Specialized Master s Program Data Science Information for ETH Students Monday, 5 March 2018, 12:15 h CAB G 51. Department of Computer Science

Transcription

1 Specialized Master s Program Data Science Information for ETH Students Monday, 5 March 2018, 12:15 h CAB G 51 B. Gianesi / G. Fourny

2 Master s Program Data Science Master Program / Application / Admission for ETH students B. Gianesi / G. Fourny

3 Agenda Structure Master s Program Data Science Course Catalog Design Principles Eligibility Application + Documents B. Gianesi / G. Fourny

4 Agenda Structure Master s Program Data Science Course Catalog Design Principles Eligibility Application + Documents B. Gianesi / G. Fourny

5 Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information 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 B. Gianesi / G. Fourny

6 120 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 1 30 CP Semester 2 30 CP Semester credits CP Semester credits CP 4 more semesters of leeway Recommended CP / Semester Hard limit at 4 years B. Gianesi / G. Fourny

7 Program Structure Master's in Data Science 120 B. Gianesi / G. Fourny

8 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Minimum required credit points B. Gianesi / G. Fourny

9 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 B. Gianesi / G. Fourny

10 Core Courses High level of competence in Data Science Solid and sound knowledge basis. Lectures + Exercises + Self-studying + Projects + Exam B. Gianesi / G. Fourny

11 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 Statistics 8 Data Management and Processing 16 Core Electives 10 B. Gianesi / G. Fourny

12 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 Statistics 8 Data Management and Processing 16 Core Electives up to you Does not sum up: freedom B. Gianesi / G. Fourny

13 Core Courses Data Analysis: Information & Learning Machine Learning (8) Mathematics of Information (8) Data Analysis: Statistics Fundamentals of Mathematical Statistics (10) Computational Statistics (10) Data Management and Processing Big Data (8) Algorithmic aspects of Data Science (8) Optimization for Data Science (8) Core Electives A lot of choice (30+ courses) B. Gianesi / G. Fourny

14 Core Courses Roughly: At last one here At least one here At least two here At least two here Data Analysis: Information & Learning Machine Learning (8) Mathematics of Information (8) Data Analysis: Statistics Fundamentals of Mathematical Statistics (10) Computational Statistics (10) Data Management and Processing Big Data (8) Algorithmic aspects of Data Science (8) Optimization for Data Science (8) Core Electives A lot of choice across CS, Math, EE (30+ courses) B. Gianesi / G. Fourny

15 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 Statistics 8 Data Management and Processing 16 Core Electives 10 Interdisciplinary Electives 8 18 up to you B. Gianesi / G. Fourny

16 Interdisciplinary Electives Bridge the gap with other disciplines cultures mindsets 8-12 credits Data Science would not exist without Data! B. Gianesi / G. Fourny

17 Interdisciplinary Electives Course compilations Computational Biology & Bioinformatics Finance & Insurance Geographic Information Systems Social Networks Transportation Systems Weather and Climate Systems B. Gianesi / G. Fourny

18 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 Statistics 8 Data Management and Processing 16 Core Electives 10 Interdisciplinary Electives 8 Data Science Lab up to you 4 up to you B. Gianesi / G. Fourny

19 Data Science Lab Apply your knowledge and skills to Real Data! Interdisciplinary projects Groups of three students + Presentation B. Gianesi / G. Fourny

20 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 Statistics 8 Data Management and Processing 16 Core Electives 10 Interdisciplinary Electives 8 Data Science Lab 14 Seminar 2 18 up to you 4 up to you B. Gianesi / G. Fourny

21 Seminar Read and understand publications Present a research paper Get involved in discussions B. Gianesi / G. Fourny

22 Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information 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 18 up to you 4 up to you B. Gianesi / G. Fourny

23 Science in Perspective Humanities and Social Sciences Language courses 851-xxxx-xx ( 3 credits including ETH BSc) B. Gianesi / G. Fourny

24 Program structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information 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 up to you 4 up to you B. Gianesi / G. Fourny

25 Agenda Structure Master s Program Data Science Course Catalog Design Principles Eligibility Application + Documents B. Gianesi / G. Fourny

26 Course Catalog: «Core Courses» Data Analysis: Information & Learning (min. 1 Kurs) Machine Learning HS 8 D-INFK Mathematics of Information FS 8 D-ITET Data Analysis: Statistics (min. 1 Kurs) Fundamentals of Mathematical Statistics HS 10 D-MATH Computational Statistics FS 10 D-MATH Data Management and Processing (min. 2 Kurse) Big Data HS 8 D-INFK New Algorithmic Aspects of Data Science HS 8 D-INFK Optimization for Data Science FS 8 D-INFK B. Gianesi / G. Fourny

27 Part of vvz SS18: «Core Electives» B. Gianesi / G. Fourny

28 Interdisciplinary Electives: Example Atmosphäre & Klima Klimasysteme 3 D-USYS Wettersysteme 3 D-USYS Atmosphäre 3 D-USYS Land-Climate Dynamics 3 D-USYS Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation 3 D-USYS Inter-annual Phenomena and their Prediction 3 D-USYS B. Gianesi / G. Fourny

29 Agenda Structure Master s Program Data Science Course Catalog Design Principles Eligibility Application + Documents B. Gianesi / G. Fourny

30 Design Principles Master in Data Science Solid and sound knowledge in analyizing and handling of big data Specialized knowledge in a research area First experience in handling real data B. Gianesi / G. Fourny

31 Agenda Structure Master s Program Data Science Course Catalog Design Principles Eligibility Application + Documents B. Gianesi / G. Fourny

32 Eligibilty 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 B. Gianesi / G. Fourny

33 Agenda Structure Master s Program Data Science Course Catalog Design Principles Eligibility Application + Documents B. Gianesi / G. Fourny

34 Application & Admission, AS 2018 Specialized Master s program Even ETH bachelor s students have to apply Bologna admission period: 1-31 march 2018 B. Gianesi / G. Fourny

35 Application Documents 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 B. Gianesi / G. Fourny

36 Admission Principles Admission without any additional requirements Gaps in Statistics, analysis, linear algebra Programming Databases, data modelling are expected to be filled in self-study Excellent track record Website with information material B. Gianesi / G. Fourny

37 Information Data Science: Study guide Regulations of study Recommended reading Admission office: B. Gianesi / G. Fourny

38 Information Studies administration: Bernadette Gianesi Office CAB F 64.1 bernadette.gianesi@inf.ethz.ch Program coordination: Dr. Ghislain Fourny ghislain.fourny@inf.ethz.ch B. Gianesi / G. Fourny