**Texts**

- S.Few [2012],
*Show Me the Numbers: Designing Tables and Graphs to Enlighten*, Amazon.ca - S.Few [2009],
*Now You See It: Simple Visualization Techniques for Quantitative Analysis*, Amazon.ca - D.M.Wong [2013],
*The Wall Street Journal Guide to Information Graphics: The Do’s And Don’ts Of Presenting Data Facts And Figures*, Amazon.ca - C.Nussbaumer Knaflic [2015],
*Storytelling with Data: A Data Visualization Guide for Business Professionals Paperback*, Amazon.ca - N.Yau [2011],
*Visualize This: The FlowingData Guide to Design, Visualization, and Statistics Paperback*, Amazon.ca - N.Yau [2013],
*Data Points: Visualization That Means Something*, Amazon.ca - E.R.Tufte [2006],
*Beautiful Evidence*, Amazon.ca - E.R.Tufte [2001],
*The Visual Display of Quantitative Information*, (2nd ed.), Amazon.ca - E.R.Tufte [1990],
*Envisioning Infortmation*, Amazon.ca - E.R.Tufte [1997],
*Visual Explanations: Images and Quantities, Evidence and Narrative*, Amazon.ca - D.Mccandless [2012],
*Visual Miscellaneum, The Revised And Updated: A Colorful Guide to the World’s Most Consequential Trivia*, Amazon.ca - D.Mccandless [2014],
*Knowledge Is Beautiful: A Visual Miscellaneum of Compelling Information*, Amazon.ca - N.Illinsky, J.Steele [2011],
*Designing Data Visualizations: Representing Informational Relationships*, Amazon.ca - H.Wainer [2009],
*Picturing the Uncertain World: How to Understand, Communicate, and Control Uncertainty Through Graphical Display*, Amazon.ca - W.Lefèvre, J.Renn, U.Shoepflin (eds.) [2003],
*The Power of Images in Early Modern Science*, Amazon.ca - P.Murrell [2006],
*R Graphics*, available online - J.Leek [2015],
*The Elements of Data Analytic Style*, leanpub - J.Avirgan [2016],
*The Map That May Unmask Banksy*, FiveThirtyEight - A.Bycoffe [2016],
*The Endorsement Primary*, FiveThirtyEight *2016 National Primary Polls*, FiveThirtyEight- N.Yau [2016],
*Data USA makes government data easier to explore*, Flowing Data - E.Lamb [2016],
*It Doesn’t Add Up*, - E.Lamb [2012],
*Abandoning Algebra Is Not the Answer*, Scientific American - E.Lamb [2016],
*Andrew Hacker and the Case of the Missing Trigonometry Question*, Scientific American - N.Yau [2016],
*Data Proofer automates the data checking process*, Flowing Data - K.Dutton, D.Abrams [2016],
*What Research Says about Defeating Terrorism*, Scientific American - C.Aschwanden [2016],
*Failure Is Moving Science Forward*, FiveThirtyEight - R.Matin, R.Azizi [2015],
*DEA with Missing Data: An Interval Data Assignment Approach*, JOIE - R.Wasserstein, N.Lazar [2016],
*The ASA’s statement on p-values: context, process, and purpose*, The American Statistician - T.Siegfried [2016],
*Experts issue warning on problems with P values*, Science News - R.Arthur [2016],
*We Now Have Algorithms To Predict Police Misconduct*, FiveThirtyEight - N.Yau [2016],
*What I Use to Visualize Data*, FlowingData - C.Aschwanden [2016],
*Statisticians Found One Thing They Can Agree On: It’s Time To Stop Misusing P-Values*, FiveThirtyEight - J.Honaker, G.King, M.Blackwell,
*Amelia II: A Program for Missing Data*, Gary King - M.Blackwell, J.Honaker, G.King
*A Unified Approach to Measurement Error and Missing Data: Overview and Applications*, - Y.Zhou, D.Wilkinson, R. Schreiber, R.Pan,
*Large-scale Parallel Collaborative Filtering for the Netflix Prize*, PDF - N.Yau [2016],
*Vega-Lite for quick online charts*, Flowing Data - B. D. CRAVEN, S. M. N. ISLAM [2005],
*Operations Research Methods*, Flowing Data - M.Panza, D.Napoletani, D.Struppa [2010],
*Agnostic Science. Towards a Philosophy of Data Analysis*, HAL - C.Paciorek [2014],
*An Introduction to Using Distributed File Systems and MapReduce through Spark*, - J.Cranshaw, R.Schwartz, J.Hong, N.Sadeh [2012],
*The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City*, *Life expectancy at birth*, Gapminder*Gapminder World 2012 in pdf*, Gapminder- K.Hsu, N.Pathak, J.Srivastava, G.Tschida, E.Bjorklund [2015],
*Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue*, - N.Yau [2010],
*Think Like a Statistician – Without the Math*, Flowing Data - N.Lorang [2016],
*Data scientists mostly just do arithmetic and that’s a good thing*, - N.Yau [2010],
*Predictive policing*, - University of Minnesota Duluth
*Lectures*, *Artistic License – Statistics*, Tvtropes*23 Design, Data Visualization and Presentation Quotes from Edward Tufte*, Tvtropes- J. DeCoster [2001],
*Transforming and Restructuring Data*, - N.Yau [2016],
*Role of empathy in visualization*, Flowing Data *Center for Big Data Ethics, Law, and Policy*, Data Science Institute- A.Barry-Jester [2016],
*What Went Wrong In Flint*, FiveThirtyEight - J.Avirgan [2016],
*A History Of Data In American Politics (Part 2): Obama 2008 To The Present*, FiveThirtyEight - C.Bialik [2016],
*Why Betting Data Alone Can’t Identify Match Fixers In Tennis*, FiveThirtyEight - F.Jacobs [2016],
*A World Map of Economic Growth*, Big Think - W.Briggs [2016],
*Machine Learning, Big Data, Deep Learning, Data Mining, Statistics, Decision & Risk Analysis, Probability, Fuzzy Logic FAQ*, WILLIAM M. BRIGGS *Imagine Storing All the Worlds Archives in a Box of Seeds*, New Scientist- S.Few, [2011],
*The Chartjunk Debate*, - H.Enten [2015],
*Harry’s Guide To 2016 Election Polls*, FiveThirtyEight - A.Gefter [2015],
*A Private View of Quantum Reality*, Quanta Magazine - N.Yau [2015],
*R growth on StackOverflow reigns supreme*, Flowing Data - C.Bialik [2015],
*As A Major Retraction Shows, We’re All Vulnerable To Faked Data*, FiveThirtyEight *A European defense ministry revamps its logistics strategy and operations*, McKinsey&Company- JF.Portarrieu [2013],
*City of Toulouse*, IBM - K.Bonnes [2014],
*Predictive Analytics for Supply Chains: a Systematic Literature Review*, - J. Bencina [2011],
*Fuzzy Decision Trees as a Decision-making Framework in the Public Sector*, *The role of quantitative techniques in decision making process*, Essay UK- R.Larson [2002],
*Public Sector Operations Research: A Personal Journey*, *Real-Time Enterprise Stories*, Real Time Research REPORTS*Decision Science for Housing and Community Development: An interview with co-author Michael Johnson*, Statistics Views*City of Almere: Statistical analysis and predictive analytics allocate resources to citizens while planning for growth*, IBM*Woonbedrijf improves tenants’ quality of living*, IBM Software- M.Rockwell [2015],
*DHS to expedite data scans for foreign fighters*, FCW - M.Hansen, A.Stermberg [2015],
*NOAA’s Data Heads for the Clouds*, the White House - D.Major [2015],
*Open data, analytics key to Police Data Initiative*, GCN - L.Cornish [2015],
*Data in action: The role of data in humanitarian disasters*, Devex *Statisticians using social media to track foodborne illness and improve disaster response*, PHYS.ORG- Z.Mendelson [2015],
*Cities Can Use Big Data to Find Out What They Really Don’t Know*, Next City - N.Bishop [2015],
*Jen Q. Public: Governments can win the improper payment chase with analytics*, IBM - J.Shueh [2014],
*Minneapolis Launches Citywide Analytics Platform*, Government Technology - N.Bishop [2015],
*Public Sector News: How data and analytics promise a different future*, IBM - N.Bishop [2015],
*Public Sector News: The question of citizen’s privacy*, IBM - N.Bishop [2015],
*Public Sector News: How governments can unleash the power of analytics*, IBM - B.Cortez-Neavel [2015],
*Data Analytics, Prevention Efforts Could Drive Down Child Deaths*, The Chronicle of Social Change *The Benefits of Analytics in the Public Sector*, JMP- H.Nicol
*Local Government and digital services: options for improving local services*, Public Service Transformation Network - S.Bateman [2014]
*The Data Science in Government programme: using data in new ways to improve what government does*, GOV.UK *Big Data for Development: Technocratic & Democratic Considerations*, K- A.Syvajarvi, J.Stenvall
*Data mining in public and private sectors: organizational and government applications*, Google Books - M.Gasco [2012]
*Proceedings of the 12th European Conference on e-Government*, Google Books - Y.Zhao
*R and Data Mining: Examples and Case Studies*, Google Books - G. K. GUPTA
*Introduction to data mining with case studies*, Google Books - P.Putten, G.Melli, B.Kitts
*Data Mining Case Studies*, - M.Nguyen-Nielsen, et.al,
*Existing data sources for clinical epidemiology: Danish registries for studies of medical genetic diseases*, - N.Yau [2016]
