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https://github.com/hack-hyc/DAT4#dat4-course-repository
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
Starbucks at 15th & Khttp://www.yelp.com/biz/starbucks-washington-15
Course Project informationhttps://github.com/hack-hyc/DAT4/blob/master/project.md
Introductionhttps://github.com/hack-hyc/DAT4#class-1-introduction
Pythonhttps://github.com/hack-hyc/DAT4#class-2-python
Getting Datahttps://github.com/hack-hyc/DAT4#class-3-getting-data
Git and GitHubhttps://github.com/hack-hyc/DAT4#class-4-git-and-github
Pandashttps://github.com/hack-hyc/DAT4#class-5-pandas
Numpy, Machine Learning, KNNhttps://github.com/hack-hyc/DAT4#class-6-numpy-machine-learning-knn
scikit-learn, Model Evaluation Procedureshttps://github.com/hack-hyc/DAT4#class-7-scikit-learn-model-evaluation-procedures
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Logistic Regression,Preview of Other Modelshttps://github.com/hack-hyc/DAT4#class-9-logistic-regression-preview-of-other-models
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Working a Data Problemhttps://github.com/hack-hyc/DAT4#class-11-working-a-data-problem
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Naive Bayeshttps://github.com/hack-hyc/DAT4#class-13-naive-bayes
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Databases and MapReducehttps://github.com/hack-hyc/DAT4#class-17-databases-and-mapreduce
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Project Presentationshttps://github.com/hack-hyc/DAT4#class-22-project-presentations
https://github.com/hack-hyc/DAT4#installation-and-setup
Anaconda distributionhttp://continuum.io/downloads
Githttp://git-scm.com/book/en/v2/Getting-Started-Installing-Git
GitHubhttps://github.com/
Slackhttps://slack.com/
https://github.com/hack-hyc/DAT4#class-1-introduction
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/01_course_overview.pdf
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/01_intro_to_data_science.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code/00_python_refresher.py
Analyzing the Analyzershttp://cdn.oreillystatic.com/oreilly/radarreport/0636920029014/Analyzing_the_Analyzers.pdf
Data Community DC newsletterhttp://www.datacommunitydc.org/thenewsletter/
event calendarhttp://www.datacommunitydc.org/calendar
https://github.com/hack-hyc/DAT4#class-2-python
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project pagehttps://github.com/hack-hyc/DAT4/blob/master/project.md
projects from past Data Science courseshttps://github.com/justmarkham/DAT-project-examples
A Crash Course in Pythonhttp://nbviewer.ipython.org/gist/rpmuller/5920182
Codecademy's Python coursehttp://www.codecademy.com/en/tracks/python
Google's Python Classhttps://developers.google.com/edu/python/
student projectshttp://cs229.stanford.edu/projects2013.html
Machine Learning coursehttp://cs229.stanford.edu/
Online Python Tutorhttp://pythontutor.com/
https://github.com/hack-hyc/DAT4#class-3-getting-data
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excellent regex lessonhttps://developers.google.com/edu/python/regular-expressions
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three-part tutorialhttp://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
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visualization pagehttp://pandas.pydata.org/pandas-docs/stable/visualization.html
notebook on matplotlibhttp://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section2_4-Matplotlib.ipynb
Choosing a Good Charthttp://www.extremepresentation.com/uploads/documents/choosing_a_good_chart.pdf
slide deckhttp://www2.research.att.com/~volinsky/DataMining/Columbia2011/Slides/Topic2-EDAViz.ppt
https://github.com/hack-hyc/DAT4#class-6-numpy-machine-learning-knn
codehttps://github.com/hack-hyc/DAT4/blob/master/code/06_numpy.py
codehttps://github.com/hack-hyc/DAT4/blob/master/code/06_iris_prework.py
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slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/06_ml_knn.pdf
Understanding the Bias-Variance Tradeoffhttp://scott.fortmann-roe.com/docs/BiasVariance.html
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
https://github.com/hack-hyc/DAT4#class-7-scikit-learn-model-evaluation-procedures
