Master's Programme in ICT Innovation (EIT Digital Master School) - Data Science

Degree:

Master of Science (Technology).
More information.

ECTS:

120 ECTS

Field of Study:

Technology and Engineering

Duration:

2 years, full-time

Eligibility:

An appropriate Bachelor´s degree or an equivalent qualification.

Language of Instruction:

English
More information.

Organising school/s:

School of Science

Application period:

2017-11-15 - 2018-02-01

Further information:

EIT Digital Master School
E-mail: masterschool@eitdigital.eu

ICT Innovation

The EIT Digital Master School offers a two year education in which you can choose two universities in two different European countries, building a curriculum of technical courses combined with innovation and entrepreneurship studies.

Data abounds: social media, manufacturing systems, medical devices, logistic services, and countless others generate petabytes of data on a daily basis. With a wealth of data available, we are at a point in history, where we can conduct analyses to detect, discover, and, ultimately, better understand the world around us.

The newly established Data Science (DSc) double degree programme offers a unique academic curriculum, whereby students can study data science, innovation, and entrepreneurship at leading European universities. In this programme, students will learn about:

  • scalable data collection techniques,
  • data analysis methods, and
  • a set of tools and technologies that address data capture, processing, storage, transfer, analysis, and visualization, as well as related concepts (e.g., data access, pricing data, and data privacy).

Study programme

The first year will be similar at all four DSc entry point universities: Universidad Politecnica de Madrid (UPM), Eindhoven University of Technology (TU/e), Universite Nice Sophia Antipolis (UNS) and Polytechnic University of Milan (Polimi) with foundation courses in

  • data handling,
  • data analysis,
  • advanced data analysis and data management,
  • visualization, and
  • applications.

An important part of the programme are also the Innovation and & Entrepreneurship (I&E) courses.

During the second year of studies, the student chooses a spesialisation option offered in one of the six exit point universities: Aalto University, UPM, TU/e, UNS, KTH, or TU Berlin. The specialisation option offered at Aalto University is Machine Learning, Big Data Management, and Business Analytics.

  • The Aalto specialization aims to provide students a versatile and diverse set of skills for managing very big data, extracting knowledge from data, learning models and making inferences, creating meaningful visualizations to interact with data, and using data-driven methods in business analytics and intelligence, as well as in other applications. These are all necessary skills to becoming a successful data scientist, one of the top professional careers world-wide. An ideal candidate to the Aalto specialization is mathematically inclined, technically proficient, has entrepreneurial spirit, and interest in solving real-life problems.

Specialisation's courses can be selected from the following list:

  • Introduction to Analytics and Data Science (2 ECTS)
  • Machine Learning: Basic Principles (5 ECTS)
  • Machine Learning and Neural Networks (5 ECTS)
  • Bayesian Data Analysis (5 ECTS)
  • Algorithmic Methods of Data Mining (5 ECTS)
  • Scalable Cloud Computing (5 ECTS)
  • Programming Parallel Computers (5 ECTS)
  • Modern Database Systems (5 ECTS)
  • Business Intelligence (6 ECTS)
  • Digital Marketing (6 ECTS)
  • Data Science for Business (6 ECTS)

Read more about the DSc studies at Aalto University from our Into student portal.

Admission to the EIT Digital Master School majors

The ICT Innovation programme has separate application periods and admission procedures than the rest of the Master's programmes offered by Aalto University. Further information on the admission can be found from the EIT Digital Master School website for prospective students (masterschool.eitdigital.eu).

Page content by: | Last updated: 09.11.2017.