*Using information graphics to calibrate bias*, Flowing Data *Accounting for Errors with a Non-Normal Distribution*, Engineering Statistics Handbook*Opinion Research Poll*, CNN Opinion Research- G.Dvorsky [2014]
*Computers are providing solutions to math problems that we can’t check*, iO9 *Missing-data imputation*, Stat Columbia- P. Allison [2012]
*Modern Methods for Missing Data*, Amstat - C.Wild [2012]
*The Wilcoxon Rank-Sum Test*, Stat Auckland - A.Pan, et.al [2013]
*Walnut Consumption Is Associated with Lower Risk of Type 2 Diabetes in Women*, The Journal of Nutrition - E.Inglis-Arkell [2012]
*Why the Exact Same Lottery Numbers Came Up Twice in One Week*, iO9 - H.Nolan [2014]
*Exonerations Are on the Rise. Justice Is Not.*, GAWKER - S.Nieuwenhuis, B.Forstmann, E.Wagenmakers [2011]
*Erroneous analyses of interactions in neuroscience: a problem of significance*, Nature NeuroScience *Significant*, Explain XKCD*Log Scale*, Explain XKCD- D.Hand [2014]
*Math Explains Likely Long Shots, Miracles and Winning the Lottery*, Scientific American - A.Koo [2013]
*A Decade After Moneyball, Have The A’s Found A New Market Inefficiency?*, Regressing - M.Enserink [2012]
*Fraud Detection Method Called Credible But Used Like an ‘Instrument of Medieval Torture’*, Science - R.Harder [2010]
*How To Generate Your Own Benford’s Law Numbers*, Think Harder - R.Nuzzo [2014]
*Scientific method: Statistical errors*, nature.com - I. JP [2014]
*Why most published research findings are false*, NCBI - D.Stapel, S.Lindenberg [2011]
*Coping with Chaos: How Disordered Contexts Promote Stereotyping and Discrimination*, Science - E.Callaway [2011]
*Report finds massive fraud at Dutch universities*, nature.com - E.Yong [2012]
*The data detective*, nature.com - E.Yong [2012]
*Replication studies: Bad copy*, nature.com *This Website Exposes a Scientific and Medical Cover Up*, nature.com- J.Walthoe
*This Website Exposes a Scientific and Medical Cover Up*, nature.com - J.Walthoe
*Looking out for number one*, +plus - A.Frazier et.al [2013]
*Prospective Study of Peripregnancy Consumption of Peanuts or Tree Nuts by Mothers and the Risk of Peanut or Tree Nut Allergy in Their Offspring*, JAMA Pediatric - R.Shapiro
*Prospective Study of Peripregnancy Consumption of Peanuts or Tree Nuts by Mothers and the Risk of Peanut or Tree Nut Allergy in Their Offspring*, JAMA Pediatric - J.Dempsey
*Our Army: Soldiers, Politics, and American Civil-Military Relations*, Princeton Press - K.Button et.al [2013]
*Power failure: why small sample size undermines the reliability of neuroscience*, nature.com - Public Health England [2014]
*Measles: guidance, data and analysis*, GOV.UK *The statisticians at Fox News use classic and novel graphical techniques to lead with data*, Simply Statistics- N.Yau [2011]
*Open thread: Can you spot the wrongness in this tax graph?*, Flowing Data - A. Hart
*Lies, damn lies, and the ‘Y’ axis*, Washington Post *A Guide for the statistically perplexed*, Polling*Lies, Damned Lies, and Statistics*, tvtropes*A Little Statistics is a Dangerous Thing*, TheNib- E.Inglis-Arkell [2014]
*The night the Gambler’s Fallacy lost people millions*, iO9 - E.Inglis-Arkell [2014]
*Statistics professor challenges midwives’ math on home birth safety*, iO9 - M.Cheyney, et.al [2014]
*Outcomes of Care for 16,924 Planned Home Births in the United States: The Midwives Alliance of North America Statistics Project, 2004 to 2009*, Wiley Online Library - R.Misra [2014]
*One graph explaining why you should always order a larger pizza*, iO9 - P.Clarke [2014]
*Title IX’s Other Effects: Do Sports Make Women Less Religious?*, Regressing - B.Barnwell [2014]
*Bridging the Analytics Gap*, Grantland - K.Wagner [2014]
*Two Days At Sloan: How Sports Analytics Got Lost In The Fog*, Regressing - M.Bruenig [2014]
*America’s Class System Across The Life Cycle*, Demos - G.Bluestone [2014]
*Casino Says World-Famous Gambler Cheated It Out of $10 Million*, GAWKER - R.Gonzalez [2014]
*Our New Favorite Website: Spurious Correlations*, iO9 - E.Inglis-Arkell [2014]
*One Mistake Fooled an Entire Nation About Who Would Be President*, iO9 - N.Yau [2014]
*Military infographic fascination*, iO9 - J.Raff [2014]
*How to Read and Understand a Scientific Paper: A Step-by-Step Guide for Non-Scientists*, Huffpost Science - J.Lepore [2014]
*The Disruption Machine*, The New Yorker - N.Yau [2014]
*Detailed UK census data browser*, Flowing Data - J.Pinto da Costa, L. Roque [2006]
*Limit Distribution for the Weighted Rank Correlation Coefficient*, REVSTAT - A.Weinstein [2014]
*Adam Weinstein’s Discussions*, GAWKER - D.Thompson [2014]
*The Misguided Freakout About Basement-Dwelling Millennials*, The Atlantic - R.Gonzalez [2014]
*Statistical Proof That Lionel Messi Is the Best Soccer Player On Earth*, iO9 - D.Mersereau [2014]
*Why Is a 30% Chance of Rain Different from a 30% Risk of Tornadoes?*, The Vane - S.Wolfram [2013]
*Data Science of the Facebook World*, Stephen Wolfram - B.Fung [2012]
*The Global Geography of HIV: 20 Years of Change—in 1 GIF*, The Atlantic - H.Brady [2013]
*Watch the Country Get Fatter in One Animated Map*, Slate - R.Gonzalez [2014]
*U.S. Remains Key Growth Market for Cigarettes, Despite Graphs Like This*, iO9 - A.Newitz [2014]
*Can Network Theory Help Explain Epic Mythology?*, iO9 - Hawkingdo [2014]
*I Solved Gerrymandering … sorta!*, GERRYMANDERING - N.Silver [2014]
*Should Travelers Avoid Flying Airlines That Have Had Crashes in the Past?*, FiveThirtyEight - B.Morris [2014]
*Billion-Dollar Billy Beane*, FiveThirtyEight - E.Lamb [2014]
*British Rail’s Shocking Defiance of Standard Metrics*, Scientific American - N.Yau [2014]
*How well we don’t understand probability*, Flowing Data - N.Silver [2010]
*BREAKING: Daily Kos to Sue Research 2000 for Fraud*, FiveThirtyEight - M.Strauss [2014]
*Statistician Creates Model To Predict What’s Next In Game Of Thrones*, iO9 - A.Burneko [2014]
*Numbers One Through 12, Ranked*, The Concourse - G.Dvorsky [2014]
*Why The Sudden Surge Of Retractions At Nature Magazine?*, iO9 - S.Burtch [2014]
*Hockey Analytics: Why They Help And What’s Coming Next*, SB Nation - R.Gonzalez [2014]
*How Much Would It Cost To Raise A Kid Like Calvin from Calvin and Hobbes?*, iO9 - Simply Statistics [2014]
*Data science can’t be point and click*, Simply Statistics - S.Corinaldi [2015]
*I created a bot to find love online – reader, it worked*, The Guardian - N.Yau [2015]
*The Elements of Data Analytic Style*, Flowing Data - N.Yau [2015]
*The Price is Right winner and cancer survivor calculates the odds*, Flowing Data - N.Yau [2015]
*Searching for stock market spoofers*, Flowing Data - C.Bialik [2015]
*Scare Headlines Exaggerated The U.S. Crime Wave*, FiveThirtyEight - J.Asher [2015]
*Murder Rates Don’t Tell Us Everything About Gun Violence*, FiveThirtyEight - R.Ehrenberg [2015]
*Analysis gives a glimpse of the extraordinary language of lying*, Science News - N.Yau [2015]
*The Most Regional Names in US History*, Flowing Data *Thanksgiving in Charts and Graphs*, The Gentlemans Armchair- N.Yau [2014]
*Lexical distance between European languages*, Flowing Data - P.Murrell [2014]
*R Graphics*, R Graphics - N.Yau [2010]
*How to visualize data with cartoonish faces ala Chernoff*, Flowing Data *A Critique of Chernoff Faces*, eagereyes- R.Misra [2014]
*6P.M. is the most dangerous time of day to be a pedestrian*, iO9 - C.Anders [2014]
*Fascinating Chart: Top 20 Metropolitan Areas in the U.S.A., 1790-2010*, iO9 - K.Wagner [2014]
*Every NBA Team’s Season, In One Chart*, Regressing - T.Ley [2014]
*Interactive Chart Finds Your New Favorite Beer For You*, FoodSpin - R.Misra [2014]
*A graph showing all the languages whose words invaded English*, iO9 - N.Yau [2014]
*How people really read and share online*, Flowing Data - R.Fischer-Baum [2014]
*Which Countries Have Produced The Most World-Famous Athletes?*, Regressing - N.Yau [2014]
*Level of road grid*, Flowing Data - N.Yau [2014]
*A visual analysis of the Boston subway system*, Flowing Data *Logistic Modeling with Categorical Predictors*, SAS*Stressed Out: Americans Tell Us About Stress In Their Lives*, NPR- N.Yau [2014]
*Polling for stress*, Flowing Data - B.Swihart, et.al
*Lasagna plots: A saucy alternative to spaghetti plots*, Lasagna plots *What’s the difference between an Infographic and a Data Visualisation?*, Jackhagley- J.Pavlus, et.al
*Infographic: If 7 Billion People Lived In One City, How Big Would It Be?*, Co.Design *Left vs Right v1.5*, Information is Beautiful- Mike [2011]
*Most Pirated Artists 2007 – 2010 Word Cloud*, The Evil Jam - M.Hahsler, S.Chelluboina

