While I’m still working on my reflection of the dataviz course I just taught, there were some useful dataviz-teaching talks at the recent IEEE VIS conference.
Jen Christiansen and Robert Kosara have great summaries of the panel on “Vis, The Next Generation: Teaching Across the Researcher-Practitioner Gap.”
Even better, slides are available for some of the talks: Marti Hearst, Tamara Munzner, and Eytan Adar. Lots of inspiration for the next time I teach.
Finally, here are links to the syllabi or websites of various past dataviz courses. Browsing these helps me think about what to cover and how to teach it.
- Andrew Thomas, CMU 36-721 F’14
- Rebecca Nugent, CMU 36-721 F’10 and 36-315 S’14
- Ben Shneiderman, U of Maryland CMSC 734 S’15
- Hadley Wickham, Rice stat499 F’10 and stat645 S’11
- Tamara Munzner, U of British Columbia CS533C and CS547 (various terms)
- Trevor Branch, U of Washington FISH 554 A W’15
- Jeffrey Heer, U of Washington CSE512 W’14, and Stanford cs448b F’12 and earlier terms
- Pat Hanrahan, Stanford CS448B W’06 and earlier terms
- Eytan Adar, U of Michigan SI649 F’15
- Alexander Lex, Harvard CS171 S’15
- Andrew Gelman, Columbia Statistics G8307 F’15
- Kaiser Fung, NYU DATA1-CE9002 S’14 and workshop Su’15
- Kevin Quealy, Metis workshop S’15
- Alberto Cairo and Scott Murray, Knight Center MOOC F’15
- Scott Murray, U of San Francisco ART 335-01 F’12: Information Visualization
- Marti Hearst, Berkeley i247 (various terms)
- Annette Greiner and Christopher Arnold, Berkeley (in development?)
- Alan Rogers, U of Utah Anth 5485 S’11
- Dan Carr, George Mason STAT 875 S’10 and STAT 663 F’09
- John Stasko, Georgia Tech CS 7450 F’15
- Alex Endert, Georgia Tech CS 7450 F’14
- Sheelagh Carpendale, Calgary CPSC 683 W’15
- Maneesh Agarwala, Berkeley CS 294 F’14
- Katy Borner, Indiana S637 S’13
- Niklas Elmqvist, Maryland INST 728V F’15
- Remco Chang, Tufts COMP 150 F’15
- Miriah Meyer, Utah CS 6964 S’12
- Chris North, Virginia Tech CS 5764 F’09
- Amelia McNamara, UCLA Statistics 98T S’15
Not quite data visualization, but related:
Comment below or tweet @civilstat with any others I’ve missed, and I’ll add them to the list.
(Update: Thanks to John Stasko for links to many I missed, including his own excellent course site & resource page.)
I’ve just finished teaching the Fall 2015 session of 36-721, Statistical Graphics and Visualization. Again, it is a half-semester course designed primarily for students in the MSP program (Masters of Statistical Practice) in the CMU statistics department. I’m pleased that we also had a large number of students from other departments taking this as an elective.
For software we used mostly R (base graphics, ggplot2, and Shiny). But we also spent some time on Tableau, Inkscape, D3, and GGobi.
We covered a LOT of ground. At each point I tried to hammer home the importance of legible, comprehensible graphics that respect human visual perception.
Remaking pie charts is a rite of passage for statistical graphics students
My course materials are below. Not all the slides are designed to stand alone, but I have no time to remake them right now. I’ll post some reflections separately.
Download all materials as a ZIP file (38 MB), or browse individual files:
I’m pretty excited for tomorrow: I’ll begin teaching the Fall 2015 offering of 36-721, Statistical Graphics and Visualization. This is a half-semester course designed primarily for students in our MSP program (Masters in Statistical Practice).
A large part of the focus will be on useful principles and frameworks: human visual perception, the Grammar of Graphics, graphic design and interaction design, and more current dataviz research. As for tools, besides base R and ggplot2, I’ll introduce a bit of Tableau, D3.js, and Inkscape/Illustrator. For assessments, I’m trying a variant of “specs grading”, with a heavy use of rubrics, hoping to make my expectations clear and my TA’s grading easier.
Classifier diagnostics from Cook & Swayne’s book
My initial course materials are up on my department webpage.
Here are the
- syllabus (pdf),
- first lecture (pdf created with Rmd), and
- first homework (pdf) with dataset (csv).
