Future Data Analytics and Decision Science experts will need to bring together expertise from a wide range of fields, like machine learning, deep learning, and artificial intelligence or mathematical optimization, heuristic algorithms and simulation techniques

Hence, the M.Sc. DDS offers a comprehensive program of core courses in the mentioned fields. 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 acquire 120 ECTS points. You may also enroll in a German language course at no extra cost.

DDS Essentials

10 credit points to be completed in semester 1

The “DDS Essentials” consist of two courses that cover and refresh relevant essentials in mathematics, statistics, algorithms and data structures. These modules lay the foundation to participate successfully in the core courses in Data Analytics and Decision Science, as well as in some of the application areas and specializations.

Data Analytics

10 credit points to be completed in semesters 1 and 2

The block “Data Analytics” consists of three modules. The module “Predictive Modeling” covers the fundamentals in data handling and data quality issues, predictive modeling and validation of business- and use- cases, as well as evaluation of predictions. The course “Machine Learning” will focus on the fundamentals and cutting edge developments in machine and deep learning.

Decision Science

15 credit points to be completed in semesters 1 and 2

This block consists of three modules: The module “Optimization Models” introduces the concepts behind building state-of-the art decision models that are able to capture the combinatorial explosion of options, including networks as well as linear and integer programs. The module “Design and Analysis of Algorithms” focuses on modern methods to solve these optimization models. The course covers algorithms not only for deterministic problems but also techniques from robust optimization and algorithmic game theory, which allow you to handle uncertainty in the decision making process. The module “Heuristic Optimization” covers the fundamentals of metaheuristics and the challenges encountered when designing high-performance heuristics for complex planning tasks in different domains.

Analytics Project

10 credit points to be completed in semester 2

The Analytics project is a practical exercise that complements the lectures from the Decision Science and Data Analytics blocks. Teams of 3 – 6 students work together on a practically motivated analytics project and go through almost the entire analytics process using machine learning and optimization techniques: formalization of the problem, modeling, understanding, gathering, and cleaning of the data, algorithm selection and development, implementation, computational solution, visualization and interpretation, and documentation of results. You learn how to present your results to both a practice-oriented and a scientific audience.

Management Electives

10 credit points to be completed in semester 2

You choose two of the following elective courses to deepen your managerial competences:

  • Management and Technology Perspectives
  • Strategic Negotiations
  • Start-Up and Growth Management
  • Service and Technology Marketing

Data Analytics & Technology Electives

5 credit points to be completed in semester 2

You choose one of three possible elective courses:

  • Advanced Machine Learning
  • Principles of Data Mining
  • Intelligent Monitoring of Engineering Systems


15 credit points to be completed in semester 3

You apply your skills in an industry work placement at global enterprises such as Deutsche Post DHL, PTV Group and others.

Application Areas

15 credit points to be completed in semester 3

You gain detailed insights in the following domains:

  • Digital Operations and Supply Chain Management
  • Optimization of Logistics Systems
  • Economic Modeling of Energy and Climate Systems

Master Thesis

30 credit points to be completed in semester 4

By writing a master thesis at the end of the program students demonstrate their ability to solve a methodological or analytic challenge using the knowledge acquired during the program and the methods of scientific research.

German Language Course

One course to be completed in semester 1 or 2

Learning another language is an important part of a successful career. Becoming proficient in German is also an essential precondition for succeeding on the German job market, both for an internship during your studies or a permanent position thereafter. As an extra offer within the DDS program, we give you the option to complete an German language course (either in semester 1 or 2) to broaden your cultural understanding and enhance your career options. The costs for the course are covered by your tuition fees.

Learning Experience

Students enrolled in this program will learn fundamental and leading edge advances in machine learning, artificial intelligence, operations research and decision science. The courses are taught by world-leading experts in their respective fields. Elective modules allow the student to specialize in a variety of fields such as production or logistics to gain a deep understanding of specific industries.