*Visualizing Association Rules: Introduction to the R-extension Package arulesViz*, Visualizing Association Rules - R.Misra [2014]
*An Interactive Chart Showing Which Jobs STEM Majors Really End Up In*, iO9 - N.Yau [2014]
*Markov Chains explained visually*, Flowing Data - M.Strauss [2014]
*Here’s What Your 1.1 Million Comments On Net Neutrality Look Like*, iO9 - N.Yau [2014]
*State of birth, by state and over time*, Flowing Data - N.Yau [2014]
*Finding small villages in big cities*, Flowing Data - G.Dvorsky [2014]
*These Simple Tips Will Make Your Science Visualizations Rock*, iO9 - M.Strauss [2014]
*Transforming Data Into Beer Could Be The Greatest Idea Ever*, iO9 - R.Misra [2015]
*What Visualization Best Illustrated A Tricky Scientific Concept For You?*, iO9 - N.Yau [2014]
*Real Chart Rules to Follow*, Flowing Data - N.Yau [2015]
*Bar Chart Baselines Start at Zero*, Flowing Data - N.Yau [2015]
*Venn Diagrams: Read and Use Them the Right Way*, Flowing Data - N.Yau [2015]
*Classic 1939 book on graphs in its entirety*, Flowing Data - N.Yau [2015]
*Weight loss and life events*, Flowing Data - N.Yau [2015]
*What probability means in different fields*, Flowing Data - N.Yau [2015]
*What Does Probability Mean in Your Profession?*, Math With Bad Drawings - N.Yau [2015]
*A timeline of history*, FlowingData *Left vs Right v1.5*, Information is Beautiful- N.Yau [2015]
*Work Counts*, FlowingData - N.Yau [2015]
*Most Common Use of Time, By Age and Sex*, FlowingData - A.Crossman [2016]
*Data Cleaning*, About Education *Data Cleaning*, Analysis*Top ten ways to clean your data*, Microsoft- R.Cody, et.al,
*Data Cleaning 101*, ucla - T.Orchard, M.Woodbury,
*A MISSING INFORMATION PRINCIPLE: THEORY AND APPLICATIONS*, Project Euclid

- P.Allison,
*Modern Methods for Missing Data*, amstat

*Regression diagnostics and cautions: outliers and influential points*, uoregon

- H.Wickham
*Tidy Data*, Journal of Statistical Software - V.Powell
*Conditional probability*, Setosa *Qualities of a Good Question*, StatPac*GOOD DATA FROM BAD QUESTIONS? IMPOSSIBLE!*, Cooperative Extension*Electronic Information Resources – Myth and Reality*, stsci- M.Püschel

*Small Guide to Making Nice Tables*, Carnegie Mellon - N.Yau [2014]
*The important parts of data analysis*, FlowingData - T.Hothorn, et.al,

[2006],*party: A Laboratory for Recursive Partytioning*, R package

- Z.Weinersmith

[2014],*An artificial one-liner generator*, Scientia salon - N.Webb

[2006],*Reliability Coefficients and Generalizability Theory*, handbook of statistics - A.Cernat [2013],
*The impact of mixing modes on reliability in longitudinal studies*, ESRC - B.Tran, C.Tucker [2010],
*Using Latent Class Models to Better Understand Reliability in Measures of Labor Force Status*, JSM 2010 - R.Fischer-Baum [2014],
*Charts: Your Spending Habits Get Lamer As You Age*, Regressing - G.Dvorsky [2014],
*20 Crucial Terms Every 21st Century Futurist Should Know*, iO9 - C.Proust-Lima, et.al,

[2016],*Package ‘lcmm’-Extended Mixed Models Using Latent Classes and Latent Processes* - Z.Bursac, et.al,

[2008],*Purposeful selection of variables in logistic regression* - Y.Zhang [2011],
*Dimension Reduction*, Dimension Reduction Slides *Organisational Core Values*, Organisational Core Values- N.Yau [2013],
*Getting started with visualization after getting started with visualization*, Flowing Data - B.Fry [2004],
*Computational Information Design*, Massachusetts Institute of Technology - N.Yau [2014],
*A more visual world data portal*, Flowing Data - S.Boriah, et.al,
*Similarity Measures for Categorical Data: A Comparative Evaluation*, University of Minnesota

- P.Allison
*What’s the Best R-Squared for Logistic Regression?*, statistical horizons *The curse of dimensionality*, The Shape of Data*Decision Trees*, The Shape of Data*Duality and Coclustering*, The Shape of Data- S.Fefilatyev, et.al,
*Detection of Anomalous Particles from Deepwater Horizon Oil Spill Using SIPPER3 Underwater Imaging Platform*, Proceedings Template – WORD *Pre-Crime Data Mining*, Pre-Crime Data Mining- A.Bellaachia, E.Guven
*Predicting Breast Cancer Survivability Using Data Mining Techniques*, Predicting Breast Cancer Survivability - J.Rath [2014],
*Data Scientists Predict Oscar Winners*, Data Center Knowledge - E.Lamb [2014],
*The Saddest Thing I Know about the Integers*, scientific american - V.Velickovic,
*What Everyone Should Know about Statistical Correlation*, american scientist - N.Silver
*Rich Data, Poor Data*, fivethirtyeight - A.Hoorfar, M.Hassant [2008],
*INEQUALITIES ON THE LAMBERT W FUNCTION AND HYPERPOWER FUNCTION*, JIPAM - Investopedia Staff,
*A Beginner’s Guide To Hedging*, investopedia - T.Yates,
*Practical And Affordable Hedging Strategies*, investopedia - M.Kang [2015],
*Exploring the 7 Different Types of Data Stories*, mediashift - H.Chen [2014],
*Curve Fitting & Multisensory Integration*, cogsci.ucsd.edu - T.Minka,
*Building statistical models by visualization*, Microsoft Research - Y.Zhao [2015],
*R and Data Mining: Examples and Case Studies*, r data mining - Y.Zhao [2015],
*Introduction to Data Mining with R*, r data mining - D. Meyer [2015],
*Support Vector Machines*, r-project - A.Fatahi [2010],
*TRUNCATED ZERO INFLATED BINOMIAL CONTROL CHART FOR MONITORING RARE HEALTH EVENTS*, IJRRAS

- A.Lazarevic, et.al, [2004],
*Data Mining for Analysis of Rare Events:A Case Study in Security, Financial and Medical Applications*, University of Minnesota Tutorial - D.Farace, J.Schöpfel,
*Grey Literature in Library and Information Studies*, DE GRUYTER *A Practical Guide to Statistics for Online Experiments*, optimizely