(I’ll probably just use Blackboard during the semester, but I may post the final materials here again.)
It’s been a pleasant challenge to plan a course that can satisfy statisticians (slice and dice data quickly to support detailed analyses! examine residuals and other model diagnostics! work with data formats from rectangular CSVs through shapefiles to social networks!) … while also passing on lessons from the data journalism and design communities (take design and the user experience seriously! use layout, typography, and interaction sensibly!). I’m also trying to put into practice all the advice from teaching seminars I’ve taken at CMU’s Eberly Center.
Also, in preparation, this summer I finally enjoyed reading more of the classic visualization books on my list.
- Cleveland’s The Elements of Graphing Data and Robbins’ Creating More Effective Graphs are chock full of advice on making clear graphics that harness human visual perception correctly.
- Ware’s Information Visualization adds to this the latest research findings and a ton of useful detail.
- Cleveland’s Visualizing Data and Cook & Swayne’s Interactive and Dynamic Graphics for Data Analysis are a treasure trove of practical data analysis advice. Cleveland’s many case studies show how graphics are a critical part of exploratory data analysis (EDA) and model-checking. In several cases, his analysis demonstrates that previously-published findings used an inappropriate model and reached poor conclusions due to what he calls rote data analysis (RDA). Cook & Swayne do similar work with more modern statistical methods, including the first time I’ve seen graphical diagnostics for many machine learning tools. There’s also a great section on visualizing missing data. The title is misleading: you don’t need R and GGobi to learn a lot from their book.
- Monmonier’s How to Lie with Maps refers to dated technology, but the concepts are great. It’s still useful to know just how maps are made, and how different projections work and why it matters. Much of cartographic work sounds analogous to statistical work: making simplifications in order to convey a point more clearly, worrying about data quality and provenance (different areas on the map might have been updated by different folks at different times), setting national standards that are imperfect but necessary… The section on “data maps” is critical for any statistician working with spatial data, and the chapter on bureaucratic mapping agencies will sound familiar to my Census Bureau colleagues.
I hope to post longer notes on each book sometime later.
Statisticians have always done a myriad of different things related to data collection and analysis. Many of us are surprised (even frustrated) that Data Science is even a thing. “That’s just statistics under a new name!” we cry. Others are trying to bring Data Science, Machine Learning, Data Mining, etc. into our fold, hoping that Statistics will be the “big tent” for everyone learning from data.
But I do think there is one core thing that differentiates Statisticians from these others. Having an interest in this is why you might choose to major in statistics rather than applied math, machine learning, etc. And it’s the reason you might hire a trained statistician rather than someone else fluent with data:
Statisticians use the idea of variability due to sampling to design good data collection processes, to quantify uncertainty, and to understand the statistical properties of our methods.
When applied statisticians design an experiment or a survey, they account for the inherent randomness and try to control it. They plan your study in such a way that’ll make your estimates/predictions as accurate as possible for the sample size you can afford. And when they analyze the data, alongside each estimate they report its precision, so you can decide whether you have enough evidence or whether you still need further study. For more complex models, they also worry about overfitting: can this model generalize well to the population, or is too complicated to estimate with this sample and hence is it just fitting noise?
When theoretical statisticians invent a new estimator, they study how well it’ll perform over repeated sampling, under various assumptions. They study its statistical properties first and foremost. Loosely speaking: How variable will the estimates tend to be? Will they be biased (i.e. tend to always overestimate or always underestimate)? How robust will they be to outliers? Is the estimator consistent (as the sample size grows, does the estimate tend to approach the true value)?
These are not the only important things in working with data, and they’re not the only things statisticians are trained to do. But (as far as I can tell) they are a much deeper part of the curriculum in statistics training than in any other field. Statistics is their home. Without them, you can often still be a good data analyst but a poor statistician.
This was my first PhD semester without any required courses (more or less). That means I had time to focus on research, right?
It was also my first semester as a dad. Exhilarating, joyful, and exhausting 🙂 So, time was freed up by having less coursework, but it was reallocated largely towards diapering and sleep. Still, I did start on a new research project, about which I’m pretty excited.
Our department was also recognized as one of the nation’s fastest-growing statistics departments. I got to see some of the challenges with this first-hand as a TA for a huge 200-student class.
See also my previous posts on the 1st, the 2nd, and the 3rd semester of my Statistics PhD program.