codehttps://github.com/hack-hyc/DAT4/blob/master/code/07_sklearn_knn.py
user guidehttp://scikit-learn.org/stable/modules/neighbors.html
module referencehttp://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors
class documentationhttp://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
articlehttp://scott.fortmann-roe.com/docs/BiasVariance.html
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/07_model_evaluation_procedures.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code/07_model_evaluation_procedures.py
data exploration and analysis planhttps://github.com/hack-hyc/DAT4/blob/master/project.md
UCI Machine Learning Repositoryhttp://archive.ics.uci.edu/ml/datasets.html
Glass Identification Data Sethttp://archive.ics.uci.edu/ml/datasets/Glass+Identification
30-second explanation of overfittinghttp://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su
overfitting and train/test splithttps://www.youtube.com/watch?v=_2ij6eaaSl0
cross-validationhttps://www.youtube.com/watch?v=nZAM5OXrktY
An Introduction to Statistical Learninghttp://www-bcf.usc.edu/~gareth/ISL/
excellent, simple example of the bias-variance tradeoffhttp://work.caltech.edu/library/081.html
https://github.com/hack-hyc/DAT4#class-8-linear-regression
IPython notebookhttp://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb
data exploration and analysis planhttps://github.com/hack-hyc/DAT4/blob/master/project.md
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/
simple linear regressionhttp://www.datarobot.com/blog/ordinary-least-squares-in-python/
multiple linear regressionhttp://www.datarobot.com/blog/multiple-regression-using-statsmodels/
introduction to linear regressionhttp://people.duke.edu/~rnau/regintro.htm
assumptions of linear regressionhttp://pareonline.net/getvn.asp?n=2&v=8
https://github.com/hack-hyc/DAT4#class-9-logistic-regression-preview-of-other-models
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/09_logistic_regression.pdf
exercisehttps://github.com/hack-hyc/DAT4/blob/master/code/09_logistic_regression_exercise.py
solutionhttps://github.com/hack-hyc/DAT4/blob/master/code/09_logistic_regression_class.py
first three videoshttps://www.youtube.com/playlist?list=PL5-da3qGB5IC4vaDba5ClatUmFppXLAhE
relationship between probability, odds, and log-oddshttp://www.ats.ucla.edu/stat/mult_pkg/faq/general/odds_ratio.htm
intuition behind "e"http://betterexplained.com/articles/an-intuitive-guide-to-exponential-functions-e/
interpreting logistic regression coefficientshttp://www.unm.edu/~schrader/biostat/bio2/Spr06/lec11.pdf
https://github.com/hack-hyc/DAT4#class-10-model-evaluation-metrics
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/07_model_evaluation_procedures.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code/07_model_evaluation_procedures.py
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/10_model_evaluation_metrics.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code/10_rmse.py
codehttps://github.com/hack-hyc/DAT4/blob/master/code/10_confusion_roc.py
videohttps://www.youtube.com/watch?v=OAl6eAyP-yo
Model evaluation homeworkhttps://github.com/hack-hyc/DAT4/blob/master/homework/10_model_evaluation.md
Sample solution codehttps://github.com/hack-hyc/DAT4/blob/master/code/10_glass_id_homework_solution.py
Kaggle project presentation videohttps://www.youtube.com/watch?v=HGr1yQV3Um0
Smart Autofillhttp://googleresearch.blogspot.com/2014/10/smart-autofill-harnessing-predictive.html
Kaggle Transforms Data Science Into Competitive Sporthttps://www.youtube.com/watch?v=8w4UY66GKcM
model evaluationhttp://scikit-learn.org/stable/modules/model_evaluation.html
model evaluation metricshttps://www.kaggle.com/wiki/Metrics
simple guide to confusion matrix terminologyhttp://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
blog post about the ROC videohttp://www.dataschool.io/roc-curves-and-auc-explained/
Sensitivity and Specificityhttps://www.youtube.com/watch?v=U4_3fditnWg&list=PL41ckbAGB5S2PavLIXUETzAmi5reIod23
ROC Curveshttps://www.youtube.com/watch?v=21Igj5Pr6u4&list=PL41ckbAGB5S2PavLIXUETzAmi5reIod23
https://github.com/hack-hyc/DAT4#class-11-working-a-data-problem
datahttps://github.com/hack-hyc/DAT4/blob/master/data/ZYX_prices.csv
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/11_GA_Stocks.pdf