**Blogs and Sites**

- MulinBlog: a digital communication blog
- SportingCharts
- FlowingData
- Column Five Media
- Visual.ly
- eagereyes: Visualization and Visual Communication
- Quick-R: accessing the power of R
- New York Times’ The Upshot
- Nate Silver’s FiveThirtyEight
- Simply Statistics
- Using Visual Explanations to Create Learning: a research portfolio about visual explanations, learning and interactivity
- Martin Grandjean: Digital Humanities, Data Visualization, Network Analysis
- The Guardian US Interactive Team
- TULP Interactive
- SMBC Comics
- Daily Science Fiction
- Cracked
- NaCTeM
- Flowing Data: Books
- Visualization Books in the Queue
- Microsoft Research
- Venu’s Mushings
- Nedroid
- Image & Narrative
- Visualizing Science
- PKP
- OpenDOAR
- University of Glasgow
- IBM developWorks
- SAS
- Dataversity
- London School of Hygiene & Tropical Medicine
- IBM Big Data & Analytics Hub
- Statistics without Borders
- Next City
- IBM
- EJEG
- Publications and Media Library
- GreyNet International
- AMSER
- The Grey Literature Report
- Open Grey
- GreyNet
- Information is Beautiful
- Bloomberg
- Ptable
- MathTube
- Dell Software
- WolframMathWorld
- DEA Zone
- Stat Trek
- StatSci.org
- Research Utopia
- Bad Science
- Richard D. Gill’s home page
- nature.com
- deutsch29
- phD
- GOV.UK
- Data cuisine
- ESPN
- Information aesthetics
- Visualization Group
- GGobi
- ggplot2
- Foam Tree
- Smart-stats
- Lucidchart
- Fathom
- Information is Beautiful
- Gapminder
- Hive On Demand
- Information Visualization
- Lucas Infografia
- STEPHEN MCMURTRY
- eagereyes
- matplotlib
- Mike Bostock’s Blocks
- Plotly
- The Information Diet
- The University of Western Australia
- stanford vis group
- UW Interactive Data Lab
- Data is Beautiful
- R Project
- IBM Knowledge Center
- Weka
- UC Irvine Machine Learning Repository
- Datahub
- Historical Climate Data
- Ottawa’s Open Data Catalogue
- Information is Beautiful
- Quantum blog
- Trading with Python
- RESEARCH UTOPIA
- A Visual Introduction to Machine Learning
- data science central
- r data mining
- William Vorhies’s Blog
- Things Of Interest
- Databases covering grey literature and reports
- Grey Literature Report
- LexisNexis Searchable Directory of Online Sources
- The Quartz guide to bad data
- PennState Eberly College of Science
- Quantopian Blog
- Twiecki Github

**General**

*Data Visualization*, Wikipedia- R.Kosara [2008],
*What is a Visualization?*, eagleeyes *Rossmo’s formula*, Wikipedia*Similarity measure*, Wikipedia*Benford’s law*, Wikipedia*Kalman filter*, Wikipedia*IRIS Toolbox*, CodePlex*Naive Bayes classifier*, Wikipedia*K-means++*, Wikipedia*MovieLens*, Grouplens*Hands-on Exercises*, Spark*Stat 571: Statistical Methods*,*Bias*, Wikipedia*Determining the number of clusters in a data set*, Wikipedia*Time series*, Wikipedia*Category:Data clustering algorithms*, Wikipedia*Introducing Kaggle Datasets*, Kaggle*Awesome Public Datasets*, GitHub*The 2015 Data Awards*, FiveThirtyEight*Datasets for Data Mining*, The University of Edinburgh School of Informatics*The Data Science Industry: Who Does What (Infographic)*, Back to DataCamp*The Field Guide to Data Science*, Booz Allen Hamilton*Data Visualization*, FEMA*Farming Concrete Mill*, Farming Concrete Mill*Public Service Transformation Academy Launch*, FEMA*Danish Medical Data Distribution*, DMDD*UC Irvine Machine Learning Repository*, UCI*Mann–Whitney U test*, Wikipedia*Bootstrapping (statistics)*, Wikipedia*Predictive analytics*, Wikipedia*Quick-R*, Quick-R*Research Methods*, StatPac*Data analysis*, Wikipedia*Big data*, Wikipedia*Analytics*, Wikipedia*Validity (statistics)*, Wikipedia*Math Department*, Clackamas*Alcula*, Alcula*Theory of Correspondence Analysis*, Statmath- M.Bendixen [1996]
*A Practical Guide to the Use of Correspondence Analysis in Marketing Research*, Marketing Bulletin *One-way MANOVA in SPSS Statistics*, AERD Statistics*There are known knowns*, Wikipedia*Nate Silver Quotes*, goodreads*Nate Silver Quotes*, Brainy Quote*FiveThirtyEight*, Wikipedia*The Signal and the Noise*, Wikipedia*Moneyball*, Wikipedia*Fabrication (science)*, Wikipedia*Scientific misconduct*, Wikipedia*Data analysis techniques for fraud detection*, Wikipedia*How to be a Data Detective*, NPE*Bad Science (book)*, Wikipedia*Publishers withdraw more than 120 gibberish papers*, nature.com*Bokeh, a Python library for interactive visualization*, Flowing Data*Misleading graph*, Wikipedia*Treemapping*, Wikipedia*Heat map*, Wikipedia*Parallel coordinates*, Wikipedia*Box plot*, Wikipedia*Chernoff face*, Wikipedia*What is Visualization? A Definition*, eagereyes*Chernoff Face*, WolframMathWorld*Data visualization*, Wikipedia*Data grab bag*, Flowing Data*Edward Tufte*, Wikipedia*Statistics Calculator: Box Plot*, alcula*Statistics Calculator: Scatter Plot*, alcula*Statistics Calculator: Linear Regression*, alcula*50 Great Examples of Data Visualization*, Web Designer Depot*The 38 best tools for data visualization*, Creative Bloq/a>*Dominant Players*, XKCD*Treemap Basics*, Hive On Demand*What is a treemap? 5 examples and how you can create one*, Fishbowl NY*Where do college graduates work?*, United States Census Bureau*Announcing the Information is Beautiful Awards 2015*, Information is Beautiful- C.Chapman [2009]
*Data Visualization and Infographics Resources*, Smashing Magazine - V.Friedman [2007]
*Data Visualization: Modern Approaches*, Smashing Magazine - V.Friedman [2008]
*Data Visualization and Infographics*, Smashing Magazine - A.Marcelionis [2015]
*Fun With Physics In Data Visualization*, Smashing Magazine - A.Sahagun [2014]
*Data Visualization: Modern Approaches | Smashing Magazine*, ARI SAHAGÚN *Data Visualization: Modern Approaches*, Pearltrees*40 videos about data visualization*, Visualoop*Visualizations*, TED*A Tour through the Visualization Zoo*, acmqueue*Graph drawing*, wikipedia- S.Machlis [2015]
*LEARN TO USE R*, Learn to use R *Calendrier des formations*, solutionstat*Winners: Kantar Information is Beautiful Awards 2015*, Information is Beautiful*Misuse of statistics*, wikipedia*Statistics/Data Analysis/Data Cleaning*, wikibooks*Data cleansing*, wikipedia*Imputation*, wikipedia*Missing data*, wikipedia*Tidy data*, R-project*Sensitivity and specificity*, wikipedia*LaTeX/Tables*, wikibooks*Receiver operating characteristic*, wikibooks*Pearson’s chi-squared test*, wikipedia*Matthews correlation coefficient*, wikipedia*Statistical classification*, wikipedia*Multiclass classification*, wikipedia*Accuracy and precision*, wikipedia*Binary classification*, wikipedia*Classification chart*, wikipedia- C.Molnar [2012],
*Conditional Trees*, linkedIn *Conditional inference trees vs traditional decision trees*, StackExchange- N.Yau [2015],
*How to Make Smoothed Density Maps in R*, Flowing Data *LaTeX table capabilities*, StackExchange*Standard Procurement Templates*, Buyandsell.gc.ca- N.Yau [2014],
*Extract CSV data from PDF files with Tabula*, Flowing Data *Growth Mixture Modeling, Path Specification*, OpenMx*Multivariate statistics*, wikipedia*Tools for making latex tables in R*, StackExchange*Introductory Statistics*, Introductory Statistics*Six Sigma*, wikipedia- N.Yau [2014]
*Large-ish data packages in R*, Flowing Data *FOOD RESILIENCE*, data.gov*9 essential LaTeX packages everyone should use*, how to tex*How to extract text based on font color from a cell with text of multiple colors*, StackExchange*DATA SCIENCE CODE OF PROFESSIONAL CONDUCT*, Data Science*Data Science + Ethics*, Data Science + Ethics*When is small data better than big?*, Data Science Central- V.Ho
*Why Small Data May Be Bigger Than Big Data*, Inc. *Category:Data clustering algorithms*, wikipedia- N.Yau [2014],
*Curse of dimensionality, interactive demo*, Flowing Data - N.Yau [2014],
*A collection of small datasets*, Flowing Data *Lean Construction Special Issue*, The Search Guide- N.Yau [2014],
*Casual visualization books for the coffee table*, Flowing Data - N.Yau [2014],
*Planets as fruit to show scale*, Flowing Data *Law of total variance*, wikipedia*Structural equation modeling*, wikipedia*Bayesian linear regression*, wikipedia*Logistic regression*, wikipedia*List of cognitive biases*, wikipedia*Scale (social sciences)*, wikipedia*Index (economics)*, wikipedia*Least squares support vector machine*, wikipedia*Multivariate normal distribution*, wikipedia*German tank problem*, wikipedia*Doomsday argument*, wikipedia*Market neutral*, wikipedia*Hedge (finance)*, wikipedia*not-for-profit academic endeavor*, spliddit*CLARIFY YOUR DECISIONS*, darkhorse*Online Web of Science to bibTeX conversion*, Lagom.nl- N.Yau [2014],
*When data gets creepy*, Flowing Data - N.Yau [2014],
*Identifying cheaters in test results, a simple method*, Flowing Data - N.Yau [2015],
*R Cheat Sheet and Guide for Graphical Parameters*, Flowing Data - R.Nuzzo [2015],
*Scientists Perturbed by Loss of Stat Tools to Sift Research Fudge from Fact*, scientific american - N.Yau [2015],
*Problems with algorithmic policy-making*, Flowing Data - A.Marcus, I.Oransky [2015],
*How the Biggest Fabricator in Science Got Caught*, nautilus - N.Yau [2015],
*Fudging the crime statistics and police misconduct*, Flowing Data - T.Minka,
*Microsoft Research*, Microsoft Research - A. Vries, J.Meys
*How to Use the Clipboard to Copy and Paste Data in R*, For Dummies *How to fix libatk-1.0-0.dll error*, wikifixes*System for Information on Grey Literature in Europe*, wikipedia*Grey literature*, wikipedia*What is Grey Literature?*, greylit*Grey Literature?*, opengrey*SSM1100Y: Research Paper Course Guide*, U of T Libraries*Meta-analysis*, wikipedia*CRAN Task View: Meta-Analysis*, r project*Why perform a meta-analysis?*, Comprehensive Meta-Analysis*Meta-Analysis*, Study Design 101- J.Deeks,
*Analysing data and undertaking meta-analyses*, Cochrane Handbook - C.Aschwanden, [2015],
*Not Even Scientists Can Easily Explain P-values*, FiveThirtyEight *TMS Recovery Program*, tmswiki

