Data Science for Society and Business | Constructor University

Data Science for Society and Business (MSc) - The program

The 2-year master program Data Science for Society and Business (DSSB) is a highly selective program for students of business and social sciences aiming to become data science experts. The program is also of high interest to students with backgrounds in health and environmental sciences aiming to expand into big data analytics, data-driven and computational social science, based on their strong quantitative background.

During two exciting years, graduates gain cutting edge competences, tools and methods to discuss, model, predict and solve the pressing challenges of digital societies, modern organizations and competitive businesses.

The truly multidisciplinary program combines a broad course offering from multiple social and data science disciplines with 3 elective tracks to allow for individual diversity and further specialization. With a focus on high employability, DSSB graduates are best prepared for quickly growing, global professional and academic career options resulting from digitization.

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5 reasons

5 reasons why you should study Data Science for Society and Business (DSSB) at Constructor University

Unique interdisciplinary modular program with elective tracks for individual diversity and specialization that promotes personalized career goals
A cutting-edge demanding academic program with access to innovative research, a close relation between teachers and students, shared learning experience and innovative learning environments
Maximum focus on employability and preparation for the demands of global career paths in all areas of data-driven professional and academic fields
A graduation rate of 90% reflects the university’s high academic standards and the intense support level with in small classes with favorable student-to-teacher ratios
Benefit from Constructor University’s direct access to the leading digital and tech companies, consulting firms, NGOs and top research institutes as well as a large global Alumni network

Rankings

#1

International university in Europe (THE 2023)

#1

private university in Germany (THE 2023)

93%

Job success within a year (Alumni survey)

100+

Student clubs, 1 campus

Program

The master program Data Science for Society and Business study program teaches international students with strong backgrounds in social sciences (e.g. business, economics, demography, media studies, political science, psychology, sociology,) how to make use of rapidly growing digital data resources and new computational tools and methods to potentially solve the challenging interdisciplinary problems in their professional or academic field.

The program may also be interesting for students with a background in humanities, natural or technical science who want to focus on innovative social data analytics and pressing social and business questions emerging from digitization.

Students of the DSSB program thereby benefit from a broad course offering in various social, data and business sciences and the close cooperation with computer sciences, environmental and life sciences.

In addition, the program also promotes individual diversity and specialization. DSSB students can pick and choose course offerings from the 3 elective tracks based on their individual interests and career plans

The Society and Business Track covers computational social science approaches, smart city, and transport concepts as well as principles of consulting, sustainable economics, and supply chain finance.
The Health & Environmental Track connects socially relevant data science questions with insights and techniques from the natural sciences (health, medical, environmental sciences)
The Data Science Track allows students with a strong mathematical or computing background to dive deep into data mining, data analytics and machine learning

At the end of the 2-year program DSSB graduates will have expertise in digital contents and data science skills to responsibly and capably solve core problems in future organizations and digital societies.

They can identify, analyze, interpret and critically access the social causes and consequences of digital transformation of societies in all of its legal and ethical implications and aspects
They are able to apply cutting-edge analytical and quantitative skills to correctly model and interpret scientific results, to make valid predictions and to derive thoughtful conclusions and interventions for pressing social and business problems
Students have learned to manage big data, develop statistical models and convincingly present them to a science and non-science audience by applying plausible writing, communication and presentation techniques and visualization tools
They will be able to program well in at least one computer language and know about state-of-the-art computational and software tools

Study program structure

Schematic study plan

The Curriculum at a glance

The MSc Data Science for Society and Business program is composed of foundational lectures, specialized modules, interactive seminars, tutorials and applied project work. These lead to a master thesis that can be conducted in close collaboration with research, institutional or industry partners on or even off-campus i.e. at a partner university, a political organization or at a company site. The program takes four semesters (two years). The following table provides an overview of the modular structure of the program. The program is partitioned into five areas (Core, Elective, Methods, Discovery, and Career) and the Master Thesis. All credit points (CP) are ECTS (European Credit Transfer System) credit points. In order to graduate, students need to obtain a total of 120 CPs.

Schematic study scheme

Core area

Core area

Core modules describe and analyze the machine-social context, the changes and the challenges new information technologies impose on today’s and future firms, entire economies and societies. They also teach students data science approaches, new models and analytical techniques. Hence, we aim at three units consisting of two 5 CP modules for research on digitization and societies (10 CP), digital transformation in business (10 CP), and data science and artificial intelligence concepts (10 CP).