- Statistical Computing:
This was a revamped, semi-required, half-semester course, and we were the guinea pigs. I found it quite useful. The revamp was spearheaded by our department chair Chris Genovese, who wanted to pass on his software engineering knowledge/mindset to the rest of us statisticians. This course was not just “how to use R” (though we did cover some advanced topics from Hadley Wickham’s new books Advanced R and R Packages; and it got me to try writing homework assignment analyses as R package vignettes).
Rather, it was a mix of pragmatic coding practices (using version control such as Git; writing and running unit tests; etc.) and good-to-know algorithms (hashing; sorting and searching; dynamic programming; etc.). It’s the kind of stuff you’d pick up on the job as a programmer, or in class as a CS student, but not necessarily as a statistician even if you write code often.
The homework scheme was nice in that we could choose from a large set of assignments. We had to do two per week, but could do them in any order—so you could do several on a hard topic you really wanted to learn, or pick an easy one if you were having a rough week. The only problem is that I never had to practice certain topics if I wanted to avoid them. I’d like to try doing this as an instructor sometime, but I’d want to control my students’ coverage a bit more tightly.
This fall, Stat Computing becomes an actually-required, full-semester course and will be cotaught by my classmate Alex Reinhart.
- Convex Optimization:
Another great course with Ryan Tibshirani. Tons of work, with fairly long homeworks, but I also learned a huge amount of very practical stuff, both theory (how to prove a certain problem is convex? how to prove a certain optimization method works well?) and practice (which methods are likely to work on which problems?).
My favorite assignments were the ones in which we replicated analyses from recent papers. A great way to practice your coding, improve your optimization, and catch up with the literature all at once. One of these homeworks actually inspired in me a new methodological idea, which I’ve pursued as a research project.
Ryan’s teaching was great as usual. He’d start each class with a review from last time and how it connects to today. There were also daily online quizzes, posted after class and due at midnight, that asked simple comprehension questions—not difficult and not a huge chunk of your grade, but enough to encourage you to keep up with the class regularly instead of leaving your studying to the last minute.
- TAing for Intro to Stat Inference:
This was the 200-student class. I’m really glad statistics is popular enough to draw such crowds, but it’s the first time the department has had so many folks in the course, and we are still working out how to manage it. We had an army of undergrad- and Masters-level graders for the weekly homeworks, but just three of us PhD-level TAs to grade midterms and exams, which made for several loooong weekends.
I also regret that I often wasn’t at my best during my office hours this semester. I’ll blame it largely on baby-induced sleep deprivation, but I could have spent more time preparing too. I hope the students who came to my sessions still found them helpful.
- Next semester, I’ll be teaching the grad-level data visualization course! It will be heavily inspired by Alberto Cairo’s book and his MOOC. I’m still trying to find the right balance between the theory I think is important (how does the Grammar of Graphics work, and why does it underpin ggplot2, Tableau, D3, etc.? how does human visual perception work? what makes for a well-designed graphic?) vs. the tool-using practice that would certainly help many students too (teach me D3 and Shiny so I can make something impressive for portfolios and job interviews!)
I was glad to hear Scott Murray’s reflections on his recent online dataviz course co-taught with Alberto.
- Sparse PCA: I’ve been working with Jing Lei on several aspects of sparse PCA, extending some methodology that he’s developed with collaborators including his wife Kehui Chen (also a statistics professor, just down the street at UPitt). It’s a great opportunity to practice what I’ve learned in Convex Optimization and earlier courses. I admired Jing’s teaching when I took his courses last year, and I’m enjoying research work with him: I have plenty of independence, but he is also happy to provide direction and advice when needed.
We have some nice simulation results illustrating that our method can work in an ideal setting, so now it’s time to start looking at proofs of why it should work 🙂 as well as a real dataset to showcase its use. More on this soon, I hope.
Unfortunately, one research direction that I thought could become a thesis topic turned out to be a dead end as soon as we formulated the problem more precisely. Too bad, though at least it’s better to find out now than after spending months on it.
- I still need to finish writing up a few projects from last fall: my ADA report and a Small Area Estimation paper with Rebecca Steorts (now moving from CMU to Duke). I really wish I had pushed myself to finish them before the baby came—now they’ve been on the backburner for months. I hope to wrap them up this summer. Apologies to my collaborators!
- Being a sDADistician: Finally, my penchant for terrible puns becomes socially acceptable, maybe even expected—they’re “dad jokes,” after all.
