Slides from my PyData2019 data_algebra lightning talk are here.
Nina and I have prepared a quick introduction video for Practical Data Science with R, 2nd Edition.
We are really proud of both editions of the book. This book can help an R user directly experience the data science style of working with data and machine learning techniques.
The book is available now at:
- Directly from the publisher Manning, now (often with significant discounts!).
- Via pre-order from Amazon.com.
Get a signed copy off us! We will be giving away some e-copies and a few signed physical copies at various conferences and meet-ups
(for example at PyData LA 2019).
Please check it out!
Practical Data Science with R, 2nd Edition author Dr. Nina Zumel, with a fresh author’s copy of her book!
We are in the last stages of proofing the galleys/typesetting of Zumel, Mount, Practical Data Science with R, 2nd Edition, Manning 2019. So this edition will definitely be out soon!
If you ever wanted to see what Nina Zumel and John Mount are like when we have the help of editors, this book is your chance!
One thing I noticed in working through the galleys: it becomes easy to see why Dr. Nina Zumel is first author.
2/3rds of the book is her work.
We are excited to share a free extract of Zumel, Mount, Practical Data Science with R, 2nd Edition, Manning 2019: Evaluating a Classification Model with a Spam Filter.
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):
One of the best data science courses I’ve taken. The course focuses on model selection and evaluation which are usually underestimated. Thanks to John Mount, the teacher and the co-authors of Practical Data Science with R. hashtag#AI200
(Note: Nina Zumel, leads on the course design, which is the heavy lifting, John Mount just got tasked to be the one delivering it.)
Zumel, Mount, Practical Data Science with R, 2nd Edition is coming out in print very soon. Here is a discount code to help you get a good deal on the book:
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).
Nina Zumel finished new documentation on how
vtreat‘s cross validation works, which I want to share here.
vtreat is a system that makes data preparation for machine learning a “one-liner” (available in
R or available in
Python). We have a set of starting off points here. These documents describe what
vtreat does for you, you just find the one that matches your task and you should have a good start for solving data science problems in
R or in
The latest documentation is a bit about how
vtreat works, and how to control some of the details of the work it is doing for you.
The new documentation is:
Please give one of the examples a try, and consider adding
vtreat to your data science workflow.
Nina Zumel finished some great new documentation showing how to use
vtreat to prepare data for multinomial classification mode. And I have finally finished porting the documentation to
vtreat. So we now have good introductions on how to use
vtreat to prepare data for the common tasks of:
- Unsupervised data preparation:
- Multinomial classification:
Rmultinomial classification example,
Pythonmultinomial classification example.
That is now 8 introductions to start with. To use
vtreat you only have to work through one introduction (the one helping with the task you have at hand in the language you are using).
As I have said before:
vtreathelps with project blocking issues commonly seen in real world data: missing values, re-coding categorical variables, and dealing high cardinality categorical variables.
- If you aren’t using a tool like
vtreatin your data science projects: you are really missing out (and making more work for yourself).
Just got the following note from a new reader:
Thank you for writing Practical Data Science with R. It’s challenging for me, but I am learning a lot by following your steps and entering the commands.
Wow, this is exactly what Nina Zumel and I hoped for. We wish we could make everything easy, but an appropriate amount of challenge is required for significant learning and accomplishment.
The second edition is coming out very soon. Please check it out.