Resources and other notes from ICOTS 10
A collection of interesting resources, recommendations and bits and bobs from the 10th International Conference on Teaching Statistics (ICOTS) in Kyoto, Japan, 8 - 13 July. Thanks to the University of Auckland’s Department of Statistics for funding my attendance, and to my amazing co-author Anna Fergusson, @annafergussonnz, for letting me work with her.
Liza’s one sentence summary
This list is to help me keep track of all the things I want to check after this conference, and I hope it might help you too.
- Slide links
- Other links
- Very rough notes from Session 3A
A Goodreads list here.
- The Art of Data Science: A Guide for Anyone Who Works with Data by Roger Peng and Elizabeth Matsui (as mentioned in Hilary Parker’s keynote, Parker and Peng podcast together)
- Sprint by Jake Knapp (‘Sprint’ is a design process Hilary Parker worked through at Stitch Fix, where she is a data scientist)
- Designerly Ways of Knowing by Nigel Cross (also mentioned by Hilary Parker)
- Dear Data by by Giorgia Lupi and Stefanie Posavec, with forward by Maria Popova (gorgeous data visualisations that inspired some activities in Andee Rubin’s data camps for middle schoolers)
- Factfulness: Ten Reasons We’re Wrong About the World – and Why Things Are Better Than You Think by Hans Rosling, Ola Rosling and Anna Rosling Rönnlund (Anna was the Tuesday Keynote. It was the topic of a Gates Notes in April.)
- Weapons of Math Destruction by Cathy O’Neil (highly recommended by Jo Hardin, @jo_hardin47)
- Thinking, Fast and Slow by Daniel Kahneman (not mentioned by anyone in particular except me)
- International Handbook of Research in Statistics Education edited by Dani Ben-Zvi, Katie Makar, Joan Garfield
- Saving Capitalism from Short-Termism: How to Build Long-Term Value and Take Back Our Financial Future by Alfred Rappaport - not sure if this was an intentional recommendation by Chris Wild, but I thought it looked interesting when he had it up on his short-termism slide.
- Ziliak, Stephen T. 2008. “Retrospectives: Guinnessometrics: The Economic Foundation of “Student’s” t.” Journal of Economic Perspectives, 22 (4): 199-216. (from Hilary Parker’s keynote - where my Gosset lovers at?)
- The Art of Data Science by Richard Boire
- A Survey of Statistical Capstone Projects by Susan E. Martonosi and Talithia D. Williams
- All the articles in Hadley Wickham and Jenny Bryan’s PeerJ Collection Practical Data Science for Stats
- Rolf Biehler’s paper from 20 years ago on software for teaching and learning statistics, recommended by Mine Çetinkaya-Rundel, @minebocek, after session 1B today.
- I asked philosopher Karen François what article of a philosophical nature she would want all statisticians to read, and she recommended “Beyond subjective and objective in statistics” by Andrew Gelman and Christian Hennig. She also references “The ethics of algorithms: Mapping the debate” by Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi.
- Hilary Parker and Roger Peng’s Not So Standard Deviations. Hilary Parker, @hspter, was an awesome keynote speaker (Monday 9, July), too.
- Mona Chalabi’s Strange Bird, thanks to Amy Hogan, @alittlestats
- Hilary Parker’s, @hspter, fabulous keynote, “Cultivating creativity in data work”.
The slides for my keynote address at #icots10. I'm so honored that I was asked to speak, and was thrilled to do so in Japan. I used the opportunity to combine just about everything I"m passionate about into one talk! https://t.co/GTBikqyoDe— Hilary Parker (@hspter) July 13, 2018
- Our talk on modern data in a large introductory statistics course
- Tim Erickson’s talk on “Data Moves: on ekey to data science at the school level”
- Robin Lock’s talk entitled “Connecting Intuitive Simulation-Based Inference to Traditional Methods”
- Bill Finzer’s talk on “Co-design of the Common Online Data Analysis Platform (CODAP) for Cross-disciplinary Use in Grades 6–14”
- Chris Wild’s talk on “Gaining iNZights from data”
- Jo Hardin’s, @jo_hardin47, talk on “Challenges and Opportunities: statistics and data science undergraduate programmes
- Mathieu Thibault’s, @ThibaultMat, talk on “A Survey of Teachers Self-reported Practices of Probability Teaching in Primary and Secondary School Levels in Québec”
- Chris Wild’s keynote “Through a glass darkly” - another truly Wild talk
- Amelia McNamara’s, @AmeliaMN, talk on “Imagining the future of statistical software”. Lots of great short links to resources in here, too!
- Jackie Carter’s, @JackieCarter, talk on “Innovations in statistical (by stealth) training: reflections from the UK Q-step Initiative”.
- Hadley Wickham’s, @hadleywickham, talk “Should all statistics students be programmers?”.
- Stacey Hancock’s talk on “Infusing Data Visualization into Intro Stat Using Tableau”. Great example of a news story that motivates multivariable visualisation. Data on Graduation Rate vs In-State Tuition by Type of Institution and why state legislators really need better stats education (in my opinion).
- Charalampos Chanialidis’s, @cchanialidis, talk on “The use of technology and social media in teaching Statistics”
- Help! I can’t find the Hilary Mason, @hmason, tweet asking about what you do first when you get new data. Hilary Parker mentioned it and I’d love to read the responses. Can you help me find it? Hit me up on Twitter.
- Most of my tweets from this conference are in the below thread.
- Hadley showed this very readable code when he was a discussant for Session 3A and in another of his talks. It highlights the value of being able to read and get the gist of even unfamiliar code and the value of languages that can do that.
