The document outlines the roles of data analysis, statistics, and machine learning, highlighting their methods, benefits, and applications. It emphasizes how data analysis can support hypotheses, while statistics help avoid false conclusions, and machine learning can predict outcomes effectively. Additionally, it provides a course outline covering various topics related to these fields.

1. Data Analysis, Statistics, Machine Learning
Leland Wilkinson
Adjunct Professor
UIC Computer Science
Chief Scienst
H2O.ai
leland.wilkinson@gmail.com

2. 2
Data Analysis
o What is data analysis?
o Summaries of batches of data
o Methods for discovering paerns in data
o Methods for visualizing data
o Benefits
o Data analysis helps us support supposions
o Data analysis helps us discredit false explanaons
o Data analysis helps us generate new ideas to invesgate
http://blog.martinbellander.com/post/115411125748/the-colors-of-paintings-blue-is-the-new-orange
Copyright © 2016 Leland Wilkinson

3. 3
Stascs
o What is (are) stascs?
o Summaries of samples from populaons
o Methods for analyzing samples
o Making inferences based on samples
o Benefits
o Stascs help us avoid false conclusions when evaluang evidence
o Stascs protect us from being fooled by randomness
o Stascs help us find paerns in nonrandom events
o Stascs quanfy risk
o Stascs counteract ingrained bias in human judgment
o Stascal models are understandable by humans
http://www.bmj.com/content/342/bmj.d671
Copyright © 2016 Leland Wilkinson

4. 4
Machine Learning
o What is machine learning?
o Data mining systems
o Discover paerns in data
o Learning systems
o Adapt models over me
o Benefits
o ML helps to predict outcomes
o ML oen outperforms tradional stascal predicon methods
o ML models do not need to be understood by humans
o Most ML results are unintelligible (the excepons prove the rule)
o ML people care about the quality of a predicon, not the meaning of the result
o ML is hot (Deep Learning!, Big Data!)
http://swift.cmbi.ru.nl/teach/B2/bioinf_24.html
Copyright © 2016 Leland Wilkinson

5. 5
Course Outline
1. Introducon
2. Data
3. Visualizing
4. Exploring
5. Summarizing
6. Distribuons
7. Inference
8. Predicng
9. Smoothing
10. Time Series
11. Comparing
12. Reducing
13. Grouping
14. Learning
15. Anomalies
16. Analyzing
Copyright © 2016 Leland Wilkinson