Category: Practical Data Science

Free Video Lecture: Vectors for Programmers and Data Scientists

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

Could not Resist

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!

Data Layout Exercises

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

Starting With Data Science: A Rigorous Hands-On Introduction to Data Science for Engineers

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

PDSwR2: New Chapters!

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!

vtreat Variable Importance

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