Posted on Categories Administrativia, Opinion, Practical Data Science, Pragmatic Data Science, Pragmatic Machine LearningTags , , , , , Leave a comment on New Year’s Resolution 2020: Work on more R Data Science Projects

New Year’s Resolution 2020: Work on more R Data Science Projects

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.

Combine this with the new second edition of Practical Data Science with R, and you have a great study set to succeed at substantial statistical modeling and analytics tasks using the R programming language.


PDSwR2Lego

(Note: Lego mini-fig not included!)

Posted on Categories data science, Opinion, Pragmatic Data Science, TutorialsTags , , , , , , , , , 1 Comment on New Timings for a Grouped In-Place Aggregation Task

New Timings for a Grouped In-Place Aggregation Task

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.

Continue reading New Timings for a Grouped In-Place Aggregation Task

Posted on Categories data science, Opinion, StatisticsTags , Leave a comment on What is a Second Edition?

What is a Second Edition?

What it is a second edition of a book to its authors?

In some sense it is the book the authors thought they were writing the first time.

Continue reading What is a Second Edition?

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Practical Data Science with R 2nd Edition update

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.

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Free R/datascience Extract: Evaluating a Classification Model with a Spam Filter

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.

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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:

Take 37% off Practical Data Science with R, Second Edition by entering fcczumel3 into the discount code box at checkout at manning.com.

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AI for Engineers

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 LinkedIn

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).

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How to Prepare Data

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.

Continue reading How to Prepare Data

Posted on Categories Administrativia, Opinion, Practical Data Science, StatisticsTags , , 2 Comments on Practical Data Science with R update

Practical Data Science with R update

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.

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Advanced Data Reshaping in Python and R

This note is a simple data wrangling example worked using both the Python data_algebra package and the R cdata package. Both of these packages make data wrangling easy through he use of coordinatized data concepts (relying heavily on Codd’s “rule of access”).

The advantages of data_algebra and cdata are:

  • The user specifies their desired transform declaratively by example and in data. What one does is: work an example, and then write down what you want (we have a tutorial on this here).
  • The transform systems can print what a transform is going to do. This makes reasoning about data transforms much easier.
  • The transforms, as they themselves are written as data, can be easily shared between systems (such as R and Python).

Continue reading Advanced Data Reshaping in Python and R

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New Getting Started with vtreat Documentation

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:

  • Missing values
  • Large cardinality categorical variables
  • Novel levels from categorical variables

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.