Grad school seems to be a good time to start a family. (If you don’t believe me, I heard it as well from Rob Tibshirani last semester.) I have a pretty flexible schedule, so I can easily make time to see the baby and help out, working from home or going back and forth, instead of staying all day on campus or at the office until late o’clock after he’s gone to bed. Still, it helps to make a concrete schedule with my wife, about who’s watching the baby when. Before he arrived, I had imagined we could just pop him in the crib to sleep or entertain himself when we needed to work—ah, foolish optimism…
It certainly doesn’t work for us both to work from home and be half-working, half-watching him. Neither the work nor the child care is particularly good that way. But when we set a schedule, it’s great for organization & motivation—I only have a chunk of X hours now, so let me get this task DONE, not fritter the day away.
I’ve spent less time this semester attending talks and department events (special apologies to all the students whose defenses I missed!), but I’ve also forced myself to get much better about ignoring distractions like computer games and Facebook, and I spend more of my free time on things that really do make me feel better such as exercise and reading.
- Stoicism: This semester I decided to really finish the Seneca book I’d started years ago. It is part of a set of philosophy books I received as a gift from my grandparents. Long story short, once I got in the zone I was hooked, and I’ve really enjoyed Seneca’s Letters to Lucilius as well as Practical Philosophy, a Great Courses lecture series on his contemporaries.
It turns out several of my fellow students (including Lee Richardson) have been reading the Stoics lately too. The name “Stoic” comes from “Stoa,” i.e. porch, after the place where they used to gather… so clearly we need to meet for beers at The Porch by campus to discuss this stuff.
- Podcasts: This semester I also discovered the joy of listening to good podcasts.
(1) Planet Money is the perfect length for my walk to/from campus, covers quirky stories loosely related to economics and finance, and includes a great episode with a shoutout to CMU’s Computer Science school.
(2) Talking Machines is a more academic podcast about Machine Learning. The hosts cover interesting recent ideas and hit a good balance—the material is presented deeply enough to interest me, but not so deeply I can’t follow it while out on a walk. The episodes usually explain a novel paper and link to it online, then answer a listener question, and end with an interview with a ML researcher or practitioner. They cover not only technical details, but other important perspectives as well: how do you write a ML textbook and get it published? how do you organize a conference to encourage women in ML? how do you run a successful research lab? Most of all, I love that they respect statisticians too 🙂 and in fact, when they interview the creator of The Automatic Statistician, they probe him on whether this isn’t just going to make the data-fishing problem worse.
(3) PolicyViz is a new podcast on data visualization, with somewhat of a focus on data and analyses for the public: government statistics, data journalism, etc. It’s run by Jon Schwabish, whom I (think I) got to meet when I still worked in DC, and whose visualization workshop materials are a great resource.
- It’s a chore to update R with all the zillion packages I have installed. I found that Tal Galili’s installr manages updates cleanly and helpfully.
- Next time I bake brownies, I’ll add some spices and call them “Chai squares.” But we must ask, of course: what size to cut them for optimal goodness of fit in the mouth?
I never got around to polishing my Small Area Estimation (SAE) “101” tutorial materials that I promised a while ago. So here they are, though still unedited and not as clean / self-explanatory as I’d like.
The slides introduce a few variants of the simplest area-level (Fay-Herriot) model, analyzing the same dataset in a few different ways. The slides also explain some basic concepts behind Bayesian inference and MCMC, since the target audience wasn’t expected to be familiar with these topics.
- Part 1: the basic Frequentist area-level model; how to estimate it; model checking (pdf)
- Part 2: overview of Bayes and MCMC; model checking; how to estimate the basic Bayesian area-level model (pdf)
- All slides, data, and code (ZIP)
The code for all the Frequentist analyses is in SAS. There’s R code too, but only for a WinBUGS example of a Bayesian analysis (also repeated in SAS). One day I’ll redo the whole thing in R, but it’s not at the top of the list right now.
- “ByHand” where we compute the Prasad-Rao estimator of the model error variance (just for illustrative purposes since all the steps are explicit and simpler to follow; but not something I’d usually recommend in practice)
- “ProcMixed” where we use mixed modeling to estimate the model error variance at the same time as everything else (a better way to go in practice; but the details get swept up under the hood)
- “ProcMCMC” and “ProcMCMC_alt” where we use SAS to fit essentially the same model parameterized in a few different ways, some of whose chains converge better than others
- “R_WinBUGS” where we do the same but using R to call WinBUGS instead of using SAS
The example data comes from Mukhopadhyay and McDowell, “Small Area Estimation for Survey Data Analysis using SAS Software” [pdf].
