What is Statistics?
I recently shared a bit of the history of The Science of Data Analysis. I thought I would follow that up with a quick chalk talk titled “What is Statistics?” (link)
PyData Los Angeles 2019 talk: Preparing Messy Real World Data for Supervised Machine Learning
Video of our PyData Los Angeles 2019 talk Preparing Messy Real World Data for Supervised Machine Learning is now available. In this talk describe how to use vtreat, a package available in R and in Python, to correctly re-code real world data for supervised machine learning tasks. Please check it […]
Why R?
I was working with our copy editor on Appendix A of Practical Data Science with R, 2nd Edition; Zumel, Mount; Manning 2019, and ran into this little point (unfortunately) buried in the back of the book. In our opinion the R ecosystem is the fastest path to substantial data science, […]
What is vtreat?
vtreat is a DataFrame processor/conditioner that prepares real-world data for supervised machine learning or predictive modeling in a statistically sound manner. vtreat takes an input DataFrame that has a specified column called “the outcome variable” (or “y”) that is the quantity to be predicted (and must not have missing values). […]
Speaking at BARUG
We will be speaking at the Tuesday, September 3, 2019 BARUG. If you are in the Bay Area, please come see us. Nina Zumel & John Mount Practical Data Science with R Practical Data Science with R (Zumel and Mount) was one of the first, and most widely-read books on […]
Florence Nightingale, Data Scientist
Florence Nightingale, Data Scientist. In 1858 Florence Nightingale published her now famous “rose diagram” breaking down causes of mortality. By w:Florence Nightingale (1820–1910). – http://www.royal.gov.uk/output/Page3943.asp [dead link], Public Domain, Link For more please here.
Estimating Rates using Probability Theory: Chalk Talk
We are sharing a chalk talk rehearsal on applied probability. We use basic notions of probability theory to work through the estimation of sample size needed to reliably estimate event rates. This expands basic calculations, and then moves to the ideas of: Sample size and power for rare events. Please […]
Practical Data Science with R, half off sale!
Our publisher, Manning, is running a Memorial Day sale this weekend (May 24-27, 2019), with a new offer every day. Fri: Half off all eBooks Sat: Half off all MEAPs Sun: Half off all pBooks and liveVideos Mon: Half off everything The discount code is: wm052419au. Many great opportunities to […]
Kudos to Professor Andrew Gelman
Kudos to Professor Andrew Gelman for telling a great joke at his own expense: Stupid-ass statisticians don’t know what a goddam confidence interval is. He brilliantly burlesqued a frustrating common occurrence many people say they “have never seen happen.” One of the pains of writing about data science is there […]
How do you know if your model is going to work?
Authors: John Mount (more articles) and Nina Zumel (more articles). Our four part article series collected into one piece. Part 1: The problem Part 2: In-training set measures Part 3: Out of sample procedures Part 4: Cross-validation techniques “Essentially, all models are wrong, but some are useful.” George Box Here’s […]