- iNZight - mentioned by lots of people, maybe start with Chris Wild’s talk on “Gaining iNZights from data”
- codap - mentioned by lots of people, maybe start with Bill Finzer’s talk on “Co-design of the Common Online Data Analysis Platform (CODAP) for Cross-disciplinary Use in Grades 6–14”
- JMP - Volker Kraft (Session 3C)
- Rawgraphs - mentioned by Charlotte Bolch (Session 3C)
- statcheck - see also:
- Tableau is free for students and academic use and Stacey Hancock’s talk on “Infusing Data Visualization into Intro Stat Using Tableau” has some great examples, and details of assessment.
Where do ICOTS folk go between ICOTS? Send me your recommendations!.
- Joint Statistical Meetings (JSM) in Vancouver later this month. Anna Fergusson, @annafergussonnz, will be presenting there too! This conference is yearly.
- ISI World Statistics Congress - Kuala Lumpur next year
- The satellite conference is the week of August 12, 2019 and August 18 - 23 is the main congress.
- 2020 IASE Roundtable: China prior to ICME14
- 2021 IASE Satellite meeting before World Statistics congress in the Netherlands
- Australian Consortium for Social and Political Research Incorporated (ACSPRI) - recommended to Jackie Carter, and passed on to me. For people interested in quantitative social science.
- Resources mentioned by Charlotte Bolch:
- More about Data Moves—and R (on data moves and dplyr verbs)
- Mobilize -introdatascience.org
- Zen and the Art of Data Science Maintainence by Jabe Wilson (mentioned by Hilary Parker)
- Gapminder Foundation and the Dollar Street Matrix
- Alison Gibbs and Nathan Taback’s Univeristy of Toronto course: An Introduction to Statistical Reasoning and Data Science
- infer package in R for tidyverse inference
- Happy Git and GitHub for the useR recommended by Jo Hardin, @jo_hardin47
- A tweet on #ICOTS10 got me wondering about a battle of conference hashtags. This investigation is like a quickly whipped up version of the personal storytelling in our talk on modern data in a large introductory statistics course.
Any great recommendations I’ve missed? Let me know, fellow ICOTSers, on Twitter.
Very rough notes from Session 3A
Data Science for ALL - a stroll in the foothills by Jim Ridgway, University of Durham
Take homes: Just because there is a new game in town, doesn’t mean you should forget everything you know. There are new models and new modellers, so adaptation is necessary to stay relevant.
The roots of our discipline come from people wanting to tackle real problems using evidence and collaborate with anyone sympathetic. But more recent critiques suggest we’re stuck in the early 1900s.
“More and different data (open data, text, images, sensor data…)”
Are Statistics and Computing “worlds in conflict”?
The Death of Theory, (Anderson 2008).
Challenges - new analytic techniques, black box models, unanticipated consequences.
Modelling is process NOT content.
“arithmetic is actually a very dangerous thing to teach people as it can be applied in all sorts of bad way”. If I have a letter for house 12 and one for house 8, I can put both in the letter box for house 20 and be done with it.
Tom Leher quote - life is like a sewer. This is a problem for algorithmic models.
Ridgway’s 3 modelling styles
- Linear, additive (e.g. school physics)
- Systems models (e.g. school biology)
- Macro-systemic models (e.g. evolution of the earth; Trump; Brexit) - unstable over time, layers of systems, VERY vulnerable to error
“Models should be viewed as ephemeral things, NOT rock sculptures”
Data Science: Supporting Evidence Informed Decision making
- Problem exploration
- Large scale datasets
- Data visualisation
- Data collection
- Measurement issues (what IS poverty?)
- What tools?
- Decision making
- ‘What if?’, what are the consequences? how will people ‘game’ the system?
Sampling - just having a big sample doesn’t save you. Who are you trying to generalise to?
Teaching Statistics in the Era of Data Science by Alison Gibbs, University of Toronto
Data science as at the intersection of statistics, computation and domain expertise. Want students to develop a rigorous mindset plus more computational and algorithmic thinking, more real world problems.
“Never become so much of an expert that you stop gaining expertise. View life as a continuous learning experience” - Denis Waitley. A good quote to describe adaptive experts. Flexible, innovative, a mindset for continuous learning and can take their knowledge and expertise and work in new contexts.
“A subject area is ultimately about the doing of a subject - using the content in a disciplined way…[not] a march through content.” - Wiggins and McTighe
Small group tutorials are just as important as lectures. Tutorials have an emphasis on communication and group work.
Challenges and opportunities for undergraduate data science major and minor degree programs by Jo Hardin, Pomona College
Donoho 2017 article, on greater data science - data science IS statistics? Jo’s goal for this talk: You will update something in everything class you teach, every time you teach it.
A survey of statistical capstone projects paper - find this article!
Update 2: Computation in statistical theory Including simulation makes a big difference to student understanding.
Update 3: Ethics Weapons of Math Destruction
Great Wald story about bullet holes on planes. Where do you reinforce a plane? Where there aren’t any bullet holes on the planes you can salvage.
“Crime data doesn’t exist…the only things we have is arrest data”
Update 4: New/augmented learning outcomes Like confounding and cross validation!
Update 5: Faculty development Use the tidyverse.
Update 6: Reproducibility Use R and R Markdown, Git and GitHub. “If they don’t know Git an GitHub, they’re not getting jobs”.
Update 7: Visualisation Not doing it badly, and/or doing it in sophisticated ways and/or doing it creatively.
Discussant: Hadley Wickham
Important skills: SQL, git, json data, Rmarkdown Ability to combine code and prose together in reproducible ways.