If you get the code to run, I’d appreciate hearing that it still works 🙂
My SAE resources page still includes a broader set of tutorials/textbooks/examples.
Once again, CMU is hosting the
illustrious notorious SIGBOVIK conference.
Not to be outdone by the journal editors who banned confidence intervals, the SIGBOVIK 2015 proceedings (p.83) feature a proposal to ban future papers from reporting any conclusions whatsoever:
In other words, from this point forward, BASP papers will only be allowed to include results that “kind of look significant”, but haven’t been vetted by any statistical processes…
This is a bold stance, and I think we, as ACH members, would be remiss if we were to take a stance any less bold. Which is why I propose that SIGBOVIK – from this day forward – should ban conclusions…
Of course, even this provision may not be sufficient, since readers may draw their own conclusions from any suggestions, statements, or data presented by authors. Thus, I suggest a phased plan to remove any potential of readers being mislead…
I applaud the author’s courageous leadership. Readers of my own SIGBOVIK 2014 paper on BS inference (with Alex Reinhart) will immediately see the natural synergy between conclusion-free analyses and our own BS.
Hats off to my classmate Alex Reinhart for publishing his first book! Statistics Done Wrong: The Woefully Complete Guide [website, publisher, Amazon] came out this month. It’s a well-written, funny, and useful guide to the most common problems in statistical practice today.
Although most of his examples are geared towards experimental science, most of it is just as valid for readers working in social science, data journalism [if Alberto Cairo likes your book it must be good!], conducting surveys or polls, business analytics, or any other “data science” situation where you’re using a data sample to learn something about the broader world.
This is NOT a how-to book about plugging numbers into the formulas for t-tests and confidence intervals. Rather, the focus is on interpreting these seemingly-arcane statistical results correctly; and on designing your data collection process (experiment, survey, etc.) well in the first place, so that your data analysis will be as straightforward as possible. For example, he really brings home points like these:
- Before you even collect any data, if your planned sample size is too small, you simply can’t expect to learn anything from your study. “The power will be too low,” i.e. the estimates will be too imprecise to be useful.
- For each analysis you do, it’s important to understand commonly-misinterpreted statistical concepts such as p-values, confidence intervals, etc.; else you’re going to mislead yourself about what you can learn from the data.
- If you run a ton of analyses overall and only publish the ones that came out significant, such data-fishing will mostly produce effects that just happened (by chance, in your particular sample) to look bigger than they really are… so you’re fooling yourself and your readers if you don’t account for this problem, leading to bad science and possibly harmful conclusions.
Admittedly, Alex’s physicist background shows in a few spots, when he implies that physicists do everything better 🙂 (e.g. see my notes below on p.49, p.93, and p.122.)
Seriously though, the advice is good. You can find the correct formulas in any Stats 101 textbook. But Alex’s book is a concise reminder of how to plan a study and to understand the numbers you’re running, full of humor and meaningful, lively case studies.
Highlights and notes-to-self below the break:
So here at CMU, we’re proud to have one of the “largest and fastest-growing” statistics departments in the US.
Tomorrow (March 3rd) is the launch party for several new (joint-)major programs for CMU undergrads: Statistics and Machine Learning, Statistics and Neuroscience, and Mathematical Statistics. That’s in addition to two existing programs: Statistics Core and the joint program in Economics and Statistics.
If you’re in Pittsburgh, come to the launch party at 4:30pm tomorrow. We’ll have project showcases, advising, interactive demos, etc., not to mention free food 🙂
I’ve just finished an exhausting but rewarding 6 weeks teaching a summer-session course on “Experimental Design for Behavioral and Social Sciences,” CMU course 36-309. My course materials are secreted away on Blackboard, but here is my syllabus. You can also see some materials from a previous session here, including Howard Seltman’s textbook (free online).
The students were expected to have already taken an introductory statistics course. After a short review of basic concepts and t-tests, we dove into more intermediate analyses (ANOVA and regression, contrasts, chi-square tests and logistic regression, repeated measures) and into how a good study should be designed (power, internal/external validity, etc.)
I’ve taught one-off statistics workshops before, and I’ve taught once-a-week semester-long Polish language classes, but this was my first experience teaching a full-length course in statistics. Detailed notes are below.