||Department of Computer Science
Monday, 25 February 2019, 12:15 hCAB G 51
25.02.2019B. Gianesi / G. Fourny 1
Specialized Master’s programData Science
||Department of Computer Science
Master’s program Data Sciencewww.inf.ethz.ch/data-science
Master’s program / Application / Admission
25.02.2019B. Gianesi / G. Fourny 2
||Department of Computer Science
Structure Master’s program Data Science Course catalogue Design principles Eligibility Application + Documents
25.02.2019B. Gianesi / G. Fourny 3
Agenda
||Department of Computer Science
Structure Master’s program Data Science Course catalogue Design principles Eligibility Application + Documents
25.02.2019B. Gianesi / G. Fourny 4
Agenda
||Department of Computer Science
StructureMaster in Data Science 120
Core Courses and Interdisciplinary Electives 72Core Courses 60
Data Analysis 16Information and Learning 8
Statistics 8
Data Management and Processing 16
Core Electives 10
Interdisciplinary Electives 8
Data Science Lab 14
Seminar 2
Science in Perspective 2
Master's Thesis 30
25.02.2019B. Gianesi / G. Fourny 5
||Department of Computer Science
120 Credit points
The master’s program is designed to be completed in 4 semesters. The overall study duration may not exceed 8 semesters. The last semester is completely focused on the Master’s thesis.
Semester 3
30 credits
Semester 4
30 credits
25.02.2019B. Gianesi / G. Fourny 6
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
||Department of Computer Science
Program structure
Master in Data Science 120
25.02.2019B. Gianesi / G. Fourny 7
||Department of Computer Science
Program structure
Master in Data Science 120Core Courses and Interdisciplinary Electives 72
Minimum required credit points
25.02.2019B. Gianesi / G. Fourny 8
||Department of Computer Science
Program structure
Master in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60
25.02.2019B. Gianesi / G. Fourny 9
||Department of Computer Science
Program structure
Master 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
25.02.2019B. Gianesi / G. Fourny 10
||Department of Computer Science
Program structure
Master 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
25.02.2019B. Gianesi / G. Fourny 11
18 u
p to
you
||Department of Computer Science
Program structure
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Information and Learning 8
Statistics 8
Data Management and Processing 16
Core Electives 10
Interdisciplinary Electives 8
18 u
p to
you
25.02.2019B. Gianesi / G. Fourny 12
||Department of Computer Science
Interdisciplinary Electives
Bridge the gap with other disciplinesculturesmindsets
Data Science would not exist without
Data!8-12 credits
25.02.2019B. Gianesi / G. Fourny 13
||Department of Computer Science
Interdisciplinary Electives
25.02.2019B. Gianesi / G. Fourny 14
Course compilations
• Computational Biology, Bioinformatics, and Biomedicine
• Computer Networks• Finance & Insurance• Geographic Information Systems• Law, Policy, and Innovation• Neural Information Processing• Social Networks• Transport Planning and Systems• Weather and Climate Systems
||Department of Computer Science
Program structure
Master in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Information and Learning 8
Statistics 8
Data Management and Processing 16
Core Electives 10
Interdisciplinary Electives 8
Data Science Lab 14
18 u
p to
you
4 up
to y
ou
25.02.2019B. Gianesi / G. Fourny 15
||Department of Computer Science
Data Science Lab
Groups of three students + Presentation
Apply your knowledge and skills to
Real Data!Interdisciplinary projects
25.02.2019B. Gianesi / G. Fourny 16
||Department of Computer Science
Program structure
Master in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Information and Learning 8
Statistics 8
Data Management and Processing 16
Core Electives 10
Interdisciplinary Electives 8
Data Science Lab 14
18 u
p to
you
4 up
to y
ou
Seminar 2
25.02.2019B. Gianesi / G. Fourny 17
||Department of Computer Science
Seminar
Read and understand publications
Present a research paper
Get involved in discussions
25.02.2019B. Gianesi / G. Fourny 18
||Department of Computer Science
Program structure
Master in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60Data Analysis 16
Information and Learning 8
Statistics 8
Data Management and Processing 16
Core Electives 10
Interdisciplinary Electives 8
Data Science Lab 14
18 u
p to
you
4 up
to y
ou
Seminar 2
Science in Perspective 2
25.02.2019B. Gianesi / G. Fourny 19
||Department of Computer Science
Science in Perspective
Humanities and Social Sciences
Language courses 851-xxxx-xx(≤ 3 credits including ETH BSc)
25.02.2019B. Gianesi / G. Fourny 20
||Department of Computer Science
Program structureMaster in Data Science 120
Core Courses and Interdisciplinary Electives 72Core Courses 60
Data Analysis 16Information and Learning 8
Statistics 8
Data Management and Processing 16
Core Electives 10
Interdisciplinary Electives 8
Data Science Lab 14
Seminar 2
Science in Perspective 2
Master's Thesis 30
18 u
p to
you
4 up
to y
ou
25.02.2019B. Gianesi / G. Fourny 21
||Department of Computer Science
Structure Master’s program Data Science Course catalogue Design principles Eligibility Application + Documents
25.02.2019B. Gianesi / G. Fourny 22
Agenda
||Department of Computer Science
Long-Term Course Catalogue
25.02.2019B. Gianesi / G. Fourny 23
Master's Program in Data Science: Long-term Course CatalogLong-term view on the course list. Please find information on the individual courses in ETH's course catalog for each semester under www.vvz.ethz.ch.
