…if you give a man a fish he is hungry again in an hour. If you teach him to catch a fish you do him a good turn. The quote is often attributed to a Chinese proverb and is excerpted from Anne Isabella Thackeray Ritchie’s novel, Mrs. Dymond (1885). The point is well understood – one of the most important things we can teach our students is how they can help themselves.
Today we have a guest entry authored by Tim Erickson (eeps media) and Bill Finzer (Concord Consortium) about the use of the Common Online Data Analysis Platform (CODAP) to teach data science. They write: We’ve been designing point-and-click data software since the early 90’s. From the beginning, though, we wanted to get beyond point-and-click to a user experience of data immersion. (William Gibson’s 1984 cyberpunk novel Neuromancer and its “cyberspace” both inspired and eluded us.
Today is July 9, 2019, and we are having serious FOMO for not being in Toulouse, France for this year’s useR! conference. We will be following along on twitter (and encourage you to do the same) to keep up with the best talks via the useR! 2019 twitter page and the #user2019 hashtag. And, great news!!!! The keynote addresses will be live streaming at R Consortium youtube. Thanks to the support of RConsortium for making the live stream possible.
A previous entry discussed the importance of coding style and “code smell” to help data analyses be clearer and more comprehensible. In this entry we will extend that discussion to describe ways of teaching code refactoring. Wikipedia defines code refactoring as “the process of restructuring existing computer code—changing the factoring—without changing its external behavior. Refactoring is intended to improve nonfunctional attributes of the software. Advantages include improved code readability and reduced complexity; these can improve source-code maintainability and create a more expressive internal architecture or object model to improve extensibility.
Intro Hello! It’s my distinct pleasure to be guest-blogging for you today! My name is Kelly Bodwin and I’m an Assistant Professor of Statistics at Cal Poly, San Luis Obispo. In my two years at Cal Poly I have taught Stat 218 - An introductory course for non-majors Stat 419 - An upper-level course in multivariate analysis Stat 331 - Our department’s introductory R class In all of these courses, I make extensive use of R.
Image credit: xkcd.com Statistics for science Figuring out truth is really hard to do. Teaching students how to attempt it may be even harder. As statisticians we know that statistical significance isn’t truth, but we still hope that the process by which we analyze data will lead us on a path toward scientific discovery. How do we teach students the best way to stay on the path of using statistics to move science forward?
What is the learnr package? The learnr package makes it easy to turn any R Markdown document into an interactive tutorial. With this vehicle instructors can offer interactive tools to their students to allow them to explore datasets in use from the class, a textbook, or even collected themselves. Straight from Garrett Grolemund’s fantastic introduction to the package, tutorials can include any or all of the following: Narrative, figures, illustrations, and equations.
The Importance of Good Coding Style To begin with another quote from Hadley Wickham: “Good coding style is like using correct punctuation. You can manage without it, but it sure makes things easier to read.” In this way, coding style is very much an example of a negative virtue. You are much more likely to be told if you have a bad coding style than you are if you have a good coding style.
Throughout the summer 2019 blog series, we have given teaching tips, best data science practices, links to compilations of papers in data science and in teaching data science, and ways to participate in the larger data science community. That is, for anyone already working in a data science community (e.g., in a job or an academic institution) or for anyone doing data science for fun, we have provided myriad rabbit holes in which to get lost for your entire summer.
Shiny Recap Yesterday we introduced R Shiny and discussed how it allows you to build interactive web applications straight from R. We saw a few examples highlighting the wondrous interactivity of exploratory data analysis, data visualization, and data models that it enables. If you didn’t catch yesterday’s post, check it out at https://teachdatascience.com/shiny1/ and be sure to go play around with some Shiny apps at RStudio’s gallery at https://shiny.