Data Science, Statistics, and Projects Jon Kettenring

My Background 1956-1962 BS and MS in Statistics from Stanford U. 1962-1964 US Army in Turkey 1964-1969 PhD in Statistics from U. North Carolina 1969-1984 Bell Labs 1984-2003 Bellcore, Telcordia Technologies 2004-2015 Drew U. Technical interests Applied statistics and data analysis Applied math, software engineering, and machine learning

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.

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

What is Data Science? Drew Conway, “The Data Science Venn Diagram”, http://drewconway.com/zia/2013/3/26/the-data-science-venn- 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.”

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

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

“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?

“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, 696-701 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.

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.

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

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 email.