**Survey and Sampling**

- Statistics Canada,
*National Population Health Survey: Household Component, Longitudinal (NPHS)*, StatCan - Statistics Canada,
*National Population Health Survey (NPHS), Cycle 1-9*, OPHID *National Population Health Survey Household Component*, Statistics Canada*National Population Health Survey Household Component Quesionnaire*, Statistics Canada*2000 National Population Health Survey (Cycle 4) Content for June 2000*, Statistics Canada*National Population Health Survey Household Component-Cycle 9-Quesionnaire*, Statistics Canada*Research Methods*, StatPac*Qualities of a Good Question*, StatPac*Good Data From Bad Questions? Impossible!*, Cooperative Extension- M.D’Orazio [2010],
*Evaluating reliability of combined responses through latent class models*, Istat *Air Carrier Traffic at Canadian Airports*, Statistics Canada*Air Carrier Traffic at Canadian Airports (51-203-X)*, Statistics Canada- N.Diakopoulos [2013],
*How Google Flu Trends Is Getting to the Bottom of Messy Data*, Harvard Business Review *Charitable giving by Canadians*, Statistics Canada*Charitable giving by Canadians Table 2*, Statistics Canada*Charitable giving by Canadians Table 3*, Statistics Canada*Charitable giving by Canadians Table 4*, Statistics Canada*Charitable giving by Canadians Table 7*, Statistics Canada*Charitable giving by Canadians Table 8*, Statistics Canada*Charitable giving by Canadians Table 9*, Statistics Canada*Charitable giving by Canadians Chart 1*, Statistics Canada*Charitable giving by Canadians Chart 2*, Statistics Canada*Charitable giving by Canadians Chart 3*, Statistics Canada*Charitable giving by Canadians Chart 4*, Statistics Canada

**Compilations**

- A.Shienkman [2015],
*Our 47 weirdest charts from 2015*, FiveThirtyEight - N.Yau [2015],
*10 Best Data Visualization Projects of 2015*, FlowingData

**Code to Produce Graphics**

- W.Chang [2013],
*R Graphics Cookbook*, Amazon.ca - J.Lander [2013],
*R for Everyone: Advanced Analytics and Graphics*, Amazon.ca *Star (Spider/Radar) Plots and Segment Diagrams*, R-Manual- N.Yau [2010],
*How to visualize data with cartoonish faces à la Chernoff*, FlowingData *Star Plots and Segment Diagrams of Multivariate Data*, Basic R package*Boxplots*, Quick-R- Bokeh, a Python interactive visualization library
- D3.js, is a JavaScript library for manipulating documents based on data,
- J. Zhang [2012],
*SUGI 29: Techniques for Generating Dynamic Code from SAS® DICTIONARY Data*, *Commonly Used Attribute Options*, SAS- S.Slaughter, L.Delwiche
*Using PROC SGPLOT for Quick High-Quality Graphs*, - P.Hebbar
*Off the Beaten Path: Create Unusual Graphs with GTL*, - N.Yau
*How to Make Bubble Charts*, Flowing Data - N.Yau
*Comparing ggplot2 and R Base Graphics*, Flowing Data - N.Yau
*Moving to the “worst” place in America*, Flowing Data *10 tips for making your R graphics look their best*, Revolutions- N.Lemoine
*R for Ecologists: Putting Together a Piecewise Regression*, R Bloggers - M.Friendly, E.Kwan, C.LaBrish [2016]
*Visualizing Categorical Data with SAS and R: Exercises*, Datavis - P.Burns [2011]
*The R Inferno*, Burns Stats - MicroSoft
*Present your data in a radar chart*, MicroSoft - Quick R
*Boxplots*, Quick R - Math UCLA
*Star Plots and Segment Diagrams of Multivariate Data*, Math UCLA - R Documentation
*Star Plots and Segment Diagrams*, R Documentation - B.Huang, et.al
*tourrGui: A gWidgets GUI for the Tour to Explore High-Dimensional Data Using Low-Dimensional Projections*, Journal of Statistical Software

- H.Wainer

*Improving Tabular Displays, with NAEP Tables as Examples and Inspirations*, Journal of Educational and Behavioral Statistics

- D.Cook

*How, when and why to use interactive and dynamic graphics*, Iowa State University

- R.Wicklin [2011]

*Visualizing correlations between variables in SAS*, The Do Loop - N.Yau [2014]
*How to Read and Use Histograms in R*, Flowing Data - N.Yau [2014]
*Accessible Web visuals and code with p5.js*, Flowing Data - N.Yau [2015]
*Horizon Graphs, with a Food Pricing Example*, Flowing Data - N.Yau [2015]
*How to Make Horizon Graphs in R*, Flowing Data - S.Machlis [2013]
*Beginner’s Guide to R: Painless Data Visulization*, computer world - H.Wickham [2011]
*ggplot2 basics*, ggplot2 basics - K.Rodden [2016]
*Sequences sunburst*, Sequences sunburst - M.Bostock [2016]
*Curved Links*, Curved Links - N.Yau [2015]
*Plotly.js, a JavaScript graphing library, open-sourced*, Flowing Data - N.Yau [2012]
*xkcd-style charts in R, JavaScript, and Python*, Flowing Data - S.Raschka [2014]
*Implementing a Principal Component Analysis in Python*, sebastianraschka *SAS:LOGISTIC Procedure*, SAS*Data-Driven Documents*, D3.js*Scatter Plots in Python*, Plotly*shapes_and_collections example code: scatter_demo.py*, matplotlib- H.Wickham, H.Hofmann
*Intro to R*, Intro to R *Line Charts*, Quick-R*Plotting earthquake data*, r-bloggers- J.Albert [2016]
*Graphing Pitch Count Effects*, Exploring Baseball Data with R *Plotting the Iris Data*, warwick*Code to create a scatterplot matrix*, ggplot2*Scatter Plot Matrices in R*, Data Analysis and Visualization Using R*R color cheatsheet*, R color cheatsheet*How to Make a Histogram with ggplot2*, r-bloggers*Histogram and density plot*, Cookbook for R- N.Horton [2011],
*Example 9.1: Scatterplots with binning for large datasets*, r-bloggers - K.Kleinman [2011],
*Example 8.41: Scatterplot with marginal histograms*, r-bloggers - D.Attali [2015],
*ggExtra: R package for adding marginal histograms to ggplot2*, r-bloggers - N.Zumel [2015],
*Wanted: A Perfect Scatterplot (with Marginals)*, r-bloggers - F.Veronesi [2013],
*Box-plot with R – Tutorial*, r-bloggers *Data Structures*, python

**General Code**

- SAS User Guide
- L. Gau,
*SAS Global Forum: Write SAS Code to Generate Another SAS Program*, - H.Wickham,
*Optimising code*, - P.Gill, E.Wong, [2014],
*Methods for Convex and General Quadratic Programming*, ucsd - C.Gohlke,
*Unofficial Windows Binaries for Python Extension Packages*, University of California, Irvine *Visualizing the distribution of a dataset*, stanford*Emacs Newbie Key Reference*, emacswiki- N.Yau [2015],
*Extract data from PDF files and export to CSV*, flowing data - J.Salvatier, et.al,
*Probabilistic Programming in Python using PyMC*, PyMC3 *Scatterplots*, Quick-R*Adding a legend to a plot*, r-bloggers*How I used R to create a word cloud, step by step*, Georeferenced*Axes and Text*, Quick-R*SVM example with Iris Data in R*, github*Cheatsheet – 11 Steps for Data Exploration in R (with codes)*, analytics vidhya- R.Hamer, P.Simpson,
*SAS Tools for Meta-Analysis*, SAS - C.Sheu, S.Suzuki, [2001],
*Meta-analysis using linear models*, citeseerx

- R.Butterfield, [2009],
*The Use of SAS in Meta-Analysis*, ncsu.edu - J.Gloudemans, et.al, [2011],
*MV_META: A SAS Macro for Multivariate Meta-Analysis*, SESUG 2011 - M.Komaroff, [2012],
*APPLICATION OF META-ANALYSIS IN CLINICAL TRIALS*, PharmaSUG - S.Kovalchik, [2013],
*Tutorial On Meta-Analysis In R*, R useR! Conference 2013 - A.C.Del Re, [2015],
*A Practical Tutorial on Conducting Meta-Analysis in R*, The Quantitative Methods for Psychology - J.Rickert, [2014],
*R and Meta-Analysis*, R bloggers

