I am re-reading from the great statistician John W. Tukey’s paper: Tukey, John W. “The Future of Data Analysis.” Ann. Math. Statist. 33 (1962), no. 1, pp. 1–67. doi:10.1214/aoms/1177704711. https://projecteuclid.org/euclid.aoms/1177704711 I’ve taken the liberty of pulling out some quotes that are very relevant to the usual “data science is not […]
I am working on a promising new series of notes: common data science fallacies and pitfalls. (Probably still looking for a good name for the series!) I thought I would share a few thoughts on it, and hopefully not jinx it too badly.
From the frontmatter: We recommend this book! Deep Learning for Coders with fastai and PyTorch uses advanced frameworks to move quickly through concrete, real-world artificial intelligence or automation tasks. This leaves time to cover usually neglected topics, like safely taking models to production and a much-needed chapter on data ethics. […]
For all our remote learners, we are sharing a free coupon code for our R video course Introduction to Data Science. The code is ITDS2020, and can be used at this URL https://www.udemy.com/course/introduction-to-data-science/?couponCode=ITDS2020 . Please check it out and share it!
Here is a small quote from Practical Data Science with R Chapter 1. It is often too much to ask for the data scientist to become a domain expert. However, in all cases the data scientist must develop strong domain empathy to help define and solve the right problems. Interested? […]
Students have asked me if it is better to use the same cross-validation plan in each step of an analysis or to use different ones. Our answer is: unless you are coordinating the many plans in some way (such as 2-way independence or some sort of combinatorial design) it is […]
We have a new data scientist sticker! If you see Nina or John at a conference/MeetUp, please ask us for a sticker!
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 […]
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 […]
As we have announced before, we have ported the R version of vtreat to a new Python version of vtreat. Our latest news is: we are speaking about the Python version at PyData LA 2019 (Thursday 10:50 AM–11:35 AM in Track 2 Room).