Course category and course names Sem. Cr. Period. Dep. Lecturer
CORE COURSES
252-0535-00 Advanced Machine Learning HS 8 yearly D-INFK J. Buhmann227-0434-10 Mathematics of Information FS 8 yearly D-ITET H. Bölcskei
401-3621-00 Fundamentals of Mathematical Statistics HS 10 yearly D-MATH S. van de Geer401-3632-00 Computational Statistics FS 8 yearly D-MATH N. Meinshausen, M. Maathuis
263-3010-00 Big Data HS 8 yearly D-INFK G. Fourny263-4500-00 Advanced Algorithms HS 8 yearly D-INFK M. Ghaffari, A. Krause261-5110-00 Optimization for Data Science FS 8 yearly D-INFK B. Gärtner
261-5130-00 Research in Data Science HS/FS 6 yearly all252-0417-00 Randomized Algorithms and Probabilistic Methods HS 7 yearly D-INFK A. Steger, E. Welzl263-0006-00 Algorithms Lab HS 6 yearly D-INFK A. Steger, E. Welzl263-0007-00 Advanced Systems Lab HS 6 yearly D-INFK G. Alonso252-1414-00 System Security HS 5 yearly D-INFK S. Capkun, A. Perrig263-3210-00 Deep Learning HS 4 yearly D-INFK F. Perez-Cruz263-5210-00 Probabilistic Artificial Intelligence HS 4 yearly D-INFK A. Krause 263-2400-00L Reliable and Interpretable Artificial Intelligence HS 4 yearly D-INFK M. Vechev263-2800-00L Design of Parallel and High-Performance Computing HS 7 yearly D-INFK T. Hoefler, M. Püschel263-5902-00 Computer Vision HS 6 yearly D-ITET M. Pollefeys252-0211-00 Information Security FS 8 yearly D-INFK D. Basin, S. Capkun263-0008-00 Computational Intelligence Lab FS 6 yearly D-INFK T. Hofmann
Core Electives
Data Management and Processing
Data Analysis: Information & Learning
Data Analysis: Statistics
||Department of Computer Science
Core courses
Roughly:
At last one here
At least one here
At least two here
At least two here
Data Analysis: Information & LearningMachine Learning (8)Mathematics of Information (8)
Data Analysis: StatisticsFundamentals of Mathematical Statistics (10)Computational Statistics (10)
Data Management and ProcessingBig Data (8)Advanced Algorithms (8)Optimization for Data Science (8)
Core ElectivesA lot of choice across CS, Math, EE (30+ courses)
25.02.2019B. Gianesi / G. Fourny 24
||Department of Computer Science
Course catalog: «Core Courses»
Data Analysis: Information & Learning (min. 1 Kurs)252-0535-00 Machine Learning HS 8 D-INFK227-0434-10 Mathematics of Information FS 8 D-ITET
Data Analysis: Statistics (min. 1 Kurs)401-3621-00 Fundamentals of Mathematical Statistics HS 10 D-MATH401-3632-00 Computational Statistics FS 10 D-MATH
Data Management and Processing (min. 2 Kurse)263-3010-00 Big Data HS 8 D-INFK263-4500-10 Advanced Algorithms HS 8 D-INFK261-5110-00 Optimization for Data Science FS 8 D-INFK
25.02.2019B. Gianesi / G. Fourny 25
||Department of Computer Science
Core courses
High level of competence in Data Science
Solid and sound knowledge basis.
