Graduate School and Research Center in Digital Sciences

Advanced Data Science Topics

[ADST]
T Technical Teaching


Abstract

In this course, we will discuss contemporary and state of the art research problems in Data Science. The content of the course will change from year to year and will reflect the current research interests of the EURECOM faculty. The course is organised partly in Seminars/Case Studies sessions supported by industrials and researchers working in the field and a Mini Scientific Conference where each student will research and present a topic from the wide range of advanced data science topics.

Teaching and Learning Methods : academic and industrial seminars, case studies in small group, written and oral presentation.

Course Policies : Attendance to all sessions is mandatory.

Bibliography

Giving a Talk: Guidelines for the Preparation and Presentation of Technical Seminars by Frank R. Kschischang, Department of Electrical and Computer Engineering University of Toronto, http://www.comm.toronto.edu/frank/guide/guide.pdf

Requirements

Basic knowledge in Data Science

Description

The course is intended to expose students with recent developments in the field of Data Science. This is achieved through seminars and case studies organised in collaboration with companies and researchers working in the field. Whenever possible visits of local companies or research centers are organised. In addition to the seminars, students are involved in the organisation of a mini scientific conference. Typically students are asked to research, read and understand recent articles in the research literature and give presentations based on their finding on specific topics. This allows participants of the course to become aware and knowledgeable about the very wide range of critical Data Science related themes at hand today.

Current topics include but are not limited to the following:

- Large Scale Multimedia Databases

- Machine and Deep learning Algorithms/Applications

- Business Intelligence

- Smart / Connected City

- Big Data Analytics

- Data Mining: From Text to Multimedia and Machine Data

- Content Modeling and Personalization

- Sentiment Analysis

- Crowdsourcing and Social Media

Learning outcomes:

-      be able to understand the key actual problems data science is addressing both within industry and within academia

-      be able to prepare and give a presentation about work on a specific data science topic.

Nb hours: 21.00

Grading Policy:: Oral presentations and reports, and possibly written exam. 

Nb hours: 21.00
Nb hours per week: 3.00