Specialized Masters Program Data Science Information for ETH - PowerPoint PPT Presentation

Specialized Master’s Program Data Science Information for ETH Students Monday, 5 March 2018, 12:15 h CAB G 51 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 1

Master’s Program Data Science Master’ Program / Application / Admission for ETH students | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 2

Agenda  Structure Master’s Program Data Science  Course Catalog  Design Principles  Eligibility  Application + Documents | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 3

Agenda  Structure Master’s Program Data Science  Course Catalog  Design Principles  Eligibility  Application + Documents | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 4

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 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 5

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 Semester 2 Semester 3 Semester 3 Semester 4 Semester 4 4 more semesters of 30 CP 30 CP 30 credits 30 CP 30 credits 30 CP leeway Recommended CP / Semester Hard limit at 4 years | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 6

Program Structure Master's in Data Science 120 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 7

Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Minimum required credit points | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 8

Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 9

Core Courses High level of competence in Data Science Solid and sound knowledge basis. + + + Lectures Exercises Self-studying Projects Exam + | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 10

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 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 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 18 up to you Statistics 8 Data Management and Processing 16 Core Electives 10 Does not sum up: freedom | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 12

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) | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 13

Core Courses Data Analysis: Information & Learning Roughly: Machine Learning (8) Mathematics of Information (8) At last one here Data Analysis: Statistics Fundamentals of Mathematical Statistics (10) Computational Statistics (10) At least one here Data Management and Processing Big Data (8) Algorithmic aspects of Data Science (8) At least two here Optimization for Data Science (8) Core Electives At least two here A lot of choice across CS, Math, EE (30+ courses) | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 14

Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 18 up to you Statistics 8 Data Management and Processing 16 Core Electives 10 Interdisciplinary Electives 8 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 15

Interdisciplinary Electives Bridge the gap with other disciplines cultures mindsets 8-12 credits Data Science would not exist without Data! | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 16

Interdisciplinary Electives Course compilations Computational Biology & Bioinformatics Finance & Insurance Geographic Information Systems Social Networks Transportation Systems Weather and Climate Systems | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 17

Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 18 up to you 4 up to you Statistics 8 Data Management and Processing 16 Core Electives 10 Interdisciplinary Electives 8 Data Science Lab 14 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 18

Data Science Lab Apply your knowledge and skills to Real Data! Interdisciplinary projects Groups of three students + Presentation | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 19

Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 18 up to you 4 up to you Statistics 8 Data Management and Processing 16 Core Electives 10 Interdisciplinary Electives 8 Data Science Lab 14 Seminar 2 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 20

Seminar Read and understand publications Present a research paper Get involved in discussions | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 21

Program Structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 18 up to you 4 up to you Statistics 8 Data Management and Processing 16 Core Electives 10 Interdisciplinary Electives 8 Data Science Lab 14 Seminar 2 Science in Perspective 2 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 22

Science in Perspective Humanities and Social Sciences Language courses 851-xxxx-xx (≤ 3 credits including ETH BSc) | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 23

Program structure Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Information and Learning 8 18 up to you 4 up to you 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 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 24

Agenda  Structure Master’s Program Data Science  Course Catalog  Design Principles  Eligibility  Application + Documents | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 25

Course Catalog: «Core Courses» Data Analysis: Information & Learning (min. 1 Kurs) 252-0535-00 Machine Learning HS 8 D-INFK 227-0434-10 Mathematics of Information FS 8 D-ITET Data Analysis: Statistics (min. 1 Kurs) 401-3621-00 Fundamentals of Mathematical Statistics HS 10 D-MATH 401-3632-00 Computational Statistics FS 10 D-MATH Data Management and Processing (min. 2 Kurse) 263-3010-00 Big Data HS 8 D-INFK New 8 D-INFK Algorithmic Aspects of Data Science HS 261-5110-00 Optimization for Data Science FS 8 D-INFK | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 26

Part of vvz SS18: «Core Electives» | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 27

Interdisciplinary Electives: Example Atmosphäre & Klima 701-0412-00 Klimasysteme 3 D-USYS 701-0473-00 Wettersysteme 3 D-USYS 701-0023-00 Atmosphäre 3 D-USYS 701-1251-00 Land-Climate Dynamics 3 D-USYS 701-1252-00 Climate Change Uncertainty and Risk: From 3 D-USYS Probabilistic Forecasts to Economics of Climate Adaptation 701-1226-00 Inter-annual Phenomena and their Prediction 3 D-USYS | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 28

Agenda  Structure Master’s Program Data Science  Course Catalog  Design Principles  Eligibility  Application + Documents | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 29

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 | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 30

Agenda  Structure Master’s Program Data Science  Course Catalog  Design Principles  Eligibility  Application + Documents | | Department of Computer Science B. Gianesi / G. Fourny 05.03.2018 31