Lectures Exercises Self-studying Projects+ + +
Exam+
25.02.2019B. Gianesi / G. Fourny 26
||Department of Computer Science
Part of vvz AS18: «Core Electives»
25.02.2019B. Gianesi / G. Fourny 27
||Department of Computer Science
Interdisciplinary Electives: example
25.02.2019B. Gianesi / G. Fourny 28
D INFK | D MATH | D ITET
Master’s Program in Data Science – Interdisciplinary Electives Finance and Insurance The course compilation Finance and Insurance introduces students to quantitative finance with a combination of economic theory and mathematical methods, supported by the knowledge on probability and statistics that Data Science students acquired from their Bachelor's degree. These courses are offered by ETH and the Finance Group at the University of Zurich. They transfer skills typically used in quantitative-oriented areas of the financial services industry, such as risk or asset management or financial product development. Data Scientists with a very strong mathematical background will be increasingly needed in this field in the future because of the high degree of complexity involved, both in terms of data analysis and in terms of domain-specific knowledge. Basic Courses
Number Title Credits Semester Language 363-1000-00L Financial Economics 3 spring EN 401-3888-00L Introduction to Mathematical Finance 10 spring EN 401-3925-00L Non-Life Insurance: Mathematics and Statistics 6 autumn EN 401-3922-00L Life Insurance Mathematics 4 autumn EN 401-3928-00L Reinsurance Analytics 4 autumn EN
Advanced Courses
Number Title Credits Semester Language UZH MFOEC107
Asset Management 3 spring EN
||Department of Computer Science
Structure Master’s program Data Science Course catalogue Design principles Eligibility Application + Documents
25.02.2019B. Gianesi / G. Fourny 29
Agenda
||Department of Computer Science
Solid and sound knowledge in analyizing and handling ofbig data
Specialized knowledge in a research area First experience in handling real data
Design Principles Master in Data Science
25.02.2019B. Gianesi / G. Fourny 30
||Department of Computer Science
Structure Master’s program Data Science Course catalogue Design principles Eligibility Application + Documents
25.02.2019B. Gianesi / G. Fourny 31
Agenda
||Department of Computer Science
Qualifying bachelor’s programs Bachelor in Electrical Engineering and Information
Technology Bachelor in Computer Science Bachelor in Mechanical Engineering Bachelor in Mathematics Bachelor in Physics
Target Group
25.02.2019B. Gianesi / G. Fourny 32
||Department of Computer Science
Structure Master’s program Data Science Course catalogue Design principles Eligibility Application + Documents
25.02.2019B. Gianesi / G. Fourny 33
Agenda
||Department of Computer Science
Specialized Master‘s program
Bologna admission period: 1 - 31 march 2019
Application & Admission, AS 2019
Even ETH bachelor’s students have to apply
25.02.2019B. Gianesi / G. Fourny 34
||Department of Computer Science
Documents Online application tool (fill in, print & sign)
ETH transcript: printed from mystudies Official transcripts of other study programs and mobility CV
GRE General Test Recommandation letters
ETH Bachelor’s students are waived Language test Application fee
Application Documents
25.02.2019B. Gianesi / G. Fourny 35
||Department of Computer Science
Website with information material
Admission without any additional requirements
Gaps in Statistics, analysis, linear algebra Programming Databases, data modelling
are expected to be filled in self-study
Admission Principles
Excellent track record
25.02.2019B. Gianesi / G. Fourny 36
||Department of Computer Science
Data Science:www.inf.ethz.ch/data-science Study guide Regulations of study Recommended reading …
Admission office:https://www.ethz.ch/en/studies/registration-application/master/application.html
25.02.2019B. Gianesi / G. Fourny 37
Information
||Department of Computer Science
Studies administration:Bernadette GianesiOffice CAB F [email protected]
Program coordination:Dr. Ghislain [email protected]
25.02.2019B. Gianesi / G. Fourny 38
Information
||Department of Computer Science
Thank you
25.02.2019B. Gianesi / G. Fourny 39
Top Related