Beyond R and Ph.D.s: The Mythology of Data Science Debunked
summary
Claims that big data doesn't really exist and that new math or hiring Ph.D.s alone will fix modeling problems; company uses big-data underwriting and R but finds failures come from bad assumptions not tooling. Shows a concrete counterexample: ten men and ten women measurements flip significance after adding a 4.5 ft outlier and a 7.5 ft outlier, illustrating non‑normal, power‑law data with no meaningful mean. Advocates building teams of mathematicians, experimental psychologists, physicists and biologists and labels that role data artist.
AI-written summary · about
chapters (6)
- 0:00Intro
- 0:12The Big Data Myth
- 1:45Math doesnt work
- 2:45Visitors arent normally distributed
- 3:39Outliers
- 5:45Data Artists
related talks
01h 01mYou don't need AI; You need to know what your data is.
42mWhy Big Data Is Not Data Science
24mHow to successfully become a Data Scientist
44mCareers in Data Science
30mDATA MODELLING IS DEAD! LONG LIVE DATA MODELLING!
31mThe State of Data Engineering
58mGrowth & change of careers, organizations & responsibility in data science
22mWhy is a good data scientist a package of professions?