Our Teaching Approach

  • Student-centered learning: All courses are based on a participant-centered approach that emphasizes interaction between instructors and participants through case discussions, group presentations, debates, or lab sessions.
  • Interdisciplinary mindset: Most courses focus on the intersection between technology and decision making, helping you tackle real-world challenges from multiple perspectives and providing you with a truly interdisciplinary mindset.
  • Hands-on experience: In most courses, you have the opportunity to work on real-world challenges, enabling you to apply the knowledge gleaned in class and gain hands-on experience.
  • Intercultural teams: As working in multicultural teams is a given in today’s world, most courses feature group projects where you can solve managerial and technological challenges in culturally diverse teams.

Our Partners

The M.Sc. DDS is delivered by the RWTH Business School and University as well as leading partners in academia and industry around the world. Choose between an exchange with one of the leading universities or gain hands-on work experience at one of our cutting-edge industrial partners.

Development Opportunities

Data is becoming the new “oil”, the raw material from which value is created as businesses become predictive enterprises and digitize their value chain. Each industry sector and application area will benefit from deriving optimal business decisions using data-driven techniques. Operational decisions are supported or even automated using state-of-the-art machine learning models combined with optimization techniques. 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- and algorithm-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 following list gives an idea of the possible data scientist jobs for the successful student of the Master Program, combining data analytics and operations research.

Retail & Value Chain Management

  • Network Planning: Planning transportation in an effective manner, deciding about locations, company-wide and within logistic nodes, can save a company millions of dollars. These decisions are inherently complex, with interdependent decisions under involved constraints. Leveraging the full potential requires modelling and solving the decision situations using the latest mathematical optimization techniques.
  • Replenishment: How many items of a specific article are needed at a particular store or production location? What may sound like a simple question is a difficult challenge in practice: Retailers and industrial production processes depend on thousands of products at multiple locations which are sourced from a complex supply chain network. In practice, a large number of additional constraints such as varying lot-sizes, delivery or production lead-times, etc. complicate the replenishment process further.  Optimal replenishment and procurement decisions avoid stockout situations while lowering capital cost or waste of perishable items and thus increasing the overall profit.
  • Dynamic Pricing: One of the core competences of any retailer is to set the best price of any product at any time. However, as many retailers stock tens of thousands of products at hundreds of sales locations, finding the right price for each of them every day is a Herculean task, in particular, as customer behavior shifts and product sales are influenced by a myriad of factors. Building robust systems setting the right price automatically according to a specific business strategy is paramount to the success of the company

Industry & Production

The next industrial revolution termed “Industry 4.0” has just begun: Apart from highly visible applications such as self-driving cars, many industrial processes can benefit significantly from data-driven decisions. To exploit the full potential it is not sufficient (or even counter productive) to massively collect data, but to collect exactly the right data in order to feed them into predictive models and base decisions on them using prescriptive modeling.

  • Determining production sequences on machines and respective lot sizes are everyday tasks, however, these decisions are strongly combinatorial in nature. Unlike algorithmic approaches, humans are generally not able to take the multitude of correlated influencing factors into account in order to make optimal decisions.
  • Although today’s production machines and industrial robots are very sophisticated, operating them often requires decades of specialized training to gain the relevant experience to operate these machines optimally. Using data-based methods, assistance systems and self-regulating machines capture a significant part of the expertise and aim to make the production process more efficient.
  • By combining measurements of the item currently being produced, other data sources and external information such as the order book, production lines can be optimized dynamically to improve the pipeline: Which item can be produced now with the materials at hand? Does the quality of the individual product match the requirements from the order, are further production steps required or should the production line be changed dynamically to fulfil a different order first?
  • All industrial production units have to undergo regular maintenance. Planning the best maintenance period is critical for most production lines to optimize the overall yield. Predictive maintenance allows to detect otherwise unnoticed signs of a forthcoming machine breakdown or tool wear. Based on this information, and also depending on the production schedule, the availability of spare parts, maintenance personnel, open orders etc. one builds prescriptive models to optimally schedule maintenance operations (prescriptive maintenance).

