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

Monday, 5 March 2018, 12:15 h CAB G 51

• B. Gianesi / G. Fourny

Specialized Master’s Program Data Science Information for ETH Students

Master’s Program Data Science

Master’ Program / Application / Admission for ETH students

• B. Gianesi / G. Fourny

• Structure Master’s Program Data Science
• Course Catalog
• Design Principles
• Eligibility
• Application + Documents

• B. Gianesi / G. Fourny

Agenda

• Structure Master’s Program Data Science
• Course Catalog
• Design Principles
• Eligibility
• Application + Documents

• B. Gianesi / G. Fourny

Agenda

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

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120 Credit Points

The master’s program is designed to be completed in 4 semesters. The

• verall 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

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Semester 1 30 CP Semester 2 30 CP Semester 3 30 CP Semester 4 30 CP 4 more semesters of leeway Recommended CP / Semester Hard limit at 4 years

Program Structure

Master's in Data Science 120

• B. Gianesi / G. Fourny

Program Structure

Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Minimum required credit points

• B. Gianesi / G. Fourny

Program Structure

Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60

• B. Gianesi / G. Fourny

Core Courses

High level of competence in Data Science Solid and sound knowledge basis. Lectures Exercises Self-studying Projects + + + Exam +

• B. Gianesi / G. Fourny

Program Structure

Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data Analysis 16 Data Management and Processing 16 Core Electives 10 Information and Learning 8 Statistics 8

• B. Gianesi / G. Fourny

Program Structure

Master's in Data Science 120 Core Courses and Interdisciplinary Electives 72 Core Courses 60 Data 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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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)

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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 & 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

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

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Interdisciplinary Electives

Bridge the gap with other disciplines cultures mindsets Data Science would not exist without

Data!

8-12 credits

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Interdisciplinary Electives

Course compilations Computational Biology & Bioinformatics Finance & Insurance Geographic Information Systems Social Networks Transportation Systems Weather and Climate Systems

• B. Gianesi / G. Fourny

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

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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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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

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Seminar

Read and understand publications Present a research paper Get involved in discussions

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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

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Science in Perspective

Humanities and Social Sciences Language courses 851-xxxx-xx (≤ 3 credits including ETH BSc)

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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 30

• B. Gianesi / G. Fourny

• Structure Master’s Program Data Science
• Course Catalog
• Design Principles
• Eligibility
• Application + Documents

• B. Gianesi / G. Fourny

Agenda

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 Algorithmic Aspects of Data Science HS 8 D-INFK 261-5110-00 Optimization for Data Science FS 8 D-INFK

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Part of vvz SS18: «Core Electives»

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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 Probabilistic Forecasts to Economics of Climate Adaptation 3 D-USYS 701-1226-00 Inter-annual Phenomena and their Prediction 3 D-USYS

• B. Gianesi / G. Fourny

• Structure Master’s Program Data Science
• Course Catalog
• Design Principles
• Eligibility
• Application + Documents

• B. Gianesi / G. Fourny

Agenda

• Solid and sound knowledge in analyizing and handling of

big data

• Specialized knowledge in a research area
• First experience in handling real data

Design Principles Master in Data Science

• B. Gianesi / G. Fourny

• Structure Master’s Program Data Science
• Course Catalog
• Design Principles
• Eligibility
• Application + Documents

• B. Gianesi / G. Fourny

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

Eligibilty

• B. Gianesi / G. Fourny

• Structure Master’s Program Data Science
• Course Catalog
• Design Principles
• Eligibility
• Application + Documents

• B. Gianesi / G. Fourny

Agenda

Specialized Master‘s program Bologna admission period: 1 - 31 march 2018

Application & Admission, AS 2018

Even ETH bachelor’s students have to apply

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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

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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

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Data Science: https://www.inf.ethz.ch/de/studium/master/master-ds.html

• Study guide
• Regulations of study
• Recommended reading
• …

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

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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

Information