Drumroll Please: The ODSC March Madness 2018 Bracket Predictions

Drumroll Please: The...

It’s that time of year, the snow has melted, flowers are starting to blossom, and the country is consumed by the fever of March Madness. Today, March 15th, marks the true kick-off of the 63-match tournament, famous for its thrilling competitive play and heart-stopping upsets. The tourney has a been focus of the data science […]

R Tip: Introduce Indices to Avoid for() Class Loss Issues

R Tip: Introduce Ind...

Here is an R tip. Use loop indices to avoid for()-loops damaging classes. Below is an R annoyance that occurs again and again: vectors lose class attributes when you iterate over them in a for()-loop. d <- c(Sys.time(), Sys.time()) print(d) #> [1] "2018-02-18 10:16:16 PST" "2018-02-18 10:16:16 PST" for(di in d) { print(di) } #> [1] 1518977777 #> [1] […]

Post-Columbine students do not support gun control

Post-Columbine stude...

In their coverage of the Parkland school shooting, The Economist writes: Though polling suggests that young people are only slightly more in favour of gun-control measures than their elders, those surveys focus on those aged 18 and above. There may be a pre- and post-Columbine divide within that group. Based on my analysis of data from the […]

Support for gun control is lower among young adults

Support for gun cont...

In current discussions of gun policies, many advocates of gun control talk as if time is on their side; that is, they assume that young people are more likely than old people to support gun control. This letter to the editor of the Economist summarizes the argument: It is unlikely that a generation raised on lockdown drills, […]

NAVIGATING THE R PACKAGE UNIVERSE

NAVIGATING THE R PAC...

There are more than 11,000 packages on CRAN, and R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use. Our session centered on this issue, with three themes in our discussion.

Machine Learning vs. Statistics

Machine Learning vs....

This was originally posted on the Silicon Valley Data Science blog was co-written by Drew Hardin   The Texas Death Match of Data Science. Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of […]

Another batch of Think Stats notebooks

Another batch of Thi...

Getting ready to teach Data Science in the spring, I am going back through Think Stats and updating the Jupyter notebooks.  When I am done, each chapter will have a notebook that shows the examples from the book along with some small exercises, with more substantial exercises at the end. If you are reading the […]

Statistics, Simians, the Scottish, and Sizing up Soothsayers

Statistics, Simians,...

A predictive model can be a parametrized mathematical formula, or a complex deep learning network, but it can also be a talkative cab driver or a slides-wielding consultant. From a mathematical point of view, they are all trying to do the same thing, to predict what’s going to happen, so they can all be evaluated […]

More notebooks for Think Stats

More notebooks for T...

More notebooks for Think Stats As I mentioned in the previous post, I am getting ready to teach Data Science in the spring, so I am going back through Think Stats and updating the Jupyter notebooks.  I am done with Chapters 1 through 6 now. If you are reading the book, you can get the notebooks by cloning this […]