specialized master’s program data science · core courses and interdisciplinary electives 72....

||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


Master’s program Data Sciencewww.inf.ethz.ch/data-science

Master’s program / Application / Admission



Structure Master’s program Data Science Course catalogue Design principles Eligibility Application + Documents


Agenda




Agenda


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



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


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


Program structure

Master in Data Science 120



Program structure

Master in Data Science 120Core Courses and Interdisciplinary Electives 72

Minimum required credit points



Program structure


Core Courses 60



Program structure


Core Courses 60Data Analysis 16


Core Electives 10

Information and Learning 8

Statistics 8



Program structure




Core Electives 10


Statistics 8

Does not sum up:

freedom


18 u

p to

you


Program structure

Master's in Data Science 120Core Courses and Interdisciplinary Electives 72



Statistics 8


Core Electives 10


18 u

p to

you



Interdisciplinary Electives

Bridge the gap with other disciplinesculturesmindsets

Data Science would not exist without

Data!8-12 credits



Interdisciplinary Electives


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


Program structure




Statistics 8


Core Electives 10


Data Science Lab 14

18 u

p to

you

4 up

to y

ou



Data Science Lab

Groups of three students + Presentation

Apply your knowledge and skills to

Real Data!Interdisciplinary projects



Program structure




Statistics 8


Core Electives 10


Data Science Lab 14

18 u

p to

you

4 up

to y

ou

Seminar 2



Seminar

Read and understand publications

Present a research paper

Get involved in discussions



Program structure




Statistics 8


Core Electives 10


Data Science Lab 14

18 u

p to

you

4 up

to y

ou

Seminar 2




Science in Perspective

Humanities and Social Sciences

Language courses 851-xxxx-xx(≤ 3 credits including ETH BSc)



Program structureMaster in Data Science 120

Core Courses and Interdisciplinary Electives 72Core Courses 60

Data Analysis 16Information and Learning 8

Statistics 8


Core Electives 10


Data Science Lab 14

Seminar 2


Master's Thesis 30

18 u

p to

you

4 up

to y

ou





Agenda


Long-Term Course Catalogue


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


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)



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



Core courses

High level of competence in Data Science

Solid and sound knowledge basis.

Lectures Exercises Self-studying Projects+ + +

Exam+



Part of vvz AS18: «Core Electives»



Interdisciplinary Electives: example


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




Agenda


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





Agenda


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





Agenda


Specialized Master‘s program

Bologna admission period: 1 - 31 march 2019

Application & Admission, AS 2019

Even ETH bachelor’s students have to apply



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



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



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


Information

http://www.inf.ethz.ch/data-science

https://www.ethz.ch/en/studies/registration-application/master/application.html


Studies administration:Bernadette GianesiOffice CAB F [email protected]

Program coordination:Dr. Ghislain [email protected]


Information

mailto:[email protected]


Thank you
