Data Analytics and Decision Science (DDS) M.Sc.

 

Private Course of Study

Basic Information

Degree:
Master of Science
Start of Studies:
Winter Semester
Standard Period of Studies:
4 semesters
ECTS Credits:
120Mehr Informationen

What does that mean?

ECTS are credit points that measure the workload of one's studies.

Language:
English

Admission Requirements

  • First university degree, required qualifications according to the examination regulations Mehr Informationen

    What does that mean?

  • Proficiency in English --- Mehr Informationen ---

    What does that mean?

    --- Zur Einschreibung in diesen Studiengang muessen Sie Kenntnisse in der Unterrichtssprache nachweisen. Details regelt die ---Exam regulations.

Admission to First Semester

Open
No NC

Language Requirements

  • See Course of Study Description

Dates and Deadlines

  • --- Application deadline for non-EU applicants: March 1 of each year; application deadline for EU applicants: July 15 of each year ---
 

Program Description

Become tomorrow’s technological expert by enrolling in our master program which combines the fields of Data Analytics and Decision Science. Learn how to develop state-of-the-art predictive models (Predictive Analytics) and use the predictions to optimize business objectives (Prescriptive Analytics using Operations Research techniques).

Our master program focusses on Machine Learning, Artificial Intelligence, Operations Research and Decision Making and is aimed at young professionals with a STEM (Science, Technology, Engineering, Mathematics) background. The program runs for 4 semesters full time, including 1 semester for the Master thesis. In this time students will gain 120 ECTS. The tuition fee of the program is 30,000 euros (+ social contribution fee at RWTH Aachen, approx. 270 euros per semester). Multiple options for scholarships are available.

The DDS offers courses in Data Science and machine learning, decision science and optimization technologies. The core modules are accompanied by a wide range of elective courses covering the latest in state-of-the-art technologies and deep-dives into specific verticals, like industry production, logistics and supply chain management or energy and climate. Your own research towards the master thesis will be done in the 4th semester.

 

Degree Content

The M. Sc. in Data Analytics and Decision Science offers a comprehensive program of core courses in machine learning, mathematical and heuristic optimization and data-driven decision making. These courses are accompanied by a wide range of elective courses offering deep-dives into specific application areas. Our courses combine demanding and cutting-edge research with practical projects and challenges. We continuously review and expand the set of electives to cover the latest trends and stay abreast of technological change.

The two-year full-time program consists of seven key building blocks, some of which can be customized to meet your individual needs and interests. You may also enroll in a German language course at no extra cost.

See course curriculum.

 

Prerequisites

A prerequisite for starting this course of studies is a Bachelor of Engineering or Science degree in a STEM field (science, technology, engineering and mathematics).

Applicants need at least

  • 125 credit points in mathematics and/or natural sciences (e.g. physics, chemistry, computer science or similar)
  • 15 credit points in the fields of higher mathematics or statistics, database and information systems, programming, algorithms and data structures, complexity theory, quantitative methods/operations research

In addition, applicants need to have at least 12 months of professional work experience by the time of enrolment.

The programme is entirely taught in English. A recognized certificate of proficiency in English is required.

 

Career Prospects

The M. Sc. in Data Analytics and Decision Science has been carefully designed to equip ambitious professionals with a STEM background with a distinct set of skills needed to succeed in a digitized and globalized economy.

Data-driven decisions become mission-critical in one vertical after the next. Many professions will face disruptive change, job descriptions will change significantly as data-driven decisions are at the core of creating value for businesses and new jobs will emerge. Tasks currently performed manually or supported by simple approaches will require specialized knowledge in Data Analytics and Decision Science, machine learning and optimization techniques in the future.

The M.Sc. degree, granted by RWTH Aachen University in Germany, will also enable you to pursue an academic career and continue studying towards a PhD in fields such as Data Science, Machine Learning & Artificial Intelligence, Operations Research and Engineering.

Whichever way you want to follow after graduation: Our dedicated team of experts in the career and entrepreneurship centers will accompany you on that journey and help you decide how to best realize your ambitions, be it an exciting new job or starting your own business. We seek to place our graduates in global technology blue chips, hidden technology champions, leading technology consultancies and fast-growing technology ventures. The Entrepreneurship Center at RWTH Aachen University also has a long track record of supporting innovative startups by our graduates – between 70 to 100 every year.

The following list gives an idea of the possible data scientist jobs for the successful student of the Master Program, combining Data Science and Operations Research:

  • Retail and Value Chain Management
  • Industry and Production
  • Transportation and Mobility
  • Energy and Climate
 

Examination Regulations

Examination regulations regulate academic goals and prerequisites, the course of study layout, and exam procedures.

 

Faculty

The Master course of studies is offered by the RWTH School of Business and Economics.

 

For Your Information

RWTH Aachen offers some of its courses of study in collaboration with other (private) institutions. They are not part of the courses of study under public law, and thus are not governed by legal guidelines, e.g. university placement allocation.  Some have tuition fees and require a period of professional experience.

Information about prerequisites, application processes, the university placement allocation process, and academic content is available from the host institution, to which you are applying.