Date: Sept 19th‐20th, 2016, 9 am ‐ 5 pm

Let’s face it. Eventually you’re going to have to analyze data, maybe a lot of data or maybe just a moderate amount, but you can’t (or don’t want) to analyze the data in excel (those functions and pivot tables are a pain). Or maybe your data are really, really messy and you just can’t envision sitting at the computer entering values one by one. You might also be really worried that in two years – after Science and Nature go on bidding war for the right to publish your paper – you’ll be asked for the analysis code and as you read through your old code, you’ll find yourself saying, ‘Geez, what was I doing here?’

If any of this fits your learning anxieties or aspirations, this bootcamp will be perfect. You’ll learn R in a semi‐structured setting with lots of other people also learning R. We’ll spend some time on the various ways to bring data into R. We’ll walk through the nuts and bolts of data visualization, and explore different methods for analyzing the data. You’ll also acquire some basic coding skills and learn to maintain robust, commented analysis/coding archives. The course will also include several data challenges and you’ll get to work with a team to develop a solution.

Don’t worry if you don’t know anything about programming. We’re going to focus on ways to develop intuition about how to fetch, structure, and explore your data. The class will be interdisciplinary and lots of different kinds of data will be explored. It would be good if you knew some basic probability, but if you don’t, you’ll learn enough to be dangerous (and we will point you toward some great stat classes that will strengthen your statistical thinking skills). In short, this will be a good class for those who want to learn R and develop basic knowledge about data science.

There is limited class enrollment and our aim is to take students with a variety of disciplinary backgrounds. So sign up as soon as possible.

To sign up, go to:

Instructors: Duncan Temple Lang, Deb Niemeier