For the first full month of the semester the Directed Study students will be learning about various tools in the data scientist’s toolbox. They will work through a self guided online tutorial (course) on this topic by Jeff Leek, PhD, a professor of biostatistics at John’s Hopkin’s Bloomberg School of Public Health. They will learn about the reasons for data analysis, the basics of R (getting it, installing it and a bit about using it (www.r-project.org), and they will learn about version control and online repositories that include version control such as github – for example: https://github.com/scollinspt

For the directed study I have started a group github repository:

https://github.com/HFP-analytics

Students will share their public github site with me so that I can follow their repositories which will grow as they learn to perform statistical programming with R. They will meet with me as they need, when they are able, to get help with their self guided learning. A huge part of doing statistical programming is learning to learn new techniques – being resourceful with the tools of the trade. This includes knowing about them, but more importantly learning how to access them and develop skills independently. People often ask me how I learned SPSS (when I was using SPSS; I have switched to R for just about all my data analysis) and why I could do so much with SPSS. I learned it through using it to solve problems. First small problems and as my abilities grew the problems I could tackle with it grew. My seeming proficiency with SPSS was not really with SPSS at all, it was with an approach to learning on my own with the tools I had available to solve the problems at hand.

I hope for the same developmental process for the directed study students. Eventually (later this semester and next semester) we can start doing team projects which will be housed on the group repository so we can each contribute to it, see what others are doing for the project, and have version control for the code produced. Finally, when we have completed a project we will have done so with the principles of “reproducible research” – our de identified data (from public sources) will be available, as will the code used to process and analyze it, and the results. All free for people to see, manipulate, confirm…..that is, reproduce if they have questions about how the data was used to generate the results presented.

This spring five students will embark on a new opportunity, a directed study geared toward equipping them with skills quite different, but yet complimentary, to those they learn in the Exercise Physiology program at UMass Lowell. The five students have agreed to participate in two semesters (a full academic year) of directed study. In the first semester they will learn about data analysis with hands on programming using the R statistical programming language (www.r-project.org). Despite the steep learning curve of using R, it is popular, powerful, open source and free! So students will be able to use the skills they obtain anywhere they end up going – not bound by expensive licenses to use the tools. If they decide to, they could get so advanced with R that they can develop their own customized R packages to for obtaining data, cleaning data for analysis, or running analyses in whatever area of exercise physiology they pursue. During the second semester they will complete a data analysis project using open source data from a variety of possible sources (part of this semester’s exercise the students will seek out and build a list of links to open source data for their projects next semester).

Data science (analysis) skills are highly sought after (see here, and here, and here), and there is an increasing demand for people with cross disciplinary skills. With mobile technologies allowing measurement of so many health, fitness and performance metrics there is a data revolution creating a need for people who understand health, fitness and performance metrics (such as exercise physiology) to also know how to analyze and generate knowledge using the data (such as hacking skills and statistics. Companies such as Garmin, Nike, MapMyFitness (owned by Underarmor), Zephyr, Polar, Wahoo…. are all competing not only for the hardware systems to measure important metrics, for the analysts that can help develop the proper tools for going from data to understanding to action.

The Exercise Physiology program provides the “substantiative expertise”, the core curriculum requirements provides an introduction quantitative reasoning and statistics; this directed study will build on those foundations and further develop the statistics skills while adding the hacking (computer programming, self directed problem solver) skills.

Source: Drew Conway, Sept 2010. Reproduced under a Creative Commons License.

During this semester I will regularly update the blog to keep anyone interested up to date with our progress. Also, if you have a data analysis project in mind, something you could use help with – send me an email and let me know (see the About page).