Transportation & Mobility

  • Airlines were among the first to adopt operations research technology in their strategic, tactical and operations planning. Typical airline planning tasks arise in network planning, aircraft routing, maintenance scheduling, crew assignment, disruption management etc. Only mathematical optimization techniques can capture the combinatorial variety of these tasks which appear in similar form when operating trains or other kinds of public transport.
  • Autonomous driving, in particular based on electric cars, requires to re-consider known planning problems in a new light. Locating charging or changing stations for cars,  coordinating or balancing the re-charging of cars in order to avoid peaks in electricity demand, etc. are only a few examples. Predictive and prescriptive analytics work hand in hand in these fields.
  • Public transportation providers face planning tasks such as network layout, line planning, creation of time-tables, vehicle and staff planning as well as managing operational disruptions in real-time. Traditional engineering approaches typically use a hierachical approach to deal with these tasks, modern optimization techniques however enable companies to solve these challenges in an integrated, more holistic approach.
  • Traffic management features such as advanced parking solutions, virtual street signs, sharing business models, etc. will be connected via integrated mobility platforms to keep cities free of traffic jams and unnecessary emissions.

Energy & Climate

  • Pricing emissions: The causes and economic consequences of climate change are becoming increasingly important for investment decisions. In order to estimate the long run effects of emissions of greenhouse gases on the economy, complex climate-economy models need to be formulated, calibrated and solved. This task requires the development and application of modern quantitative methods for solving complex, high-dimensional, non-linear dynamic optimization problems with explicit treatment of uncertainty.
  • Policy consulting: Decision-making regarding sustainable growth requires policy makers to take the intrinsically uncertain nature of future states of the economy and the earth system components into account. Facing this important problem, decision makers have to choose an appropriate portfolio of actions and policies to increase the welfare return to society. Thus, decision makers face the risk management problem of designing an energy and climate policy that is robust to a broad range of uncertain future conditions.

Further Application Areas

  • Marketing (e.g. ad placement, SEO optimization, marketing campaign optimization, …)
  • Health care (e.g. personalized medicine, automated diagnostics, …)
  • Finance & Insurance (e.g. predictive underwriting, fraud detection, tariff optimisation, …)
  • Politics & public sector
  • Agriculture
  • Sustainability & Smart Living
  • Sports
  • Education

In fact, it is hard to come up with an industry or service that is not directly or indirectly impacted by data and algorithms driven decision making. As the need for decisions changes (more flexible, real-time, under uncertain and ever changing environments), a  decision making process rooted in mathematical optimization is unavoidable. Only predictive analytics (e.g. machine learning) can harness the potential of historic and present data; only prescriptive analytics (mathematical optimization and operations research) can capture the full range of options the decision maker faces.

Further Career Prospects

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 analytics, 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.

Your Aspirations

This program is ideal for you if you want to develop as a professional and transform your career, provided:

  • You have a STEM (Science, Technology, Engineering and Mathematics) background, at least one year of full-time work experience and want to deepen your knowledge in machine learning, artificial intelligence, operations research techniques (mathematical optimization, heuristic algorithms and simulation), and data-driven decision making.
  • You are passionate about engineering and technology and want to enhance your skills to face tomorrow’s challenges in creating value from data using algorithms.
  • You want to learn how to leverage machine learning, artificial intelligence, operations research, and data-driven decision making into profitable and sustainable business models that allow you to lead the technological transformation in your industry rather than just following it.
  • You have a good knowledge and working experience with at least one high level programming language (e.g. Python, Java, C/C++), as well as some experience in developing software or contributing to a software project.


