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):
(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:
Take 37% off Practical Data Science with R, Second Edition by entering fcczumel3 into the discount code box at checkout at manning.com.
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 Python.
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 Python vtreat to prepare data for multinomial classification mode. And I have finally finished porting the documentation to R vtreat. So we now have good introductions on how to use vtreat to prepare data for the common tasks of:
R regression example, Python regression example.R classification example, Python classification example.R unsupervised example, Python unsupervised example.R multinomial classification example, Python multinomial 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:
vtreat helps with project blocking issues commonly seen in real world data: missing values, re-coding categorical variables, and dealing high cardinality categorical variables.vtreat in 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.
Of course we try to avoid inessential problems. All of the code examples from the book can be found here (and all the data sets here).
The second edition is coming out very soon. Please check it out.
Win Vector LLC‘s Dr. Nina Zumel has just released some new vtreat documentation.
vtreat is a an all-in one step data preparation system that helps defend your machine learning algorithms from:
I hoped she could get the Python vtreat documentation up to parity with the R vtreat documentation. But I think she really hit the ball out of the park, and went way past that.
The new documentation is 3 “getting started” guides. These guides deliberately overlap, so you don’t have to read them all. Just read the one suited to your problem and go.
The new guides:
Perhaps we can back-port the new guides to the R version at some point.
I was working with our copy editor on Appendix A of Practical Data Science with R, 2nd Edition; Zumel, Mount; Manning 2019, and ran into this little point (unfortunately) buried in the back of the book.
In our opinion the R ecosystem is the fastest path to substantial data science, statistical, and machine learning accomplishment.
This is why we use and teach R (in addition to using and teaching Python).
data_algebra project: a data processing tool family available in R and Python. These tools are designed to transform data either in-memory or on remote databases.
In particular we will discuss the Python implementation (also called data_algebra) and its relation to the mature R implementations (rquery and rqdatatable).
We will be speaking at the Tuesday, September 3, 2019 BARUG. If you are in the Bay Area, please come see us.
Nina Zumel & John Mount
Practical Data Science with R
Practical Data Science with R (Zumel and Mount) was one of the first, and most widely-read books on the practice of doing Data Science using R. We have been working hard on an improved and revised 2nd edition of our book (coming out this Fall). The book reflects more experience with data science, teaching, and with R itself. We will talk about what direction we think the R community has been taking, how this affected the book, and what is new in the upcoming edition.