**Debugging and Common Questions**

- SAS
- StackFlow
- SAS
*Mathematics Questions*, stackexchange*Approximation for Lambert W function near zero*, stackexchange*pymc3*, github*How to customize lines in ggpairs*, stackoverflow*What are pseudo R-squareds*, Institute for Digital Research and Education*How do I interpret odds ratios in logistic regression*, Institute for Digital Research and Education

**Technical Details**

- M.Lin [2013],
*A color palette optimized for data visualization*, MulinBlog *PyMC3*,*Color Code*, Coolors*Color Code_R*,*Using colors in R*,

**Interactive/Dynamic/Animated Data Visualization**

*Keeping Up With the 2014 Winter Olympics*, Washington Post (member required for access).*Sochi 2014 Winter Olympic Games Calendar*, Sports Interaction- N.Yau [2016],
*How You Will Die*, FlowingData - K.Collins [2015],
*Why Infectious Bacteria are Winning*, Quartz - Bokeh, a Python interactive visualization library
- D3.js, is a JavaScript library for manipulating documents based on data
*You Draw It: How Family Income Predicts Children’s College Chances*, The Upshot, New York Times- R.Harris, N.Popovich, K.Powell [2015],
*Watch how the measles outbreak spreads when kids get vaccinated – and when they don’t*, The Guardian - S.Yee, T.Chu [2015],
*A Visual Introduction to Machine Learning, part 1*, R2D3.us - T.Randall, B.Migliozzi [2015],
*2014 Was the Hottest Year on Record*, Bloomberg - J.W.Tulp [2015],
*Goldilocks*, TULP Interactive *This is What the Spread of Walmart Looks Like From 1962 to 2006*, Cheezburger*Player Usage Charts*, Hockey Abstract- N.Yau [2015],
*Automatic charts and insights in Google Sheets*, FlowingData

**Heat Maps**

**Box Plots**

*Box Plot*, Wikipedia

**Parallel Coordinates/Spaghetti Plots**

*Parallel Coordinates*, Wikipedia

**Maps**

- N.Yau [2014],
*Where people run*, FlowingData. - N.Yau [2014],
*Amount of snow to cancel school*, FlowingData, reporting on redditor atrubetskoy’s map. - R.Masra [2014],
*A map of ?how much snow it takes to cancel school across the U.S.*, io9, reporting on redditor atrubetskoy’s map. - N.Yau [2013],
*The most regional names in US history*, FlowingData *An Unconventional Look at the European Map*, The Dialogue- N.Yau [2016],
*Changing river path seen through satellite images*, FlowingData - D.Walbert
*The mathematics of projections*, LEARN NC *This is What the Spread of Walmart Looks Like From 1962 to 2006*, Cheezburger- C.Maria [2014],
*Nine beautiful maps that will change how you see the world*, The Weather Network - A.Newitz [2014],
*Map shows which countries are contributing the most to climate change*, iO9 - G.Dvorsky [2014],
*An interactive map showing how baby names spread across the US*, iO9 *Many ways to see the world*, ODT Maps*Find all the countries of the world in the updated map*, Gapminder- N.Yau [2014]
*How to Make an Interactive Treemap*, Flowing Data - F.Jacobs
*Current Affairs: European Electricity Exports and Imports*, Big Think - A.Liptak [2015]
*Data Visualization Shows How Segregated Our Cities Are*, iO9 - L.Czerniewicz [2015]
*A World Map Based on Scientific Research Papers Produced*, iO9 - F.Jacobs
*The Map as Persuader*, Big Think *Plotting elevation maps and shaded relief images from latitude, longitude, and elevation pairs*, StackExchange*What Makes a Map Beautiful?*, StackExchange*A Model of Breast Cancer Causation*, Breast Cancer- N.Yau [2014],
*Explorations of People Movements*, Flowing Data - S.Sayad
*An Introduction to Data Mining*, Saedsayad - S.Lynn
*Self-Organising Maps for Customer Segmentation using R*, LinkedIn

**Text Analysis**

- K.Elliott, R.Johnson, T.Mellnik [2014],
*History through the president’s words*, Washington Post (membership required for access). - N.Yau[2016],
*The Guardian analyzes 70m comments, unearthing online abuse*, Flowing Data - P.Wong
*Visualizing Association Rules for Text Mining*, Visualizing Association Rules for Text Mining

**Queueing**

*Queueing Delay*,- Y.Abdelkader, M.Al-Wohaibi [2011],
*Computing the Performance Measures in Queueing Models via the Method of Order Statistics*, Journal of Applied Mathematics

**Data Envelopment Analysis**

**Time Series**

- O.Anava, E.Hazan, A.Zeevi [2015],
*Online Time Series Prediction with Missing Data*, - D.Fung [2006],
*Methods for the Estimation of Missing Values in Time Series*, - M.Vlachos [2005],
*A practical Time-Series Tutorial with MATLAB*, *Timeseries class*, MathWorks*Time Series Decomposition*, MathWorks*Parametric Trend Estimation*, MathWorks*Seasonal Adjustment Using S(n,m) Seasonal Filters*, MathWorks*Moving Average Trend Estimation*, MathWorks*Seasonal Adjustment Using a Stable Seasonal Filter*, MathWorks*Resample*, MathWorks*Seasonal Adjustment*, MathWorks- Statistics Austria, T.Wien [2012]
*Interactive adjustment and outlier detection of time dependent data in R*, Conference of European Statistians - B.Pecar [2012]
*Automating Time Series Analysis*, *PROC X12 Statement*, SAS- J.Honaker, G.King [2010]
*What to Do about Missing Values in Time-Series Cross-Section Data*, *Mann-Kendall Test For Monotonic Trend*,*Detrending*,*PROC X12 Example*, SAS- T.Jackson, M.Leonard
*Seasonal Adjustment Using the X12 Procedure*, *Working With Time Series Data*,- R.Peng [2016]
*Time Series Analysis in Biomedical Science – What You Really Need to Know*,

**Bayesian Analysis**

*Understanding empirical Bayes estimation (using baseball statistics)*,- J.Bowers, C.Davis,
*Bayesian Just-So Stories in Psychology and Neuroscience*, - J.Horgan
*Are Brains Bayesian?*, - H.Thornburg
*Introduction to Bayesian Statistics*, - E.Yudkowsky
*An Intuitive Explanation of Bayes’ Theorem*, *IS THE BRAIN BAYESIAN?*,- J. Horgan [2016]
*Bayes’s Theorem: What’s the Big Deal?*, Scientific American - Andrew [2008]
*Why I don’t like Bayesian statistics*, Statistical Modeling, Causal Inference, and Social Science *Bayes’ Theorem with Lego*, COUNT BAYESIE- T.Wiecki
*Bayesian data analysis with PyMC3*, Quantopian Inc. - A.Gelman, et.al, [2014]
*Stan: A platform for Bayesian inference*, Columbia University

- N.Yau [2012]
*Bayesian fantasy football 101*, flowing data *Bayesian Data Analysis with PyMC3*, github

**Star Diagrams**

*Star (Spider/Radar) Plots and Segment Diagrams*, R-Manual*Star Plots and Segment Diagrams of Multivariate Data*, Basic R package

**Chernoff Faces**

*Chernoff Faces*, Wolfram MathWorld- R.Kosara [2007],
*A Critique of Chernoff Faces*, eagereyes - N.Yau [2010],
*How to visualize data with cartoonish faces à la Chernoff*, FlowingData *Chernoff Face*, Wikipedia*The Trouble with Chernoff*, Map Hugger- L.Golden, M.Sirdesai [1992]
*Chernoff Faces: a Useful Technique For Comparative Image Analysis and Representation*, Map Hugger - C.Morris, D.Ebert, P.Rheingans
*An Experimental Analysis of the Effectiveness of Features in Chernoff Faces*, UMBC

*Baseball managers Chernoff faces*, information aesthetics

- A.Schwarz [2008]
*Professor Puts a Face on the Performance of Baseball Managers*, The New York Times

**Network Visualizations**

- M.Grandjean [2015], Network Visualizations: Mapping Shakespeare’s Tragedies, Martin Grandjean
- A.Newitz [2014]
*Can Network Theory Help Explain Epic Mythology?*, iO9 *Visualizing Reddit Discussions*, Visualizing Reddit Discussions- N.Hirakata [2015]
*Neo4j to make svg for visualization of relationship graph*, Gappy Facets *global language network*, global language network

**Neural Network**

- N.Yau [2016],
*Here’s how a neural network works*, FlowingData - D. Smilkov and S. Carter, GitHub
- E.Bernhardsson [2016],
*Analyzing 50k fonts using deep neural networks*, - I.Zandi [2000],
*Use of Artificial Neural Network as a Risk Assessment Tool in Preventing Child Abuse*, Artificial neural network - P.RK [2000],
*Applying artificial neural network models to clinical decision making*, Artificial neural network - M.Gales [2001],
*Multi-Layer Perceptron: Introduction and Training*, Multi-Layer Perceptron: Introduction and Training

