Informationsveranstaltung: Spezialisierter Master …...Data Management and Processing (min.2 Kurse) 263-3010-00 Big Data HS 8 D-INFK 263-4500-10 Advanced Algorithms HS 8 D-INFK 261-5110-00

||Departement Informatik

Montag, 25. Februar 2019, 12:15 hCAB G 51

25.02.2019B. Gianesi / G. Fourny 1

Informationsveranstaltung:Spezialisierter Master-StudiengangData Science


Master-Studiengang Data Sciencewww.inf.ethz.ch/data-science

Masterprogramm / Bewerbung / Zulassung



Aufbau / Elemente Master-Studiengang Data Science Kurskatalog Lernziele Master in Data Science Zielpublikum Bewerbung + Unterlagen


Agenda




Agenda


StrukturMaster's 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 Kreditpunkte

Die reguläre Studienzeit beträgt 4 Semester – maximal 8 SemesterDas letzte Semester widmet sich ausschliesslich der Masterarbeit .

Semester 3

30 credits

Semester 4

30 credits


Semester 1

30 KP

Semester 2

30 KP

Semester 3

30 KP

Semester 4

30 KP

max. 4 zus. Semester, inkl. Masterarbeit

Empfohlene KP / Semestermax. Studiendauer:4 Jahre


Programmstruktur

Master's in Data Science 120



Programmstruktur

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

Minimum benötigteKP pro Kategorie



Programmstruktur


Core Courses 60



Programmstruktur


Core Courses 60Data Analysis 16


Core Electives 10

Information and Learning 8

Statistics 8



Programmstruktur




Core Electives 10


Statistics 8

Does not sum up:

freedom


18 u

p to

you


Programmstruktur




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



Programmstruktur




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



Programmstruktur




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



Programmstruktur




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)



ProgrammstrukturMaster's 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 & LearningAdvanced Machine Learning (8)Mathematics of Information (8)

Data Analysis: StatisticsFundamentals of Mathematical Statistics (10)Computational Statistics (8)

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)



Kurskatalog: «Core Courses»

Data Analysis: Information & Learning (min. 1 Kurs)252-0535-00 Advanced 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 8 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+



Auszug vvz HS18: «Core Electives»



Interdisciplinary Electives: Beispiel


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


Fundierte Kenntnisse in der Analyse und der Handhabung grosser Datenmengen

Fachliche Kenntnisse in einem Anwendungsgebiet Erste Erfahrungen im Umgang mit realen Daten

Lernziele Master in Data Science





Agenda


Qualifizierende Studiengänge Bachelor in Elektrotechnik und Informationstechnologie Bachelor in Informatik Bachelor in Maschinenbau Bachelor in Mathematik Bachelor in Physik

Zielpublikum





Agenda


Spezialisierter Master-Studiengang

Bologna Bewerbungsperiode: 1. März - 31. März 2019

Bewerbung & Zulassung, HS 2019

Auch ETH Bachelor-Studierende müssen sich bewerben



Einzureichende Unterlagen Online-Bewerbungsformular (ausfüllen, drucken & unterzeichnen)

ETH-Transkript: Ausdruck aus mystudies Offizielle Transkripts von früheren Studien & Mobilität CV

GRE General Test Referenzschreiben

ETH Bachelor-Studierenden sind entbunden von Sprachnachweis Bewerbungsgebühr

Bewerbungsunterlagen



Webseite mit Material zur Vorbereitung

Selektive Zulassung, in der Regel ohne Auflagen

Lücken im Vorwissen Statistik, Analysis, Lineare Algebra Programmierung Datenbanken, Datenmodellierung

Wir erwarten, dass Studierende diese Lücken selbständig schliessen

Zulassungsgrundsätze

Gute Studienleistungen im Bachelor-Studium



Data Science:www.inf.ethz.ch/data-science Studienführer (nur auf Englisch) Studienreglement Empfohlene Leseliste zur Vorbereitung (nur über englische Website) u.a.m.

Zulassungsstelle:https://www.ethz.ch/de/studium/anmeldung-bewerbung/master/bewerbung.html


Informationen

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

https://www.ethz.ch/de/studium/anmeldung-bewerbung/master/bewerbung.html


Studienadministration:Bernadette GianesiBüro CAB F [email protected]

Studienkoordination:Dr. Ghislain [email protected]


Informationen

mailto:[email protected]

mailto:[email protected]


Danke für Ihre Aufmerksamkeit


Documents

Informationsveranstaltung: Spezialisierter Master …...Data Management and Processing (min.2 Kurse) 263-3010-00 Big Data HS 8 D-INFK 263-4500-10 Advanced Algorithms HS 8 D-INFK 261-5110-00