informationsveranstaltung: spezialisierter master …...data management and processing (min.2 kurse)...
TRANSCRIPT
||Departement Informatik
Montag, 25. Februar 2019, 12:15 hCAB G 51
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Informationsveranstaltung:Spezialisierter Master-StudiengangData Science
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Master-Studiengang Data Sciencewww.inf.ethz.ch/data-science
Masterprogramm / Bewerbung / Zulassung
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||Departement Informatik
Aufbau / Elemente Master-Studiengang Data Science Kurskatalog Lernziele Master in Data Science Zielpublikum Bewerbung + Unterlagen
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Agenda
||Departement Informatik
Aufbau / Elemente Master-Studiengang Data Science Kurskatalog Lernziele Master in Data Science Zielpublikum Bewerbung + Unterlagen
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Agenda
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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
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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
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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
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Programmstruktur
Master's in Data Science 120
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||Departement Informatik
Programmstruktur
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Minimum benötigteKP pro Kategorie
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||Departement Informatik
Programmstruktur
Master's in Data Science 120Core Courses and Interdisciplinary Electives 72
Core Courses 60
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||Departement Informatik
Programmstruktur
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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||Departement Informatik
Programmstruktur
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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18 u
p to
you
||Departement Informatik
Programmstruktur
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
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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 & Insurance• Geographic Information Systems• Law, Policy, and Innovation• Neural Information Processing• Social Networks• Transport Planning and Systems• Weather and Climate Systems
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||Departement Informatik
Programmstruktur
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
Data Science Lab 14
18 u
p to
you
4 up
to y
ou
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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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||Departement Informatik
Programmstruktur
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
Data Science Lab 14
18 u
p to
you
4 up
to y
ou
Seminar 2
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Seminar
Read and understand publications
Present a research paper
Get involved in discussions
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||Departement Informatik
Programmstruktur
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
Data Science Lab 14
18 u
p to
you
4 up
to y
ou
Seminar 2
Science in Perspective 2
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||Departement Informatik
Science in Perspective
Humanities and Social Sciences
Language courses 851-xxxx-xx(≤ 3 credits including ETH BSc)
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||Departement Informatik
ProgrammstrukturMaster'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
18 u
p to
you
4 up
to y
ou
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||Departement Informatik
Aufbau / Elemente Master-Studiengang Data Science Kurskatalog Lernziele Master in Data Science Zielpublikum Bewerbung + Unterlagen
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Agenda
||Departement Informatik
Long-Term Course Catalogue
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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
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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)
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||Departement Informatik
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
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||Departement Informatik
Core courses
High level of competence in Data Science
Solid and sound knowledge basis.
Lectures Exercises Self-studying Projects+ + +
Exam+
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||Departement Informatik
Auszug vvz HS18: «Core Electives»
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||Departement Informatik
Interdisciplinary Electives: Beispiel
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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
||Departement Informatik
Aufbau / Elemente Master-Studiengang Data Science Kurskatalog Lernziele Master in Data Science Zielpublikum Bewerbung + Unterlagen
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Agenda
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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
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||Departement Informatik
Aufbau / Elemente Master-Studiengang Data Science Kurskatalog Lernziele Master in Data Science Zielpublikum Bewerbung + Unterlagen
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Agenda
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Qualifizierende Studiengänge Bachelor in Elektrotechnik und Informationstechnologie Bachelor in Informatik Bachelor in Maschinenbau Bachelor in Mathematik Bachelor in Physik
Zielpublikum
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||Departement Informatik
Aufbau / Elemente Master-Studiengang Data Science Kurskatalog Lernziele Master in Data Science Zielpublikum Bewerbung + Unterlagen
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Agenda
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Spezialisierter Master-Studiengang
Bologna Bewerbungsperiode: 1. März - 31. März 2019
Bewerbung & Zulassung, HS 2019
Auch ETH Bachelor-Studierende müssen sich bewerben
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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
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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
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||Departement Informatik
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
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Informationen
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Studienadministration:Bernadette GianesiBüro CAB F [email protected]
Studienkoordination:Dr. Ghislain [email protected]
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Informationen
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Danke für Ihre Aufmerksamkeit
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