The high relevance of the M.Sc. DDS attracts talented participants from around the world with a wide range of professional backgrounds. Enrolling in the M.Sc. DDS will not only allow you to gain a distinct set of skills needed to succeed in today’s economy. It will also enable you to gain a truly global perspective on Data Analytics and Decision Science and to build a vibrant network that will last for a lifetime.

Students Profile

  • 100% of our students are STEM graduates.
  • They have Bachelor’s degrees, amongst others, in Computer Science, Electronics & Communication Engineering, Mathematics & Statistics, Telecommunications & Electronics, Mechanical Engineering, Industrial Engineering, and Automobile Engineering.
  • 6 nationalities are represented.
  • Their age ranges between 24 and 32 with a mean of 27 years.
  • They have an average full-time work experience of 3.5 years.
  • 20 percent of the students are female.

Previous Employers Of Our Graduates

include leading international technology firms such as Accenture, Caligo, Capgemini, Thyssenkrupp, Caterpillar, Robert Bosch, SandvineInc., Nuclear Power Corporation, HCL Technologies, TEK Systems, Infosys, Delphi Automobile and Scandinavian Health Ltd..

Portrait Shruthi Manivannan
Student talk | Shruthi Manivannan

I am very happy with how interactive and easily approachable professors and student coordinators are. In case of any doubt with regards to subjects or organizational issues, we are able to easily reach out to the faculty.

Portrait Roney Mathew
Student talk | Roney Mathew

The course is very industry relevant and the topics are very much trending job skills. I also like the practical application oriented course structure and how it is a right blend of management and technical courses.


To apply for the M.Sc. Data Analytics & Decision Science, you need to prove different academic and English language requirements.

Academic Requirements

Technology Focus
RWTH Business School is looking for applicants who have a background in a technological area and at least a Bachelor’s of Engineering or Science degree in a STEM field (science, technology, engineering and mathematics). Applicants need a minimum of 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, as well as at least 125 credit points mathematics and/or natural sciences (e.g. physics, chemistry, computer science or similar).

Further details are defined in the examination regulations.

Professional Work Experience
Courses within the DDS program also address a broad set of technology and management challenges from current practice. Applicants thus need to have at least 12 months of professional work experience.

English Language Requirements

For all of our master’s programs certified proof of your competence in the English language through the completion of one of the accepted examinations listed below is needed:

  • TOEFL Internet-based minimum of 90 pts.,
  • IELTS (Academic) test minimum overall band 5,5 pts.,
  • Cambridge Test – Certificate in Advanced English (CAE),
  • First Certificate in English (FCE), completed with a B,
  • Placement-Test of RWTH Aachen University’s language center (B2).
  • For German applicants: A certificate attesting English language skills at level B2 of the Common European Framework of Reference for Languages (CEFR). This proof is provided, for example, by the submission of a German Abitur certificate, which shows that English has been continuously taught until the end of qualification phase 1 (grade 11 for G8 A-levels, otherwise grade 12) and has been completed with at least sufficient marks.

In general:

  • The score report needs to be presented during enrollment.
  • The score report must not be older than two years by August 1 of the year your degree course will start.
  • Please do not send the original TOEFL or IELTS score sheets to the university (also not via ETS).

Exemptions for the language requirements are only applicable for nationals or first-degree holders from the USA, the UK, Canada, Ireland, New Zealand, and Australia.

Required Documents

  • CV
  • Motivation letter
  • At least one letter of recommendation (merged as one document)
  • Transcript of records
  • Proof of professional experience of at least 12 months
  • Other proof of performance/assessment, e.g. English certificate, degree certificate, further qualifications (not mandatory for admission)
  • Translation of submitted documents (if your documents are not in English or German)


Application for the DDS is possible from October 1 until August 31 for applicants with a Degree from a University from a EU- or EEA-Country. For non-EU-Degree applicants, our application portal is open from October 1 until and including March 1. There is no application fee.


RWTH Business School offers various scholarships to support particularly suitable candidates. Information on our scholarships, the application process, and external scholarship opportunities can be found at the link below.


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