Course Identification

Projects in data science
20243132

Lecturers and Teaching Assistants

Dr. Yaron Antebi, Prof. Yifat Merbl, Prof. Rotem Sorek, Dr. David Zeevi, Dr. Eyal Karzbrun
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Course Schedule and Location

2024
Second Semester
Wednesday, 14:00 - 16:00, FGS, Rm 1
10/04/2024
10/07/2024

Field of Study, Course Type and Credit Points

Life Sciences: Seminar; 3.00 points
Life Sciences (Computational and Systems Biology Track): Seminar; Obligatory; Regular; 3.00 points
Life Sciences (ExCLS Track): Seminar; Elective; Regular; 3.00 points

Comments

First-class will be on Wednesday, March 13, from 14-16.
Subsequent meetings will be set individually by each lecturer. 2 hrs each.

Prerequisites

Previous knowledge in programming (in R/Matlab/Python) is required for this course.

Restrictions

15

Language of Instruction

English

Attendance and participation

Obligatory

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

50%
50%

Evaluation Type

Seminar

Scheduled date 1

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Estimated Weekly Independent Workload (in hours)

5

Syllabus

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.

Timeline:
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.

Weeks 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 work to the course participants and lecturers.


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

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Website

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