In this note, we discuss the use of Cohen’s D for planning difference-of-mean experiments. Estimating sample size Let’s imagine you are testing a new weight loss program and comparing it so some existing weight loss regimen. You want to run an experiment to determine if the new program is more effective than the old one. … Continue reading Cohen’s D for Experimental Planning
We have just released two new free video lectures on vectors from a programmer’s point of view. I am experimenting with what ideas do programmers find interesting about vectors, what concepts do they consider safe starting points, and how to condense and present the material. Please check the lectures out. Vectors for Programmers and Data … Continue reading Free Video Lecture: Vectors for Programmers and Data Scientists
Also, Practical Data Science with R, 2nd Edition; Zumel, Mount; Manning 2019 is now content complete! It is deep into editing and soon into production!
John Mount, Nina Zumel; Win-Vector LLC 2019-04-27 In this note we will use five real life examples to demonstrate data layout transforms using the cdata R package. The examples for this note are all demo-examples from tidyr/demo/, and are mostly based on questions posted to StackOverflow. They represent a good cross-section of data layout problems, … Continue reading Data Layout Exercises
I thought I would give a personal update on our book: Practical Data Science with R 2nd edition; Zumel, Mount; Manning 2019. The second edition should be fully available this fall! Nina and I have finished up through chapter 10 (of 12), and Manning has released previews of up through chapter 7 (with more to … Continue reading Practical Data Science with R Book Update (April 2019)
Here is an example how easy it is to use cdata to re-layout your data. Tim Morris recently tweeted the following problem (corrected). Please will you take pity on me #rstats folks? I only want to reshape two variables x & y from wide to long! Starting with: d xa xb ya yb 1 1 … Continue reading Controlling Data Layout With cdata
A good friend shared with us a great picture of Practical Data Science with R, 1st Edition hanging out in Cambridge at the MIT Press Bookstore. This is as good an excuse as any to share a book update. Nina Zumel and I (John Mount) are busy revising chapters 10 and 11 of Practical Data … Continue reading Practical Data Science with R Book Update
Starting With Data Science A rigorous hands-on introduction to data science for engineers. Win Vector LLC is now offering a 4 day on-site intensive data science course. The course targets engineers familiar with Python and introduces them to the basics of current data science practice. This is designed as an interactive in-person (not remote or … Continue reading Starting With Data Science: A Rigorous Hands-On Introduction to Data Science for Engineers
Manning has a new discount code and a free excerpt of our book Practical Data Science with R, 2nd Edition: here.
This section is elementary, but things really pick up speed as later on (also available in a paid preview).
We have two new chapters of Practical Data Science with R, Second Edition online and available for review! The newly available chapters cover: Data Engineering And Data Shaping – Explores how to use R to organize or wrangle data into a shape useful for analysis. The chapter covers applying data transforms, data manipulation packages, and … Continue reading PDSwR2: New Chapters!
Please help share our news and this discount. The second edition of our best-selling book Practical Data Science with R2, Zumel, Mount is featured as deal of the day at Manning. The second edition isn’t finished yet, but chapters 1 through 4 are available in the Manning Early Access Program (MEAP), and we have finished … Continue reading Practical Data Science with R, 2nd Edition discount!
vtreat‘s purpose is to produce pure numeric R data.frames that are ready for supervised predictive modeling (predicting a value from other values). By ready we mean: a purely numeric data frame with no missing values and a reasonable number of columns (missing-values re-encoded with indicators, and high-degree categorical re-encode by effects codes or impact codes). … Continue reading vtreat Variable Importance