Course Policies Handout

This is the course handout for the actual course as it is taught at Brandeis.



The interpretation of data is key to making new discoveries, making optimal decisions, and designing experiments. Students will learn skills of data analysis through hands-on, computer-based tutorials and exercises that include experimental data from the biological sciences. Knowledge of very basic statistics (mean, median) will be assumed. Usually offered every second year.

Structure of the course:

In-class Labs – Classroom time is devoted to self-guided tutorials. The professor may occasionally give introductory remarks, and will be available for questions.

Problem sets / homework sets  – Homework sets are completed outside of class on the computer.  There are 6 homework sets during the course.

Team projects – At about 3 points during the course, there will be a team exercise. Students will split into teams to create 1 or 2 figures that address a particular question. One member of the team will briefly present the figure during a designated class session.

The course is designed in a hands-on manner because it is crucial to integrate the conceptual thinking with the mathematical and computer skills needed to actually carry out the analysis. The in-class labs are designed to break down, into very small parts, the concepts and skills that are required to address certain questions. The purpose of the homework is to allow students to practice what they have learned and to develop the ability to solve similar problems independently. The team projects allow students to work in groups, much as one would in a real research laboratory, in order to share ideas, solutions, and criticisms, and to receive feedback from other groups.

How to get the most out of the labs

Some of you may be doing these labs for credit in the course, others may be doing them to learn how to use Matlab on your own or as you are working in a lab. To get the most out of the labs:
  • Type in the example code yourself, rather than cutting and pasting. If you type it out yourself, you'll start to develop familiarity with the language.
  • Make sure you go slowly enough for the material to sink in. We all get used to skimming web pages, but you'll get more out of the tutorials if you can slow yourself down and think as you read.
  • Even if you are not taking the course for credit, answer the questions as you go. This will help to ensure that you are thinking about the material as you go.

Required software and books:

The course requires access to Matlab. Matlab is available in the Goldfarb and Farber computer clusters here on campus, and is also available in many research laboratories. Students who don't have easy access to Matlab in one of these locations can install Matlab using the university's site license; see Setting Up Shop at Home

In addition, I've required 2 textbooks; 1 is a basic introduction to Matlab for scientists and engineers, while the other is a statistics book for the biological sciences. While I believe the tutorials will make it easy to learn the concepts and to work with data, it will be useful to have these textbooks to use as a reference.  These books are on reserve in the Main Library on campus. At various times, you will need access to the books to complete some of the problem sets.

In the 2011-2012 course, we used this Matlab text that may be of interest to some students:
Readers might also be interested in the following book by a neuroscientists:

Collaboration policy:

Students are encouraged to discuss the homework sets and help each other, with some limitations:
  • Students should attempt each homework question for 20 minutes before seeking help on that portion from other students or the professor.
  • Students may talk about concepts, discuss lines or snippets of code in emails, message boards, or on a whiteboard, and may debate whether various solutions are correct.
  • Students are not permitted to exchange complete '.m' files; each student should write their own '.m' files themselves and understand what they are turning in.
  • When you have consulted other students while completing your homework, credit them by including their names.

Asking questions:

We will use the site piazza.com to handle questions. When anyone asks a question, the entire class will be able to see the answer. If the question is not a clarifying question, I am very likely to give a hint rather than spelling out the solution, so leave yourself plenty of time to complete the assignments. See the "Ask a question" link on the left side bar to log-in.  You can also send a private question to the instructor if you have a question about your grade.

In addition, there are office hours in my office [SCHEDULE]. The TA will also have office hours [SCHEDULE].

Grading:

The overall course grade will consist of a weighted average of scores for in-class labs (20%), out-of-class problem sets (due about once per 2 weeks) (45%), and team projects (35%). There is no final exam but problem sets will probably require more time for completion than in a typical course. It is important to begin work on the problem sets early, as typically they will be difficult to complete on the last day.  Each student can turn in 1 assignment up to 4 days late without penalty; other problem sets will lose 10% of the maximum credit per hour they are late.


Copyright 2011-2013 Stephen D. Van Hooser
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