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.
Mada University for Science & Technology 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 Mada University for Science & Technology awards. No professional-body logo is used in marketing to imply endorsement.
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