Responsible Data Science
A Diploma of Advanced Studies for professionals who want to navigate the green and digital transition through data-driven solutions, responsible AI, ESG data, and practical innovation methods.
Diploma of Advanced Studies, with flexible pathway options
This programme helps participants use data, AI, ESG principles, and digital tools for better decision-making and organizational transformation.It builds a practical foundation in data-driven work, ESG data, digital responsibility, and sustainable business practice, with specialization pathways in Accelerated Data Science, Green & Digital Transformation, or AI-Enhanced Low-Code Data Science.
Framework Modules
Explore the obligatory modules and specialization pathways from data literacy and ESG data to AI, low-code tools, and responsible digital transformation.
Certificate Options
Complete the full programme as a Diploma of Advanced Studies, or take selected parts as Certificates of Advanced Studies that can later be built toward the full diploma.
Dates & Format
Currently available for organizational groups of 10+ participants. Individual learners can express interest below.
Admission
Contact us below for more information about the program, admission options, and learning pathways.
Why this Course?
Oganizations are under growing pressure to use data, AI, ESG principles, and digital tools in a more practical and responsible way. The challenge is no longer only access to technology, but the ability to translate data into better decisions, meaningful transformation, and sustainable business practice.his course helps participants build that bridge. It gives professionals the foundation to understand data-driven work, use AI and digital tools with confidence, and connect ESG information with real organizational challenges. Instead of treating digital transformation and sustainability as separate topics, the programme brings them together as part of one practical skillset.
Data, AI, ESG principles, and digital tools are becoming central to how organizations make decisions and manage transformation. This course helps participants build the practical bridge between these fields: understanding data-driven work, using AI and digital tools with confidence, and applying ESG information to real organizational challenges.
No advanced technical background is required. The focus is on practical understanding, strategic confidence, and the ability to work with data in responsible business contexts.
Obligatory Modules
The obligatory modules provide all participants with a common foundation in data-driven work, ESG data, digital responsibility, and organizational transformation. They are designed to connect practical analytics skills with the broader strategic and regulatory context in which data and AI are used today.
Data Diven Organizational Transformation
This module introduces participants to the role of data in modern organizational transformation. It focuses on how business problems can be translated into data questions, how datasets can be explored and visualized, and how tools such as Excel, Tableau, and Microsoft Power BI can support better decision-making. Participants also develop a foundation in statistics, performance metrics, and data storytelling, helping them communicate insights in a practical and business-relevant way.
Data Science Foundations: ESG Data-Centered
This module builds the technical foundation needed to work with data in responsible and sustainability-related contexts. Participants are introduced to essential SQL skills, basic Python, ESG-related datasets, inferential statistics, and statistical analysis tools such as SPSS. The module also explores how AI techniques can enhance data analysis and support more informed decision-making across organizational and ESG challenges.
Digital Responsibility: Sustainable Practices & Regulations
This module connects data science with responsibility, compliance, and sustainable business practice. Participants explore topics such as responsible management, ethics in data science, corporate digital responsibility, AI alignment, ESG data, sustainable finance regulation, the EU AI Act, and related European regulatory frameworks. The module helps participants understand how data and AI can be applied in ways that are not only effective, but also responsible, transparent, and aligned with sustainability goals.
The goal is to turn data into information, and information into insight.
Accredited learning with flexible participation formats
The Responsible Data Science programme is fully accredited by Steinbeis Akademie, the upskilling and continuing education arm of Steinbeis University. The programme is delivered in a blended format, combining online learning with Berlin-based components that connect data, AI, ESG, and digital transformation with practical organizational application.Participants can complete the full programme as a structured qualification pathway or study selected modules individually, depending on their professional goals, available time, and organizational needs.
Diploma of Advanced Studies
Complete the full Responsible Data Science programme as an accredited Steinbeis Akademie qualification. This option combines the obligatory foundation modules with a selected specialization pathway and is designed for participants who want a comprehensive learning journey in data-driven transformation, ESG data, digital responsibility, and applied AI.
Certificate of Advanced Studies
Participants may also study selected modules individually as part of a more flexible Certificate of Advanced Studies pathway. This option is suitable for professionals or organizations that want to focus on specific competency areas such as ESG data, digital responsibility, AI-supported analysis, or green and digital transformation.
Admission
There are no formal admission requirements for this course. It is designed for professionals who want to strengthen personal well-being and connect daily work with longer-term purpose.
Register Here
Interested in joining the course? Complete the registration form and we will contact you with the admission form and further enrolment information.