https://github.com/hack-hyc/DAT4#class-12-clustering-and-visualization
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/12_clustering.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code
datahttps://github.com/hack-hyc/DAT4/blob/master/data/songs.csv
A Plan for Spamhttp://www.paulgraham.com/spam.html
Kevin's guidehttp://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
excellent videohttps://www.youtube.com/watch?v=U4_3fditnWg&list=PL41ckbAGB5S2PavLIXUETzAmi5reIod23
introductory slideshttps://docs.google.com/presentation/d/1cM2dVbJgTWMkHoVNmYlB9df6P2H8BrjaqAcZTaLe9dA/edit#slide=id.gfc3caad2_00
OpenIntro Statistics textbookhttps://www.openintro.org/stat/textbook.php
Introduction to Data Mininghttp://www-users.cs.umn.edu/~kumar/dmbook/index.php
chapter on cluster analysishttp://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf
section on clusteringhttp://scikit-learn.org/stable/modules/clustering.html
https://github.com/hack-hyc/DAT4#class-13-naive-bayes
A Plan for Spamhttp://www.paulgraham.com/spam.html
Slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/13_naive_bayes.pdf
Visualization of conditional probabilityhttp://setosa.io/conditional/
codehttps://github.com/hack-hyc/DAT4/blob/master/code/13_bayes_iris.py
Slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/13_naive_bayes.pdf
Airport security examplehttp://www.quora.com/In-laymans-terms-how-does-Naive-Bayes-work/answer/Konstantin-Tt
codehttps://github.com/hack-hyc/DAT4/blob/master/code/13_naive_bayes.py
SMS Spam Collectionhttps://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
CountVectorizerhttp://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
Naive Bayeshttp://scikit-learn.org/stable/modules/naive_bayes.html
Visualizing Bayes' theoremhttp://oscarbonilla.com/2009/05/visualizing-bayes-theorem/
Bayes' Rule for Duckshttps://planspacedotorg.wordpress.com/2014/02/23/bayes-rule-for-ducks/
5-minute video on conditional probabilityhttps://www.youtube.com/watch?v=Zxm4Xxvzohk
slides on conditional probabilityhttps://docs.google.com/presentation/d/1psUIyig6OxHQngGEHr3TMkCvhdLInnKnclQoNUr4G4U/edit#slide=id.gfc69f484_00
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
his follow-up articlehttp://www.paulgraham.com/better.html
related paperhttp://www.merl.com/publications/docs/TR2004-091.pdf
https://github.com/hack-hyc/DAT4#class-14-natural-language-processing
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/14_natural_language_processing.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code/14_nlp_class.py
Natural Language Processing with Pythonhttp://www.nltk.org/book/
NLP online coursehttps://www.coursera.org/course/nlp
video lectureshttps://class.coursera.org/nlp/lecture
slideshttp://web.stanford.edu/~jurafsky/NLPCourseraSlides.html
Brief slideshttp://files.meetup.com/7616132/DC-NLP-2013-09%20Charlie%20Greenbacker.pdf
Detailed slideshttps://github.com/ga-students/DAT_SF_9/blob/master/16_Text_Mining/DAT9_lec16_Text_Mining.pdf
A visual survey of text visualization techniqueshttp://textvis.lnu.se/
DC Natural Language Processinghttp://www.meetup.com/DC-NLP/
Stanford CoreNLPhttp://nlp.stanford.edu/software/corenlp.shtml
Python introductory lessonhttps://developers.google.com/edu/python/regular-expressions
reference guidehttps://github.com/justmarkham/DAT3/blob/master/code/99_regex_reference.py
real-time regex testerhttps://regex101.com/#python
in-depth tutorialshttp://www.rexegg.com/
SpaCyhttp://honnibal.github.io/spaCy/
https://github.com/hack-hyc/DAT4#class-15-decision-trees
IPython notebookhttp://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/15_decision_trees.ipynb
these guidelineshttps://github.com/hack-hyc/DAT4/blob/master/peer_review.md
Decision Treeshttp://scikit-learn.org/stable/modules/tree.html
Download and install PKG filehttp://www.graphviz.org/Download_macos.php
Download and install MSI filehttp://www.graphviz.org/Download_windows.php
https://github.com/hack-hyc/DAT4#class-16-ensembling
IPython notebookhttp://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/16_ensembling.ipynb
Ensemble Methodshttp://scikit-learn.org/stable/modules/ensemble.html
How do random forests work in layman's terms?http://www.quora.com/How-do-random-forests-work-in-laymans-terms/answer/Edwin-Chen-1
