Data Science and Scientific Computing track core course

Format:
The course is divided into three 4/5-week segments in which students work in interdisciplinary groups under the supervision of a DSSC faculty member. During each segment, students first learn the necessary background and tools, and are then coached by faculty to tackle a specific DSSC problem or data set in pairs. Evaluation is based on homework and written or oral reports at the end of each segment.

Topics:
• Segment 1: Basic inspection of real data (G. Tkacik)
• Segment 2: Numerical computation and optimization (M. Mondelli)
• Segment 3: Data generation, evaluation, presentation – project work (M. Robinson)
Goals:
• Provide hands-on experience and scientific insight into different DSSC problems and methodologies
• Learn about evaluation criteria for good models in different fields
• Build a community of computational / data students by project work
• Practice the following skills: handling data, extracting knowledge from data, creating models, running numerical simulations, identifying and understanding sources of error, working in mixed background teams, visualize data, written and oral communication

Target group: • Students who plan a PhD on the topic of data analysis, modeling in the life sciences or a data-driven direction of computer science/physics
• If uncertain if this is the right course for you, please consult the DSSC track representative, your mentor and/or potential future PhD supervisors

Prerequisites: • Math: multi-dimensional calculus, linear algebra, probabilities
• Programming in a language that supports numerical computation (Python, Mathematica, C/C++, Matlab)
• Note: this course does welcome students with life-science background so long as they satisfy the prerequisites; this course is not an introductory course in general modeling for students lacking any coding or math background.

Evaluation: • final grade is a grade average over the three segments; any optional bonus points in the track are capped at 110% for calculating the average
• within each segment, the grading is typically assigned based on 50% homework and 50% final mini-project, but can be adjusted and announced prior to course start by the course instructor to match the teaching curriculum

Teaching format: • classroom lectures
• student projects (in small groups)

ECTS: 6 Year: 2021

Track segment(s):
DSSC-CORE Data Science and Scientific Computing - track core course

Teacher(s):
Marco Mondelli Matthew Robinson Gasper Tkacik

Teaching assistant(s):
Simone Bombari Reka Borbely Ilse Krätschmer

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