This fall I’ll be again teaching the course, Communicating Data and Statistics.

Here’s the rough course plan. I’ll tinker with it between now and September but this is the basic idea. (The course listing is here, but that online description is out of date; the course plan linked above is more accurate.)

Here are the topics for the 13 weeks of the course:

1. Introducing yourself and telling a story

2. Principles of statistical graphics

3. Teaching

4. Making effective graphs

5. Communicating variation and uncertainty

6. Displaying fitted models

7. Giving a presentation

8. Dynamic graphics

9. Writing

10. Collaboration and the scientific community

11. Data processing and programming

12. Student projects

13. Student projects

Communication is central to your job as a quantitative researcher. Our goal in this course is for you to improve at all aspects of statistical communication, including writing, public speaking, teaching, informal conversation and collaboration, programming, and graphics.

Always keep in mind your *goals* and your *audience*.

Never forget, as one of our blog commenters reminds us: your closest collaborator is you six months ago . . . and she doesn’t reply to email!

The course is intended for Ph.D. students from all departments on campus; it is also open to some masters and undergraduate students who have particular interest in the topic.

This is my favorite course ever. As a student, you’ll get practice in all sorts of useful skills that are central to data and statistics, and you’ll participate in fast-moving conversations with fellow students with different backgrounds and experiences. In the interstices, you’ll learn all sorts of important ideas and methods in statistical design and analysis, that you’d never learn anywhere else. It’s the course where we first introduced statistics diaries. It’s the course where (a prototype version of) ShinyStan was one of the final projects!

You don’t want to miss this one.

The class will meet twice a week.

Andrew, would it be possible for NYU PhD students to audit the course?

Enrico:

Sure, as long as there’s space in the room.

If it made sense to you, would you and NYU consider posting the lectures, syllabus, slides, notes, etc. for us who are not there?

Mike:

This course has nothing to do with NYU. It’s at Columbia. I just told the other guy that if an NYU student wanted to sit in the class and there’s space in the room, that’s fine with me.

Regarding your questions: There will be no lectures. The syllabus is at the top link above. There will be no slides or notes, except to the extent that any students or I occasionally pull stuff up to share with the class. The class proceeds by discussion. So it would be difficult to share in this way.

The syllabus is there for anyone to use, also it’s a chapter in the second edition of my book with Deb Nolan, so others should feel free to teach versions of this course on their own!

For a class like this, you should consider Stephen Few’s book, “Show me the numbers, 2e”. I’d use it before Cleveland’s book, and I’m a big fan of Cleveland.

I was going to suggest the same. I teach Data Visualization, and Stephen Few’s work is fundamental. There has been significant developments in the data visualization world in the last years, coming from cognitive sciences, data journalism, and business intelligence. I’m happy to share a syllabus.