In an off-topic post we would like to share a series of horror narrations based on Win Vector LLC’s very own Nina Zumel’s translations of Uruguayan author Horacio Quiroga. This is a free series produced by Rue Morgue
The first is: “The Feather Pillow.” DO NOT LISTEN TO THIS IN BED!
We had such a positive reception to our last Introduction to Data Science promotion, that we are going to try and make the course available to more people by lowering the base-price to $29.99. We are also creating a 1 month promotional price of $20.99. To get a permanent subscription to the course for less than $21 just visit this link https://www.udemy.com/course/introduction-to-data-science/ and use the discount code ITDS21 any time in January of 2020.
I’d like to share some new timings on a grouped in-place aggregation task. A client of mine was seeing some slow performance, so I decided to time a very simple abstraction of one of the steps of their workflow.
This section reflects an important design decision in the book: teach model evaluation first, and as a step separate from model construction.
It is funny, but it takes some effort to teach in this way. New data scientists want to dive into the details of model construction first, and statisticians are used to getting model diagnostics as a side-effect of model fitting. However, to compare different modeling approaches one really needs good model evaluation that is independent of the model construction techniques.
This teaching style has worked very well for us both in R and in Python (it is considered one of the merits of our LinkedIn AI Academy course design):
For the last year we (Nina Zumel, and myself: John Mount) have had the honor of teaching the AI200 portion of LinkedIn’s AI Academy.
John Mount at the LinkedIn campus
Nina Zumel designed most of the material, and John Mount has been delivering it and bringing her feedback. We’ve just started our 9th cohort. We adjust the course each time. Our students teach us a lot about how one thinks about data science. We bring that forward to each round of the course.
Roughly the goal is the following.
If every engineer, product manager, and project manager had some hands-on experience with data science and AI (deep neural nets), then they are both more likely to think of using these techniques in their work and of introducing the instrumentation required to have useful data in the first place.
This will have huge downstream benefits for LinkedIn. Our group is thrilled to be a part of this.
We are looking for more companies that want an on-site data science intensive for their teams (either in Python or in R).
Real world data can present a number of challenges to data science workflows. Even properly structured data (each interesting measurement already landed in distinct columns), can present problems, such as missing values and high cardinality categorical variables.
In this note we describe some great tools for working with such data.