November 2015 Items of Interest

November Meetups:

Tidewater Analytics: Tuesday, 10 November

7:00 pm at 757 Creative Space, 259 Granby St. Suite 250, downtown Norfolk.

The feature presentation will by by Steve Miller, an Operations Researcher for Newport News shipyard. He will give an overview of simulation and its different modes, along with ties to analytics.

Following Steve’s presentation, the Kaggle Machine Learning group will meet and review the Titanic Survivor data set and Data Camp tutorial.

757 R User’s Group: Tuesday, 17 November

6:30 pm at 757 Creative Space, 259 Granby, Suite 250, downtown Norfolk.

Keith Brown, a risk analyst for USAA, will discuss Hadley Wickham’s package ggplot2  for graphics.

Office Hours: Saturday, 21 November

4:00 pm at 757 Creative Space, 259 Granby, Suite 250, downtown Norfolk.

An informal gathering to review basic R features and functions for those who are relatively new to R. The long-term orientation will be towards machine learning and the Kaggle competition being planned by Tidewater Analytics.

This month the topics will be writing functions in R, and finding/installing R packages for using R functions that other people have written.

Tidewater Big Data Enthusiasts: Tuesday, 24 November

7:00 pm, Tuesday, 22 September at 757 Creative Space, 259 Granby St. Suite 250, Norfolk.

This month’s topic will be Medicare Payments to the Tidewater Area. It will be a hands-on exercise with Hadoop and Hive to examine 11 million records and show the financial impact of selected procedures in various ZIP codes in the Tidewater area.

MOOCs and other educational venues:


As mentioned last month, Coursera now has a number of interesting specialization tracks under the general heading of Data Science. They include the following:

Data Science, with Johns Hopkins. This was their flagship course, and a lot of people locally, nationally, and globally got their feet wet in data science through this excellent set of one-month classes.

Big Data, with UC San Diego

Executive Data Science, with Johns Hopkins University

Business Analytics, with the University of Pennsylvania

Data Mining, with University of Illinois

Genomic Data Science, with Johns Hopkins University

Check out the web sites for start dates.


EdX has a structure similar to Courser’s in which they team up with top-notch universities on various topics. They are really upping their game, and some of their relevant offerings are the following:

Data Science and Analytics in Context, with Columbia University

Data Analysis for Life Sciences (Statistics with R), with Harvard University. This is a multi-class offering.

Introduction to Statistics, with UC Berkeley. This is a multi-class offering that includes descriptive statistics, inferential statistics, probability, etc.

Introduction to R Programming, with Microsoft. Self-paced.

Check out the web sites for start dates. Some of the courses have come and gone, but the material has been archived and is still available for self-study.

Revolutions Index of Online R Courses:

This is an excellent listing of online R courses.


Advanced R, by Hadley Wickham

I don’t consider myself an “advanced” R user, but I’ve moved well beyond the beginner phase and was looking for something to help get me to the next level. It turned out this book was just the ticket. Although described as “advanced,” the topics, examples, and writing are all easily accessible for anyone who has progressed to the late-beginner stage or beyond. One of the best books I’ve bought on programming in general, and R in particular.

Moneyball, by Michael Lewis

If there was one single thing that turned my head towards data analytics some years ago, it was reading this book. Since that first read, I probably read four more times  over the years. A recent situation induced me to read it again, and I found that even after all these years, it’s still an awesome book. If you want to see how data analytics can be a literal and figurative game-changer, this is the book to read.


What to do with small data?

There has been a lot of press about “big data,” and many an enterprise has built out a human and physical infrastructure to deal with it. But what about small data? Often times big data solutions fail in small(er) data scenarios. This is an interesting article that discusses a number of approaches to the problem.

Hadley Wickham AMAs (Ask Me Anything)

For those who don’t know of him, Hadley Wickham is the Chief Scientist at RStudio, and the author of numerous R packages. He had an AMA on Reddit in late-September that was awesome and well worth reading, and he will have another AMA on Friday, 13 November to discuss new stuff in ggplot2.

Edward Tufte podcast

Edward Tufte is to data visualization what Hadley Wickham is to R packages. He’s the guy who — probably more than any one person — put data visualization in the popular imagination with his series of stunning books. In any case, this podcast is about 50 minutes, and you can hear his thoughts on the current data visualization landscape and where things are going.

Not So Standard Deviations podcast

Roger Peng — of the Coursera Data Science series — has teamed up with data scientist and blogger Hilary Parker to create an ongoing series of podcasts that discuss various aspects of data analytics and data science. Kind of goofy at times, but enjoyable and often informative.

ODU Business Gateway unWINEd

5:00 – 7:00 pm, Tuesday, 17 November at Bon Secours Cancer Institute, 155 Kingsley Lane, Norfolk

This is a social networking event that the Business Gateway has about once a month. It’s free…there are snacks and wine…and it’s a good place to meet people. Dress tends to be business casual.

Hampton Roads Dev Fest

Saturday, 14 November

This seems to be a local Microsoft love-in. Not that there’s anything wrong with that, but I see little, if any, focus on data science or analytics. And I find it odd that there are no R speakers, considering Microsoft bought Revolution Analytics recently. Nonetheless, if you are a programmer you might enjoy it and/or get something out of it.

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