Course Name: ST599 Statistical Computing and Big Data
Term: Spring 2014
Prerequisites: ST 412/512 or ST 552 or equivalent
Lectures: MWF 900-950 OWEN 103
Charlotte Wickham, 76 Kidder
Alix Gitelman, 48 Kidder
Wickham: 1-2pm WF in 76 Kidder
Gitelman: 2-3pm M in 48 Kidder
We’ll start with data that is just a little bigger and a little messier than you are used to and focus on strategies for making big data small. We’ll progress to tools for extending our skills to datasets that exceed the capabilities of our local computer. Once we are equipped to physically handle big data, we’ll explore some of the statistical issues that arise. The class revolves around three big data analysis projects that you will work on in groups. By the end of the course you will confidently approach massive data sets and be aware of the possible statistical pitfalls.
R will be our lingua franca, but expect to pick up a little of some other languages along the way.
Class time will consist of discussion, group exercises and problem solving, group project work and some lectures.
Course materials, lecture notes, handouts and readings will be posted on the class website stat599.cwick.co.nz. Blackboard will be used for submitting questions, project reports and team evaluations, and to record grades.
There is no textbook for this class. Readings will be assigned for each topic. You will also be expected to research some concepts and computing tools on your own.
|2-3||Getting started with big data|
|4||Project 1 presentations|
|5-6||Statistical issues with big data|
|7||Project 2 presentations|
|10||Project 3 presentations|
You will be assigned groups for the first project at the start of the second week. At the completion of that project groups will be rearranged, unless there is unanimous agreement from all group members to stay together. Group policies and guidelines will be discussed at the start of the second week.
For each project, your group will be responsible for:
For each project, you will also individually submit:
Accommodations are collaborative efforts between students, faculty and Disability Access Services (DAS). Students with accommodations approved through DAS are responsible for contacting me prior to or during the first week of the term to discuss accommodations. Students who believe they are eligible for accommodations but who have not yet obtained approval through DAS should contact DAS immediately at (541) 737-4098.
Academic dishonesty is a serious offense and will be addressed following the guidelines set out in the Academic Regulations of OSU (go to http://catalog.oregonstate.edu, click on Registration Information then Academic Regulations, and read AR 15).
The Student Conduct Code defines Academic dishonesty as
… an act of deception in which a Student seeks to claim credit for the work or effort of another person, or uses unauthorized materials or fabricated information in any academic work or research, either through the Student’s own efforts or the efforts of another.
Examples include, but are not limited to, the following:
You are responsible for knowing what academic dishonesty is, and for avoiding it. Ignorance of these rules does not absolve you from responsibility.