1. Data Science, Statistics, and Projects
Jon Kettenring

2. My Background 1956-1962 BS and MS in Statistics from Stanford U.
US Army in Turkey
PhD in Statistics from U. North Carolina
Bell Labs
Bellcore, Telcordia Technologies
Drew U.
Technical interests
Applied statistics and data analysis
Applied math, software engineering, and machine learning

3. Data Science
A trendy term that captures the interdisciplinary nature of modern data problems. Data Science is becoming fashionable in universities. Many employers are looking for well-trained data scientists.

4. What is Data Science? Extraction of knowledge from data
Uses math, statistics, information technology, probability, machine learning, visualization, …
Applies to “small” and “big” data
Just buzz words?
Just another name for statistics?
“I think data-scientist is a sexed up term for a statistician....Statistics is a branch of science. Data scientist is slightly redundant.”—Nate Silver
2008: Obama vs. McCain 49 of 50 states
2012: Obama vs. Romney 50 of 50 states

5. What is Data Science?
Drew Conway, “The Data Science Venn Diagram”, diagram
Danger zone: “know enough to be dangerous” … short on math and statistics skills.
I would substitute “Computer Science” for “Hacking Skills” and “Domain Knowledge” for “Substantive Knowledge.”

6. Examples of Important Statistical Ideas in Data Science
Sampling
Bias
Variability
Replication
Experimental design
Modeling
Inference
Prediction

7. Examples of Projects (all with underlying data themes)
Past projects:
Exercise and health
Autism
Introductory statistics courses
Potential projects:
Climate change (as in your reading)
Reproducibility of scientific findings
Sexism in science
Big Data
Any topic of interest to you that involves data

8. “Playing Dumb on Climate Change” Naomi Oreskes, New York Times Sunday Review, 1/3/15
Are scientists too conservative?
Presumption of innocence or no effect: “show me the data”.
Type 1 error: conclude effect is real when it isn’t.
Type 2 error: conclude effect is not there when it is.
Have scientists under predicted the threat of climate change?
Image by Oliver Munday
Given the growing evidence of the impact of climate change, have scientists been too cautious in making their case thereby putting us and our planet at risk?
Have they focused too much on controlling the type 1 error at the expense of the type 2 error?

9. “Why Most Published Research Findings Are False” article by J
“Why Most Published Research Findings Are False” article by J. Ioannidis, PLoS Medicine, August 2005,
Over reliance on p-values (Type 1 error estimates)
Reject study hypothesis if p < .05 (a common standard of practice)
Publish the ‘research finding’
Bias problems in analysis and reporting
Bury negative findings
Conflicts of interest and prejudice
Isolated reporting of results of one of many studies
Sample size too small
Effect size too small
Pressure to publish on hot topics
Need for more attention to replication among other things.

10. Sexism in Science (Has gender balance been reached?)
2012 paper in PNAS
Male and female science professors evaluated resumes more favorably if they believed the applicant was a male.
2014 article in the NYT
“Academic science isn’t sexist.”
Gender balance achieved in math- intensive fields.

11. What is “Big Data”? Massive amounts of complex information
Data often observational (not randomized)
Data quality vs. data quantity
Standard data analytical tools break down
How to visualize the data?
How to analyze the data?
Examples arise in many domains:
Medicine, genomics, environment, business, finance, geography, astronomy, and social media

12. Projects with Data Themes
Potential projects:
Climate change
Reproducibility of scientific findings
Sexism in science
Big Data
Any topic of interest to you that involves data
If you would like to discuss a potential project, please stop by my office or send me an .