Can you tell us about your upcoming keynote at EARL and what the key take-home messages will be for delegates? I’m going to talk about functional programming which I think is one of the most important programming techniques used with R. It’s not something you need on day 1 as a data scientist but it gives you some really powerful tools for repeating the same action again and again with code. It takes a little while to get your head around it but recently, because I’ve been working writing the second edition of Advanced R, I’ve prepared some diagrams that make it easier to understand. So the take-home message will be to use more functional programming because it will make your life easier!

Regarding our recent post on the syllogism that ate science, someone points us to this article, “The CSI Effect: Popular Fiction About Forensic Science Affects Public Expectations About Real Forensic Science,” by N. J. Schweitzer and Michael J. Saks.

UEA & UCR Time Series Classification Repository

Interventional Robustness of Deep Latent Variable Models

A data science project requires numerous iterations that are time-consuming. When dealing with numbers and data interpretations, it goes without question that you have to be quite smart and proactive.

Despite recent advances and press regarding the field, quantum computing is still veiled in mystery and myth, even within the field of data science and technology. Even those within the field of quantum computing and quantum machine learning are still learning the potential for progress and the stark limitations of current systems. However, quantum computing has arrived in its infancy, and many major companies are pouring money into related R&D efforts. D-Wave’s system has been commercially available for a couple of years already (albeit at a price tag of $10 million), and other systems have been opened for research purposes and commercial partnerships with quantum machine learning companies.

This is an analysis of the Kaggle 2018 survey dataset. In my analysis I am trying to understand the similarities and differences between men and women users from US and India, since these are the two biggest segments of the respondent population. The number of respondents who chose someting other than Male/Female is quite low, so I excluded that subset as well.

At: Vanderbilt University Location: Nashville, TNWeb: www.vanderbilt.eduPosition: Lecturer in Data and Analytics

As the midterm elections loom, the ads focusing on key issues are running in full force. Using data from Nielsen, Bloomberg mapped the issues talked about across the country.

At: Vanderbilt University Location: Nashville, TNWeb: www.vanderbilt.eduPosition: Senior Lecturer in Data and Analytics