Course Identification

Projects in data science

Lecturers and Teaching Assistants

Prof. Emmanuel Levy, Prof. Shalev Itzkovitz, Prof. Koby Levy, Prof. Eran Segal, Prof. Yitzhak Pilpel
Hugo Schweke

Course Schedule and Location

Second Semester
Tuesday, 16:00 - 17:00, FGS, Rm C

Field of Study, Course Type and Credit Points

Life Sciences: Seminar; Elective; Core; 3.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Seminar; Elective; Core; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Seminar; Elective; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Seminar; Obligatory; Core; 3.00 points


irst lecture will be :
Thursday, April 23rd 16:30-17:30
Subsequent lectures will be set individually by each lecturer. 2 hrs each.


previous knowledge in programming is required for this course.



Language of Instruction


Registration by


Attendance and participation

Required in at least 80% of the lectures

Grade Type

Pass / Fail

Grade Breakdown (in %)


Evaluation Type

Final assignment

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



The course is aimed to introduce the students to aspects of data analysis through working on specific projects in small groups (2-3 students). Each group will work closely with a lecturer in the course (PI from a computational biology group) to design and implement a computational analysis to address a biological question related to their lab. Each group will work independently, with weekly supervision of the lecturer, and at the end of the course they will present their work in a seminar and in writing.

Week 1:
In the first meeting each lecturer will present a small project from his lab, and students will then be divided to groups of 2-3 (considering their preferences). Each of the groups will then meet with the designated lecturer to define the specifics of the project.

week 2-13:
Each small group will work together to advance the project, and will meet with the lecturer (once a week) to discuss their progress and how to move forward .

week 14:
presentation of the groups seminars.

Expected projects:
- The projects should be purely computational and should rely only on existing data.
- The projects should be feasible within the timeframe and considering the limited computational experience of the students.
- The project should include common elements of data analysis that are likely to be useful for other computational projects.

Learning Outcomes

Upon successful completion of the course, the students will be able to:

Acquire elements of data analysis that are likely to be useful for other computational projects.

Reading List