Data Analytics at Université de Montelieu combines statistical theory with hands-on coding in Python and SQL. You will clean real-world datasets, build dashboards, and interpret findings for business and policy audiences. Modules cover probability, regression, experimental design, and introductory machine learning. Campus seminars use anonymised datasets from public health, finance, and education partners. Communication skills are emphasised — analysts must explain uncertainty, not just produce charts.
BSc (Hons) Data Analytics is taught as one award, not a list of unrelated modules. The published length is 3 years full-time, and the credit total is 360. Students move from Statistics I — Probability & Inference through Statistics I — Probability & Inference; Programming for Analytics (Python/R); Data Visualisation & Storytelling; Business Context for Data; Linear Algebra & Discrete Mathematics; Data Acquisition & Wrangling, and finish on Capstone Analytics Project & Dissertation.
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: Clean, analyse, and visualise structured datasets with reproducible notebooks Apply regression and hypothesis testing to business questions Build interactive dashboards for non-technical stakeholders
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.
Access to ESCP's analytics sandbox includes JupyterHub, Tableau licences, and faculty office hours in the Data Studio.
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