https://github.com/hack-hyc/DAT4#class-17-databases-and-mapreduce
database codehttps://github.com/hack-hyc/DAT4/blob/master/code/17_sql.py
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/17_db_mr.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code/17_map_reduce.py
Forbes: Is it Time for Hadoop Alternatives?http://www.forbes.com/sites/johnwebster/2014/12/08/is-it-time-for-hadoop-alternatives/
IBM: What is MapReduce?http://www-01.ibm.com/software/data/infosphere/hadoop/mapreduce/
Wakari MapReduce IPython notebookhttps://www.wakari.io/sharing/bundle/nkorf/MapReduce%20Example
What Every Data Scientist Needs to Know about SQLhttp://joshualande.com/data-science-sql/
Brandon's SQL Bootcamphttps://github.com/brandonmburroughs/sql_bootcamp
SQLZOOhttp://sqlzoo.net/wiki/Main_Page
Mode Analyticshttp://sqlschool.modeanalytics.com/
https://github.com/hack-hyc/DAT4#class-18-recommenders
slideshttps://github.com/hack-hyc/DAT4/blob/master/slides/18_recommendation_engines.pdf
codehttps://github.com/hack-hyc/DAT4/blob/master/code/18_recommenders_class.py
The Netflix Prizehttp://www.netflixprize.com/
Why Netflix never implemented the winning solutionhttps://www.techdirt.com/blog/innovation/articles/20120409/03412518422/why-netflix-never-implemented-algorithm-that-won-netflix-1-million-challenge.shtml
Visualization of the Music Genome Projecthttp://www.music-map.com/
The People Inside Your Machinehttp://www.npr.org/blogs/money/2015/01/30/382657657/episode-600-the-people-inside-your-machine
https://github.com/hack-hyc/DAT4#class-19-advanced-scikit-learn
codehttps://github.com/hack-hyc/DAT4/blob/master/code/19_advanced_sklearn.py
GridSearchCVhttp://scikit-learn.org/stable/modules/grid_search.html
StandardScalerhttp://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
Pipelinehttp://scikit-learn.org/stable/modules/pipeline.html
notebookhttp://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/19_regularization.ipynb
Ridge, RidgeCV, Lasso, LassoCVhttp://scikit-learn.org/stable/modules/linear_model.html
LogisticRegressionhttp://scikit-learn.org/stable/modules/linear_model.html
RFE, RFECVhttp://scikit-learn.org/stable/modules/feature_selection.html
A Few Useful Things to Know about Machine Learninghttp://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
feature scalinghttp://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb
Clever Methods of Overfittinghttp://hunch.net/?p=22
Common Pitfalls in Machine Learninghttp://danielnee.com/?p=155
https://github.com/hack-hyc/DAT4#class-20-course-review
Data science reviewhttps://docs.google.com/document/d/1XCdyrsQwU5OC5os7RHdVTEtS-tpHBbsoKKWLpYI6Svo/edit?usp=sharing
Comparing supervised learning algorithmshttps://docs.google.com/spreadsheets/d/15_QJXm6urctsbIXO-C_eXrsSffbHedio8z0E5ozxO-M/edit?usp=sharing
Choosing a Machine Learning Classifierhttp://blog.echen.me/2011/04/27/choosing-a-machine-learning-classifier/
scikit-learn "machine learning map"http://scikit-learn.org/stable/tutorial/machine_learning_map/
Machine Learning Done Wronghttp://ml.posthaven.com/machine-learning-done-wrong
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/
An Empirical Comparison of Supervised Learning Algorithmshttp://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf
Getting in Shape for the Sport of Data Sciencehttps://www.youtube.com/watch?v=kwt6XEh7U3g
Resources for continued learning!https://github.com/hack-hyc/DAT4/blob/master/resources.md
https://github.com/hack-hyc/DAT4#class-21-project-presentations
https://github.com/hack-hyc/DAT4#class-22-project-presentations
Readme https://github.com/hack-hyc/DAT4#readme-ov-file
Please reload this pagehttps://github.com/hack-hyc/DAT4
Activityhttps://github.com/hack-hyc/DAT4/activity
0 starshttps://github.com/hack-hyc/DAT4/stargazers
1 watchinghttps://github.com/hack-hyc/DAT4/watchers
0 forkshttps://github.com/hack-hyc/DAT4/forks
Report repository https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fhack-hyc%2FDAT4&report=hack-hyc+%28user%29
Releaseshttps://github.com/hack-hyc/DAT4/releases
Packages 0https://github.com/users/hack-hyc/packages?repo_name=DAT4
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/
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