A double-path master's: a shared analytical core, then two named pathways — Data Science (modelling, ML engineering, experimentation) and Product Management (discovery, roadmapping, metrics) — closing with a dual-track capstone that ships a data-informed product decision. For analysts moving into product and PMs who need real quantitative depth.
SRH University teaches this award to one academic standard whether you study on campus in Warsaw or on the tuition-free online pathway. Assessment is identity-checked; the marking scheme does not change with your postcode. This is an academic Master's award; where a field has a licensing or registration route, that remains a matter for your professional body or regulator.
You will work through the material as a practitioner-researcher: applied methods, real or anonymised cases, and a supervised capstone or dissertation as the final component. Faculty mark whether you noticed the constraints a slide deck usually hides.
MSc Data Science & Product Management (Dual Track) is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 42. Students move from Statistics & Experimental Design for Decisions through Statistics & Experimental Design for Decisions; Data Engineering & Analytics Foundations; Product Discovery, Users & Value; Research Methods & Identity-Checked Assessment; Machine Learning Theory & Practice; Predictive Modelling & Forecasting, and finish on Dual-Track Capstone: Data-Informed Product Decision.
Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation.
The award is built so that a graduate can do the following in practice: Turn a messy business question into a defensible analysis Choose, fit and validate the right statistical or ML model Build dashboards and pipelines that other people can trust and maintain
Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass.
The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.
The programme carries 42 credits across four assessed modules. Delivery is tuition-free (0) on the online pathway. Transcripts use letter grades and a GPA scale consistent with SRH University awards. No professional-body logo is used in marketing to imply endorsement.
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