Workshop:
Integrating Computing into the Statistics Curricula
Date: Sunday, July 13 to Thursday, July 17, 2008
Location: U.C. Berkeley
Overview |
Agenda |
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The program will cover material and work on actual labs/projects/exercises
that would make up courses in modern statistical computing and data technologies.
The goal is to help faculty in developing statistical computing
classes within their undergraduate and graduate level statistics
programs.
We will look at three high-level components of statistical computing
- Programming languages, environments and paradigms -- general purpose programming
languages with which statisticians can express statistical ideas and computations
at a relatively high level and create new algorithms and functionality;
- Information technologies -- new computational tools, techniques and standards
that allow us to access to data from varied sources and to present information
in rich, dynamic ways.
- Computational statistics -- rather than the classical topics in
numerical algorithms, we consider topics such as computer experiments, MCMC,
the EM algorithm, and re-sampling which are of importance because they are
less amenable to general purpose implemenations.
Organization
Each day will include:
- Overviews on computing topics, including why the topic is relevant,
the essential ideas that students need to see, how we might teach these topics,
and how we might integrate them into courses.
- Lab time where we will work through case studies and projects,
and discuss different approaches to using these for students.
- Discussions Topics that will be covered include:
- Projects and Case studies
- What makes a good project?
- How much help to give?
- Course Management
- Communication: news groups, team work
- Undergraduate assistants
- Types of Assignments: homework, labs, projects, exams
- Ensuring proper student conduct: sharing code
- Administrative Issues
- Site licenses
- Packages, software installation
- Student accounts
- Hosting a database, website
Last modified: Tue Apr 29 2008