- N.Yau [2015],
*Neural Network for selfie analysis*, flowing data

**Association Rules**

*Association Rules*, Association Rules- S.Brossette, et.al,
*Association Rules and Data Mining in Hospital Infection Control and Public Health Surveillance*, NCBI - E.García, et.al,
*Drawbacks and solutions of applying association rule mining in learning management systems*, the International Workshop *Association Rules*, Association Rules- R.Sousa, F.Rodrigues, [2013],
*Mining association rules with rare and frequent items*, ACM Digital Library - C.Berberidis,
*Inter-Transaction Association Rules Mining for Rare Events Prediction*, Aristotle University of Thessaloniki - Y.Koh, N.Rountree,
*Rare Association Rule Mining and Knowledge Discovery*, Information Science Reference - D.Rai

, et.al, [2012],*MSApriori using Total Support Tree Data Structure*, International Journal of Computer Applications

- YC.Lee, et.al, [2004],
*Mining association rules with multiple minimum supports using maximum constraints*, science direct - YH.Hu, YL.C [2004],
*Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism*, science direct - Wesley [2012],
*Association Rule Learning and the Apriori Algorithm*, r bloggers - H.Yun, et.al, [2003],
*Mining association rules on significant rare data using relative support*, science direct - W.Lin, [2003],
*Collaborative Recommendation via Adaptive Association Rule Mining*, Worcester Polytechnic Institute - U.Bhatt, P.Patel, [2014],
*A Recent Overview: Rare Association Rule Mining*, International Journal of Computer Applications - C.Romero, et.al,
*Mining Rare Association Rules from e-Learning Data*, University of Córdoba - >M.Khan, et.al,
*Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework*, citeseerx - >E.Cohen, et.al,
*Finding Interesting Associations without Support Pruning*, stanford.edu - >
*Confidence Based Pruning*, hyper textbook shop - >S.Kannan, R.Bhaskaran [2009],
*Association Rule Pruning based on Interestingness Measures with Clustering*, International Journal of Computer Science Issues - >M.Steinbach, et.al [2007],
*Objective Measures for Association Pattern Analysis*, Contemporary Mathematics - >R.Bayardo Jr, et.al, [1999],
*Constraint-Based Rule Mining in Large, Dense Databases*, The 15th Int’l Conf. on Data Engineering - >R.Bayardo Jr, R.Agrawal, [1999],
*Mining the Most Interesting Rules*, The Fifth ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining - >J.Li,
*Efficient Mining of High Confidence Association Rules without Support Thresholds*, citeseerx - J.Bailey, [2002],
*Fast Algorithms for Mining Emerging Patterns*, Springer Link - A.Batbarai, D.Naidu

[2014],*Approach for Rule Pruning in Association Rule Mining for Removing Redundancy*, IJIRCCE - L.Szathmary, et.al,
*Towards Rare Itemset Mining*, HAL

**Classification**

- W.Loh
*Classification and regression trees*, - J.Platt, N.Cristianini, [2000]
*Large Margin DAGs for Multiclass Classification*, *Statistical classification*, wikipedia*Multiclass classification*, wikipedia*Accuracy and precision*, wikipedia*Binary classification*, wikipedia*Classification chart*, wikipedia*Supervised vs. unsupervised learning*, valpola_thesis*Tree-Based Models*, Quick-R*Decision Trees*, r data mining*Classification using neural net in r*, r-bloggers- JP.Vert

*Practical session: Introduction to SVM in R*, svmbasic_notes *Support Vector Regression with R*, SVM Tutorial- J.Rickert [2013],
*Draw nicer Classification and Regression Trees with the rpart.plot package*, Revolutions *Support Vector Machines*, scikit-learn*Support Vector Machines Tutorial*, NEC Labs America*Why use SVM?*, yaksis*Introduction to Support Vector Machines*, opencv

**Clustering**

- M.Meila
*Classic and Modern Data Clustering*, University of Wahington *Clustering – spark.mllib*, Spark- B.Bahmani, B.Moseley, A.Vattani, R.Kumar, S.Vassilvitskii
*Scalable K-Means++*, - A.Vassilaros
*ISODATA*, *Clustering Algorithm Applications*,*ROCK: A Robust Clustering Algorithm for Categorical Attributes*, ROCK- M.Mampaey, J.Vreeken
*Summarizing Categorical Data by Clustering Attributes*, Summarizing Categorical Data by Clustering Attributes - T.Chen et.al
*Model-based multidimensional clustering of categorical data*, Science Direct - P.Kudová et.al
*Categorical Data Clustering Using Statistical Methods and Neural Networks*, Categorical Data Clustering - B.Frey, D.Dueck
*Clustering by Passing Messages Between Data Points*, Science - J.Carbonera
*Are there clustering algorithms developed for dealing naturally with nominal/conceptual/categorial data*, ResearchGate *Cluster Analysis – Introduction*, Clustering and Classification methods for Biologists- H.Finch,
*Comparison of Distance Measures in Cluster Analysis with Dichotomous Data*, Cluster Analysis *Cluster Analysis*, Cluster Analysis*K-means Clustering*, R-statistics blog- B.Mehta,
*IRIS Clustering using R-NNet Neural Network*, SAP

**Predictive Analytics**

*Spine Extrapolation*,*Predictive Analytics*, IBM*The Age of Predictive Analytics*, Office of the Privacy Commissioner of Canada*Analytics Paves the Way for Better Government*, Forbes Insights*Practical Predictive Analytics*, LinkedIn*Predictive Analytics: Context and Use Cases*, LinkedIn*Predictive Analytics World for Government*, Predictive Analytics World for Government- R.Mitchell [2013]
*12 predictive analytics screw-ups*, Computer World - G.Jurman [2012]
*A Comparison of MCC and CEN Error Measures in Multi-Class Prediction*, Plos One *Model evaluation: quantifying the quality of predictions*, scikit-learn- J. Gorodkin [2004]
*Comparing two K-category assignments by a K-category correlation coefficient*, Science Direct - Vihinen M [2004]
*How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis.*, NCBI *Aviation Activity and Forecast*, Toronto Peasrson*PredPol – Predicting crime through data mining*, Generally Thinking

**Uncertainty**

- S.Bell [1999],
*A Beginner’s Guide to Uncertainty of Measurement*, National Physical Laboratory - C.Smith,
*Detecting Anomalies in Your Data Using Benford’s Law*, SUGI - G.Iaccarino,
*Uncertainty Analysis and Optimization*, - D.Kriegman [2001]
*Uncertainty*, - N.Yau [2016]
*An uncertain spreadsheet for estimates*, Flowing Data - H.Wainer [2009]
*Picturing the uncertain world: how to understand, communicate, and control uncertainty through graphical display*, Information Research - E.Inglis-Arkell [2014]
*How near-complete certainty can make you completely wrong*, iO9 *Almost Sure*, Almost Sure- N.Yau [2015]
*Criminal sentencing and a stat lesson on probabilities and uncertainty*, Flowing Data - N.Yau [2015]
*Lessons in statistical significance, uncertainty, and their role in science*, Flowing Data - J.Davies [2015]
*Why You’re Biased About Being Biased*, nautilus *Error and Uncertainty*, Whole Course Items: Error and Uncertainty

**Big Data**

*Big data in the abstract*, CQADS*Big data software*, CQADS- E. Mcnulty [2014],
*Uncerstanding the Big Data: The Seven V’s*, Dataconomy - B.Marr [2014],
*Big Data: The 5 Vs Everyone Must Know*, LinkedIn - B.Marr [2015],
*Why only one of the 5 Vs of big data really matters*, IBM - D. Lawson [2013],
*Time for Vendors (and Fundraisers) to Be Big About Big Data*, Working Philanthropy - J.Hess [2015],
*From Police to Pipes: Fresno Leveraging ‘Big Data’ To Improve City Functions*, NPR For Central California *Embracing the Power of Big Data Correlation in Government*, FedTech- N.Bishop [2015],
*Public Sector News: Advancing analytics to transform cities*, IBM - R.Delgado [2015],
*The Big Data Obstacles Faced by Developing Nations*, TECHVIBES - N.Bishop [2015],
*Public Sector News: The ongoing impact of big data and analytics*, IBM - M.Jeelani [2015],
*Chicago uses new technology to solve this very old urban problem*, Forture - N.Bishop [2015],
*Public Sector News: How analytics is changing our world*, IBM - B.Howarth [2014],
*Big data: how predictive analytics is taking over the public sector*, The Guardian - A.Jensen et.al [2014],
*Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients*, Nature - M.Chen [2014],
*? Is ‘Big Data’ Actually Reinforcing Social Inequalities?*, The Nation - J.Sullivan [2013],
*Forget the needle, consider the haystack: Uncovering hidden structures in massive data collections*, Princeton University - R.Misra [2014],
*How does Big Data help us understand the vastness of space? Ask us now!*, iO9 - L.Greenemeier [2014],
*Why Big Data Isn’t Necessarily Better Data*, Scientific American - A.Newitz [2014],
*Here’s What You Need to Know About Big Data*, iO9 - M.Korolov [2014],
*10 big myths about Big Data*, network world - C.Mims [2014],
*Why the only thing better than big data is bigger data*, Quartz - A.Jensen [2014],
*Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients*, Nature Communications - B.Casselman [2015],
*Big Government Is Getting In The Way Of Big Data*, fivethirtyeight

