In this course, we collectively work on student’s real (post-QE) projects and data, to derive, improve, or critically scrutinize models or data analysis techniques, to suggest alternatives, or possibly discover new interesting structures in the data.

Specifically, the aims of this course are as follows:
Learn to choose an appropriate model or data analysis method for the project.
Learn, try out and teach other students about new data analysis approaches.
Deal with limitations of real data (data size, normalizations, lack of calibration etc.).
Learn to communicate with people with whom you share data-analysis background, but not the biological topic or system; much in contrast to the typical case.
Learn structured communication. For their project, the student maintains a written record (see below for details): (i) a 2-page abstract of the project focusing on data-analysis issues, (ii) the initial presentation, (iii) key questions from the group and student’s responses (mimicking referee responses), (iv) key open data analysis issues identified by the student and suggestions by the group to explore, for all 4 cycles, (v) the final presentation.

Since this course deals with real-world problems and is not a theory course, there are typically no “absolutely correct” solutions. Rather, the point is to give suggestions and try out and evaluate alternative approaches. The role of the TA and the Instructor is to evaluate the suggestions from the group and the student to identify and make precise the tasks that the student should do as a homework and report on until the next cycle.

Target group: Students with strong quantitative background working on biological problems. This course is targeted specifically to support students with math / coding / physics / engineering background working on life-science problems, typically in life-science or interdisciplinary groups. Students with life-science backgrounds can register only if they have sufficient knowledge of math and coding (see Prerequisites).

Prerequisites: Students should be well-versed in coding and some data analysis, being able to write their own numerical code, simulation, or scripts and not just use existing packages. The required level of background is the same as for taking the DSSC Track Core course. This course does not teach basic modeling or basic coding to biology students!

Evaluation: Active participation (50%) + Written record (50%).

Teaching format: One long slot per week, for 13 weeks. Every week, the group comprising up to 6 students, the Instructor and the TA, focuses on two student’s projects. With 6 students, it takes 3 weeks to cycle through each student’s project, and the class will go through 4 such cycles. For details see "course schedule".

ECTS: 6 Year: 2019

Track segment(s):
DSSC-QUANT Data Science and Scientific Computing - Quant. and Comput. Methods in Biology

Gasper Tkacik

Teaching assistant(s):

If you want to enroll to this course, please click: REGISTER