welcome to the department of computer science...broad spectrum of topics in research and education...
TRANSCRIPT
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Welcome to the Department of Computer Science
Professor Ueli MaurerDirector of Studies
14 September 2020
Department ofComputer Science
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14.09.2020Department of Computer Science 2
Core Faculty Data Science
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Broad Spectrum of Topics in Research and Education
14.09.2020Department of Computer Science 3
Data Management
Machine Learning
Information Security
Probability theory
Insurance mathematics, stochasticfinance
Programming Languages
Statistics
Parallel Computing
Visual Computing
Computer Engineering
System Security
Mathematical Information Science
…und many more
Signal processing
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ETH among top 5 Universities for Computer Science in 2020
14.09.2020Department of Computer Science 4
Rank 2020 Institution Country
1 University of Oxford United Kingdom
2 Stanford University United States
3 ETH Zurich Switzerland
4 Massachusetts Institute of Technology United States
5 University of Cambridge United Kingdom
Source: https://www.timeshighereducation.com/world-university-rankings/2020/subject-ranking/computer-science
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Start your own Company
Department of Computer Science 14.09.2020 5
46 Academic ETH Spin-offs founded since 1993
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D-INFK Master Programs
14.09.2020Department of Computer Science 6
MSc Data Science
MSc Cyber Security
57 newstudents
MSc Computer Science
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Take advantage of the unique opportunity of studying at ETH
Attend classes, interact with TAs and faculty
Make this not only a degree, but a major step in your career
Self-reflection
14.09.2020Department of Computer Science 7
Some Advice
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Getting startedMaster‘s Program in Data Science
Department ofComputer Science
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Who’s who
B. Gianesi / G. Fourny 9
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Who’s who
Master's programin Data Science
Computer Science
Mathematics
Electrical Engineering
ETH has 16 departments,identified with four letters
(D-AAAA)
A joint program accross three departments
B. Gianesi / G. Fourny 10
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Who’s who
Prof. David BasinHead of Department
Prof. Ueli MaurerDirector of StudiesExamination regulationsValidation of examinationresults
Bernadette GianesiStudies AdministrationMain point of [email protected]
Dr. Ghislain FournyStudy CoordinatorQuestions on planning your studiesand course [email protected]
B. Gianesi / G. Fourny 11
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Tutors (Core Data Science Faculty)
...and many more Professors who can supervise Master's theses.
B. Gianesi / G. Fourny 12
Discuss and approve yourpersonal study program
List onhttps://www.inf.ethz.ch/studies/master/master-ds/faculty.html
Contact via e-mail
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The Master’s Program
B. Gianesi / G. Fourny 13
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B. Gianesi / G. Fourny 14
Study Guide
We recommend:Read it!
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• ECTS credits (European Credit Transfer System)
• Course completed successfully full amount of credits is awarded
• 30 credits per semester
Master’s program: 120 credits
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Credit System
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120 Credit Points
The master’s program is designed to be completed within 4 semesters. The overall study duration must not exceed 8 semesters. The last semester focuses completely on the Master’s thesis.
Semester 3
30 credits
Semester 4
30 credits
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Semester 1
30 CP
Semester 2
30 CP
Semester 3
30 CP
Semester 4
30 CP
4 more semesters of
leeway
Recommended CP / SemesterHard limit at4 years
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• Pass: grade ≥ 4• Fail: grade < 4• Resolution for individual grades: 0.25
Repetition of examsEvery examination or project can be repeated once.
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Grading System
6 Very good5 Good4 Sufficient3 Insufficient2 Poor1 Very poor
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Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
4 up
to y
ou
Data Science Lab 14Seminar 2
Master's Thesis 30Science in Perspective 2
Master’s Program Structure
B. Gianesi / G. Fourny 18
Minimum required credit points
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Program Structure
Master's in Data Science 120
B. Gianesi / G. Fourny 19
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Program Structure
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60
B. Gianesi / G. Fourny 20
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Core courses
High level of competence in Data Science
Solid and sound knowledge basis.
