welcome to the department of computer science...broad spectrum of topics in research and education...
TRANSCRIPT
Welcome to the Department of Computer Science
Professor Ueli MaurerDirector of Studies
14 September 2020
Department ofComputer Science
14.09.2020Department of Computer Science 2
Core Faculty Data Science
Broad Spectrum of Topics in Research and Education
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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
ETH among top 5 Universities for Computer Science in 2020
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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
Start your own Company
Department of Computer Science 14.09.2020 5
46 Academic ETH Spin-offs founded since 1993
D-INFK Master Programs
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MSc Data Science
MSc Cyber Security
57 newstudents
MSc Computer Science
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
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Some Advice
Getting startedMaster‘s Program in Data Science
Department ofComputer Science
Who’s who
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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
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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]
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Tutors (Core Data Science Faculty)
...and many more Professors who can supervise Master's theses.
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Discuss and approve yourpersonal study program
List onhttps://www.inf.ethz.ch/studies/master/master-ds/faculty.html
Contact via e-mail
The Master’s Program
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Study Guide
We recommend:Read it!
• 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
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
• 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
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
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Minimum required credit points
Program Structure
Master's in Data Science 120
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Program Structure
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60
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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
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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
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
Program Structure
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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
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)
Program Structure
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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
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
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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
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
Program Structure
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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 14
Data Science Lab
Groups of three students + Presentation
Apply your knowledge and skills to
Real Data!
Interdisciplinary projects
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Program Structure
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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 14
Seminar 2
Seminar
Read and understand publications
Present a research paper
Get involved in discussions
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Program Structure
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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 2Science in Perspective 2
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
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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 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
Select a tutor
First, contact the professor you would like as a tutor.
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Select a tutor
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Select a tutor
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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
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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
• 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
• 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
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.
Exam times
3 pm – 4 pm
really means
3pm – 4pm
This does not apply to exams, meetings, etc.
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Student Portal
https://ethz.ch/students/en.html
All the best for your studies
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