**Do’s and Don’ts**

*Misleading Graph*, Wikipedia- P.Ford [2014],
*Amazing Military Infographics: an appreciation*, The Message - [2012],
*A History of Dishonest Fox Charts*, Media Matters - Bad Graphs, Tumblr
- E.Klein [2010],
*Lies, Damn Lies and the Y axis*, Washington Post - J.Leek [2012],
*The statisticians at Fox News use classic and novel graphical techniques to lead with data*, Simply Statistics - J.Joyner [2010],
*Bad Graphs Mislead More Than 1000 Words*, Outside the Beltway - K.Drum [2011],
*Fun With Graphs: Making the Rich Look Poor*, Mother Jones - J.Chait [2011],
*Does the Middle Class Have All the Money?*, New Republic *Obama’s Chief Data Scientist Reveals How the Government Uses Big Data*, Time- S.Dhillon
*Researchers to study big data collection used on Canadians*, The Globe and Mail - P.Karon [2015]
*Can Big Data Help Government Do Better? This Foundation Thinks So*, Inside Philanthropy - I.Kottasova [2015]
*Europe’s big data bombshell: What you need to know*, CNN - J.Higgins [2015]
*Federal Agencies Warming Up to Big Data*, Commerce Times - J.Higgins [2015]
*Federal Investment in Big Data Applications Heads for Liftoff*, Commerce Times - C.Yiu [2015]
*The Big Data Opportunity*, Policy Exchange *Denmark plans to preserve illegally collected medical data*, EDRi- N.Yau [2016]
*Bad Data — And Worse Decisions — Poisoned Flint*, Flowing Data - T.Siegfried [2010]
*Odds Are, It’s Wrong*, ScienceNews *The Problem with Small Sample Sizes*, The Last Behaviorist*Misleading Graphs: Real Life Examples*, Statistics How To- J.Joyner
*Bad Graphs Mislead More Than 1000 Words*, Outside the Beltway *Bad Graphs*, Bad Graphs- D.Shere H.Groch-begley [2012]
*A History Of Dishonest Fox Charts*, MediaMatters - J. Grohol [2006]
*Bad Statistics: USA Today*, psychcentral - B.Goldacre [2011]
*These Guardian / Independent stories are dodgy. Traps in data journalism.*, Bad Science - R.Parikh [2014]
*How to Lie With Data Visualization*, Gizmodo *Don’t Let Maps Fool You*, Fake Science- A.Balliett [2011]
*The Do’s And Don’ts Of Infographic Design*, Smashing Magazine - T.Farrant-Gonzalez [2013]
*All That Glitters Is Not Gold: A Common Misconception About Designing With Data*, Smashing Magazine - N.Veltman [2013]
*Avoiding mistakes when cleaning your data*, School of Data - S.Frankel [2015]
*Data Scientists Don’t Scale*, Harvard Business Review - J.Breaugh [2003]
*Effect Size Estimation: Factors to Consider and Mistakes to Avoid*, Journal of Management - N.Yau [2014]
*CSV Fingerprint: Spot errors in your data at a glance*, Flowing Data - J.Hassell [2014]
*3 Mistaken Assumptions About What Big Data Can Do For You*, CIO - M.Michel [2015],
*6 Reasons You Can’t Trust Science Anymore*, cracked

**Others**

- T.Elms [2008],
*Lexical Distance Among the Languages of Europe*, Etymologikon *Thanksgiving in Charts and Graphs*, The Gentleman’s Armchair- Data Cusine, experimental research on the representation of data with culinary means
*The online world*, The online world- J. Harris,
*The Periodic Table of Storytelling*, *Random*, SMBC- NYtimes
- Atomic Radius
- NASA
- Web elements
- Periodic Table
- Meta-synthesis
- NASA
- Coursera
- Cheezburger
- W.Hickey [2015] FiveThirtyEight
- K.Goldsberry [2015] FiveThirtyEight
- K.Schaul [2015],
*The number of ‘mass shootings’ in the U.S. depends on how you count*, The Washington Post - Nichols [2015],
*New Ways To Visulize Shot Supression*, The Sports Daily *SCIENTIFIC ENGLISH APHORISMS*, SCIENTIFIC ENGLISH APHORISMS*Popular Quotes*, goodreads*Maths Quotes*, sfsu math- A.Marinus, et.al [2014]
*6 Shocking Studies That Prove Science Is Totally Broken*, Cracked - N.Yau [2014]
*Famous Movie Quotes as Charts*, Flowing Data - N.Silver [2014]
*What the Fox Knows*, FiveThirtyEight - A.Hadhazy [2014]
*HOW TO TRICK OTHERS INTO DOING YOUR BIDDING*, Popular Science *Air Carrier Traffic at Canadian Airports 2009*, Statistics Canada*Air Carrier Traffic at Canadian Airports 2012*, Statistics Canada- M.Daniels
*The Largest Vocabulary*, Polygraph - N.Yau [2014]
*Distribution of letters in the English language*, Flowing Data *Word clouds*, wordle*Data Quotes*, Data Quotes*13 Really Cool Quotes About Data*, Data Quotes*Quotes about Data Science*, Statistics*Five Ws*, wikipedia*25 Greatest Data Quotes*, Data Quotes*Grapefruit*, xkcd*Extrapolating*, xkcd*No, You’re Not Entitled To Your Opinion*, iflscience*Crimes Against Hugh’s Manatees*, tumblr*Steven Pinker’s Sense of Style*, Scientific American*QUOTE OF THE DAY*, forbes*Quotes About Classification*, Data Quotes- J.Markoff [2011],
*Government aims to build a ‘data eye in the sky’*, The New York Times - A.Berg,
*Names and Faces in the News*, UC Berkeley - K.Poulsen [2014]
*How a Math Genius Hacked OkCupid to Find True Love*, wired - E.Yong [2008]
*European genes mirror European geography*, scienceblogs *Will Machines Ever Think Like Humans?*, Scientific American- N.Yau [2014]
*Jeopardy! clues data*, Flowing Data - W.Hickey [2014]
*How Data Can Help You Write A Better Screenplay*, fivethirtyeight *Proposal*, mitacs- N.Yau [2014]
*A scaled Periodic Table of Elements*, Flowing Data - K.Trendacosta [2014]
*This Linguistic Family Tree Is Simply Gorgeous*, iO9 - M.Bertin [2015]
*Why Soccer’s Most Popular Advanced Stat Kind Of Sucks*, regressing - C.Aschwanden [2015]
*How To Tell Good Studies From Bad? Bet On Them*, five thirty eight - S.Wolfram [2015]
*What Is Spacetime, Really?*, Stephen Wolfram blog *How Math Works*, Comics*How statisticians changed the war, and the war changed statistics*, The Economist

**Videos**

- grantwoolard,
*Classical Music Mashup*

- D.Arnold, J.Rogness,
*Mobius Transformations Revealed*

- N.Halloran
*The Fallen of World War II*

- N.Yau
*Math of crime and terrorism*

- N.Yau
*Suite of data tools for beginners, focused on fun*

- P.Boily
*The Discovery of Elements*

- T.Lehrer (music), Can YOU sing the elements? (video)
*The Element Song*

- originsX
*Discovery of the Elements – the Movie*

- FiveThirtyEight
*How A Data Scientist Who’d Never Heard Of Basketball Mastered March Madness*

- FiveThirtyEight
*How Data Helped Win The Battle Over Same-Sex Marriage*

- Reason.com
*Prying Open Government: The Sunlight Foundation’s Fight for Transparency*

- N.Yau
*Data science, big data, and statistics – all together now* - Piled Higher and Deeper
*Who owns your data?* - N.Yau [2016]
*Algorithms for the Traveling Salesman Problem visualized* - FiveThirtyEight
*How The NYPD Abused Citizens In The Name Of Data, And How One Cop Exposed It*

- R.Vollman [2013]
*NEW TOOL: PLAYER USAGE CHARTS* - IBMVisualAnalytics [2013]
*The Four Pillars of Effective Visualizations* - iNTERNSiDEA master’s Chanel [2012]
*David McCandless: “The beauty of data visualization”* - LinkedIn Tech Talks [2012]
*Designing Data Visualizations with Noah Iliinsky* - Office Videos [2015]
*Welcome to our office: David McCandless, renowned data journalist and speaker* - N.Yau [2015]
*US boundary evolution* - N.Yau [2015]
*Sometimes the y-axis doesn’t start at zero, and it’s fine* - N.Yau [2015]
*Fast image classifications in real-time* - D.Conway [2011]
*Tidy Data* - N.Yau [2014]
*Statistical concepts explained through dance* *Explore visualization features*- N.Yau [2015]
*White House appoints first US Chief Data Scientist* - N.Yau [2015]
*Mathematics of love* - MLSS Sydney 2015
*Bayesian Inference and MCMC with Bob Carpenter*