Lectures Exercises Self-studying Projects+ + +
Exam+
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Program Structure
B. Gianesi / G. Fourny 22
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
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Program Structure
B. Gianesi / G. Fourny 23
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
Does not sum up:
freedom
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Program Structure
B. Gianesi / G. Fourny 24
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
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Core courses
Data Analysis: Information & LearningAdvanced Machine Learning (10)Neural Network Theory (4)Mathematics of Information (8)
Data Analysis: StatisticsFundamentals of Mathematical Statistics (10)Computational Statistics (8)
Data Management and ProcessingBig Data (10)Advanced Algorithms (9)Optimization for Data Science (8)
Core ElectivesA lot of choice (30+ courses)
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Core courses
Roughly:
At last one here
At least one here
At least two here
At least two here
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Data Analysis: Information & LearningAdvanced Machine Learning (10)Neural Network Theory (4)Mathematics of Information (8)
Data Analysis: StatisticsFundamentals of Mathematical Statistics (10)Computational Statistics (8)
Data Management and ProcessingBig Data (10)Advanced Algorithms (9)Optimization for Data Science (8)
Core ElectivesA lot of choice (30+ courses)
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Program Structure
B. Gianesi / G. Fourny 27
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
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Interdisciplinary electives
Bridge the gap with other disciplinesculturesmindsets
Data Science would not exist without
Data!8-12 credits
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Interdisciplinary electives
Course compilations
Computational Biology, Bioinformatics, and Biomedicine
Computer Networks
Finance and Insurance
Geographic Information Systems
Law, Policy, and Innovation
Neural Information Processing
Social Networks
Transportation Systems
Weather and Climate Systems
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Program Structure
B. Gianesi / G. Fourny 30
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
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Program Structure
B. Gianesi / G. Fourny 31
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
4 up
to y
ou
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Program Structure
B. Gianesi / G. Fourny 32
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
4 up
to y
ou
Data Science Lab 14
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Data Science Lab
Groups of three students + Presentation
Apply your knowledge and skills to
Real Data!
Interdisciplinary projects
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Program Structure
B. Gianesi / G. Fourny 34
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
4 up
to y
ou
Data Science Lab 14
Seminar 2
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Seminar
Read and understand publications
Present a research paper
Get involved in discussions
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Program Structure
B. Gianesi / G. Fourny 36
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
4 up
to y
ou
Data Science Lab 14Seminar 2Science in Perspective 2
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Science in Perspective
Humanities and Social Sciences
Language courses 851-xxxx-xx(≤ 3 credits including ETH BSc)
Coordinator's pick:Big Data, Law, and Policy
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Program Structure
B. Gianesi / G. Fourny 38
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Data Management and Processing 16
Core Electives 10
Information and Learning 8
Statistics 8
18 u
p to
you
Interdisciplinary Electives 8
4 up
to y
ou
Data Science Lab 14Seminar 2
Master's Thesis 30Science in Perspective 2
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Master's Thesis
This is the final step!
6 months of researchand complex problemsolving
(And think about your future... maybe a PhD?)
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General recommendations
• Stick to 30 credits per semester (don't overload)
• Start with the core courses
• Data Science Lab after interdisciplinary courses, ideally in same field
At least 8 CP must habe been obtained under Data Analysis and 8 CP under Data Management. Interdisciplinary courses are not mandatory to have been taken priorto Data Science Lab
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Exchange programs
Prof. Dr. Bernd Gärtner
(No credits for "core core" courses and Data Science Lab)
Dr. Claudia Otto
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• Only for students with a ETH bachelor degree
• Not in the first semester
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Plan your studies
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Select a tutor
First, contact the professor you would like as a tutor.
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Select a tutor
B. Gianesi / G. Fourny 44
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Select a tutor
B. Gianesi / G. Fourny 45
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Create your learning agreement
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Create your learning agreement
First, meet your tutor and discuss your study plan.
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Fill your learning agreement
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Submitting your learning agreement
Submit your learning agreement after discussing with your tutor.
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Submitting your learning agreement
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Academic Year
R. Sasse / B. Gianesi 51
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Autumn Semester
examination registration
(until end of 4th
week)
course registration (until end of 2nd week)
beginning of term end of term
term term brake
end of term examinations session examinations
deregistration end of term
examinations
deregistration session
examinations
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Spring Semester
course registration (until end of 2nd week)
end of term examinations session examinations
deregistration end of term
examinations
deregistration session
examinations
examination registration
(until end of 4th
week)
end of term
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• Important deadline (course registrations, exam registration and deregistration, etc.) are always announced ahead of time via e-mail [email protected]
• Make sure you read your e-mails!
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Deadlines
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• Solve the exercises during the semester
• Solve old examinations (available from the student body, i.e. VIS)
• Oral examinations: Get minutes of former examinations from VIS
• If you have questions, ask your fellow students or the assistants
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Preparing Examinations
highly, highly, highly recommendedto attend all lectures and exercise sessions
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Course times
3 pm – 4 pm
actually means
3:15 pm – 4:00 pm
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• At ETH Zentrum (here): Classes start a quarter past the indicated hour.
• On Hönggerberg Campus: depends on exact building course is in! see https://ethz.ch/students/en/studies/academic-support/course-catalogue/lectures-times.html
• Also check https://ethz.ch/students/en/studies/academic-support.html for general information – including lecture recording links, etc
• Autumn semester 2020: The lecturers will communicate the exact lesson times of ONLINE courses.
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Exam times
3 pm – 4 pm
really means
3pm – 4pm
This does not apply to exams, meetings, etc.
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R. Sasse / B. Gianesi 58
Student Portal
https://ethz.ch/students/en.html
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All the best for your studies
B. Gianesi / G. Fourny 59