlschool.modeanalytics.com/
Khan Academyhttps://www.khanacademy.org/computing/computer-programming/sql
Codecademyhttps://www.codecademy.com/courses/learn-sql
Datamonkeyhttp://datamonkey.pro/guess_sql/lessons/
Code Schoolhttp://campus.codeschool.com/courses/try-sql/contents
advanced tutorialhttps://www.codeschool.com/courses/the-sequel-to-sql/
w3schoolshttp://www.w3schools.com/sql/trysql.asp?filename=trysql_select_all
Reddit Commentshttps://www.kaggle.com/c/reddit-comments-may-2015/data
What Every Data Scientist Needs to Know about SQLhttp://joshualande.com/data-science-sql/
Introduction to SQL for Data Scientistshttp://bensresearch.com/downloads/SQL.pdf
10 Easy Steps to a Complete Understanding of SQLhttps://web.archive.org/web/20150402234726/http://tech.pro/tutorial/1555/10-easy-steps-to-a-complete-understanding-of-sql
Query Planninghttp://www.sqlite.org/queryplanner.html
A Comparison Of Relational Database Management Systemshttps://www.digitalocean.com/community/tutorials/sqlite-vs-mysql-vs-postgresql-a-comparison-of-relational-database-management-systems
14 mini-courseshttps://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about
Blazehttp://blaze.pydata.org
https://github.com/code-ram/DAT8#recommendation-systems
GA slide deckhttps://github.com/justmarkham/DAT4/blob/master/slides/18_recommendation_engines.pdf
Python scripthttps://github.com/justmarkham/DAT4/blob/master/code/18_recommenders_soutions.py
Mining of Massive Datasetshttp://infolab.stanford.edu/~ullman/mmds/bookL.pdf
A Programmer's Guide to Data Mininghttp://guidetodatamining.com/
Netflix Recommendations: Beyond the 5 starshttp://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html
Winning the Netflix Prize: A Summaryhttp://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
A Perspective on the Netflix Prizehttp://www2.research.att.com/~volinsky/papers/chance.pdf
paperhttp://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
Stack Overflow Q&Ahttp://stackoverflow.com/questions/2323768/how-does-the-amazon-recommendation-feature-work
Facebookhttps://code.facebook.com/posts/861999383875667/recommending-items-to-more-than-a-billion-people/
Etsyhttps://codeascraft.com/2014/11/17/personalized-recommendations-at-etsy/
The Global Network of Discoveryhttp://www.gnod.com/
The People Inside Your Machinehttp://www.npr.org/blogs/money/2015/01/30/382657657/episode-600-the-people-inside-your-machine
coursehttps://www.coursera.org/learn/recommender-systems
Readme https://github.com/code-ram/DAT8#readme-ov-file
Please reload this pagehttps://github.com/code-ram/DAT8
Activityhttps://github.com/code-ram/DAT8/activity
0 starshttps://github.com/code-ram/DAT8/stargazers
0 watchinghttps://github.com/code-ram/DAT8/watchers
0 forkshttps://github.com/code-ram/DAT8/forks
Report repository https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fcode-ram%2FDAT8&report=code-ram+%28user%29
Releaseshttps://github.com/code-ram/DAT8/releases
Packages 0https://github.com/users/code-ram/packages?repo_name=DAT8
https://github.com
Termshttps://docs.github.com/site-policy/github-terms/github-terms-of-service
Privacyhttps://docs.github.com/site-policy/privacy-policies/github-privacy-statement
Securityhttps://github.com/security
Statushttps://www.githubstatus.com/
Communityhttps://github.community/
Docshttps://docs.github.com/
Contacthttps://support.github.com?tags=dotcom-footer

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