1. INFORMATION TECHNOLOGY IN BUSINESS AND SOCIETY SESSION 19 – GETTING DATA AND VISUALIZING IT SEAN J. TAYLOR

2. ADMINISTRATIVIA Assignment 3: Still Grading Assignment 4: GREAT JOB EVERYONE!

3. GROUP PROJECT 1 (DUE 4/13) 1.Find some data 2.Load it into Access and/or Excel (or anything) 3.Explore the data set, find something interesting 4.Create interesting visualizations of the data 5.Use your exploration to help define a question you’d like to answer. 6.Answer that question as best you can using the data.

4. FINDING DATA 1.Infochimps [http://www.infochimps.com/]http://www.infochimps.com/ 2.NYC Open Data [https://nycopendata.socrata.com/]https://nycopendata.socrata.com/ 3.ScraperWiki [https://scraperwiki.com/]https://scraperwiki.com/ 4.Google Insights for Search [http://www.google.com/insights/search/]http://www.google.com/insights/search/ 5.World Bank Data [http://data.worldbank.org/]http://data.worldbank.org/ 6.Many more: [https://bitly.com/bundles/hmason/1]https://bitly.com/bundles/hmason/1 Million song data set Movies and ratings Census data, Enron emails, Tweets, Bit.ly link clicks, etc.

5. LOADING DATA Formats: csv, tab-delimited, fixed-width, many more Unstructured: html or web API data Import into Excel first, then Access May have to clean it first!

6. TOOLS

7. WHY VISUALIZE DATA? Same average for X Same variance for X Same average for Y Same variance for Y (approx) Same correlation between X and Y Same linear regression:

8. ANSCOMBE’S QUARTET Your brain can efficiently process properly visualized data.

9. EDA: EXPLORATORY DATA ANALYSIS An approach to analyzing data sets to summarize their main characteristics in easy-to-understand form. Often with visual graphs, without using a statistical model or having formulated a hypothesis. Helps to formulate hypotheses that could be tested on new data-sets.

10. RULE #1: NO PIE CHARTS!

11. RULE #1: NO 3-D PIE CHARTS!

12. HISTOGRAMS Shows entire distribution of one particular variable. Each column’s height is determined by the count of the number of items which fall into the bin. Bin size is a variable you can play with: wider is more smooth, while smaller bins can yield erratic plots.

13. DENSITY PLOTS A population of women who were at least 21 years old, of Pima Indian heritage and living near Phoenix, Arizona, was tested for diabetes according to World Health Organization criteria. The data were collected by the US National Institute of Diabetes and Digestive and Kidney Diseases. We used the 532 complete records. Red: Diabetes = 0 Blue: Diabetes = 1 Black: Diabetes = 0 or 1

14. BOX PLOTS Display differences between subpopulations in your data. Furthest lines are min/max. Box shows 25 th to 75 th percentiles. Thick line shows the 50 th percentile (the median).

15. SCATTER PLOTS Suggests correlation between two variables. Correlations may be positive (rising), negative (falling), or null (uncorrelated). A line of best fit (alternatively called 'trendline') can be drawn. Ability to show nonlinear relationships between variables.

16. PARETO CHARTS MUCH better than a pie chart. Shows individual components as well as cumulative total.

17. RUN CHART Shows a variable over time. Allows comparison between different variables. Can show trends or time- relationships between variables.

18. USING AREA/VOLUME/SHAPE Don’t: hard for our brains to compare total area of odd shapes If you must, use regular bars (in some kind of… bar chart )

19. USING COLOR Colors have no natural scale. Bad: Better:

20. USING PLACEMENT

21. USING PLACEMENT: BAD

22. TRANSFORM/COMBINE YOUR VARIABLES! Relationships can exist between your variables and computed variables. Height => Height^2 Skewed variables (counts) => take log (e.g. number of friends) Running backs in the NFL Weight / (40 yard dash time) = Speed Score Make categories out of continuous variables: Good performance: 1 if > 5% return in the last year, 0 otherwise.

23. “BEAUTIFUL VISUALIZATION” “THE VISUAL DISPLAY OF QUANTITATIVE INFORMATION”

24. NEXT CLASS: SOFTWARE ENGINEERING Read “No Silver Bullet”