Methods area

Methods area

Methods courses are key in data science. Programming skills, innovative, dynamic models and experimental methods as well as most up-to-date software lay the groundwork for understanding, replicating and contributing to research.

Discovery area

Discovery area

Discovery modules immerse students into diverse applications. Faculty from different disciplines introduce to up-to-date data science applications. Invited experts from business, public administration and other organizations reveal their digital data needs and solutions. These diverse experiences and insights lead to innovative experimentation in a data science lab and culminate in an individual capstone project in which students bring their theoretical and practical expertise together to creatively answer pressing social and data science problems, i.e. in health education, social media marketing, robotics, data security or digital government. Students who prefer to complete an internship with a company, or public organization can exchange the capstone project, and two of the mandatory elective career modules for this off-campus learning experience.

Career area

Career area

Modules in the career area aim at broadening the intellectual skills of students and boost their employability. Language modules and seminars on ethical and legal questions help to understand people with other cultural backgrounds and to understand normative concerns about the digitalization of our societies. Targeted modules on communication and career skills directly support students to exchange and function well in professional environments. Students who prefer to complete an internship with a company, or public organization can exchange the capstone project, and two of the mandatory elective career modules for this off-campus learning experience.

Elective area

Elective area

Electives allow students to expand and connect their expertise with other subjects. Courses in business, computer science, criminology, spatial sciences, public health and supply chain management also allow specialization.
The Data Science for Society and Business graduate program attracts students with diverse career goals, backgrounds and prior work experience.
Students can choose to strengthen their knowledge by focusing in one of 3 different areas:

Society and Business
Data Science
Environmental & Health

These are recommended focus tracks. Students may however choose combination of the non-mandatory courses.

DSSB_MSc_Handbook_2024.pdf

Data Science for Society and Business (MSc) program handbook Fall 2024

DSSB_MSc_Handbook_16-08-2023_Track changes .pdf

Data Science for Society and Business (MSc) program handbook Fall 2023

dssb-msc-handbook-2022.pdf

Data Science for Society and Business (MSc) program handbook Fall 2022

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Iana Domnina

Recruitment Counselor & Admissions Officer

Student Recruitment Master’s Degrees

Email: idomnina@constructor.university

Office: RLH, Room 291

WhatsApp: +49 172 3292745

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Target group

The MSc Data Science for Society and Business (DSSB) graduate program is a highly selective program for students who have completed their BA / BSc in the social sciences (e.g. business, economics, political science, psychology, sociology,) who want to become a data scientist with an interest in business and social science research questions.

We are also open to accept students from the humanities and natural science (e.g. history, health or other neuro sciences with a quantitative orientation).

Teaching approach

DSSB is a truly interdisciplinary program that combines a broad course offering from diverse social and non-social science disciplines with elective tracks for individual diversity and specialization.
By combining on- and off-line learning tools in core and method courses, students with diverse cultural and knowledge backgrounds and individual needs are quickly integrated. Remedial online learning allows to close mathematical or technical knowledge gaps.
Students thereby benefit from a close relation between teachers and students, small classes, shared learning experience and innovative learning environments.
To further gain professional and research practice, students have access to the data science lab, may engage in capstone projects, choose an elective internship or participate in public big data challenges.

Students & Alumni

The program is excellent – simple enough for students who have no data science background but it also enhances your skills when you already have data science knowledge.

Ellen Mendy

The unique structure of the DSSB program with tailor-made choice of courses based on individual interests and career plans is outstanding and supports specific professional and research goals.

Victor Osione Momodu

The DSSB programs combines the business part with the data and quantitative part in order to succeed in a job. This is one of the most important skills that employers look for.

Adriana Martinez

When I came to Jacobs, I wanted to be a diplomat. Now, I am a CEO, and I think that is because Jacobs teaches you a lot of different skills, a lot of interdisciplinary skills.

Benjamin Thies

My favorite thing about the Jacobs study program was the multi-disciplinary approach. It made me challenge myself and learn about something very different to what I was normally supposed to learn with my curriculum. It broadened my horizons and made think differently.

Laura Garcia

School of Business, Social & Decision Sciences

This study program is part of the School of Business, Social & Decision Sciences.

The School of Business, Social and Decision Sciences focuses on interdisciplinary research and education in business sciences, finance and economics, political sciences, as well as in fields related social interactions and to cognitive processes underlying behavior of individuals, groups, or institutions.

Key disciplines in the school include Management Science, Finance, Economics, Industrial Engineering, Logistics, Political Science, Cognitive Psychology, and Sociology.