MSc in Financial Data Analysis and Business Intelligence in Paris
Explore this MSc programme at EBFM Business School, a business school in Paris connecting analytical skills with business decisions.
About the MSc in Financial Data Analysis & Business Intelligence
The MSc in Financial Data Analysis & Business Intelligence at EBFM Business School combines subject knowledge with case analysis and applied project work.
Develop financial data research, multi-source business intelligence and decision support. Evaluate risk indicators, events and digital threats, connecting analytical models with investigation evidence, governance and secure communication.
EBFM Business School — European School of Business Finance and Management. Duration: 2 years. Programme reference: ESPAST-26-M.
Building on relevant undergraduate study, the programme develops comparison of methods, work with complex constraints and independent research or applied project management.
Study pathways
Financial data analysis; business intelligence; financial risk and digital threats. Pathways share a common core. Applicants indicate their preferred pathway; availability is confirmed before admission.
Learning outcomes
On completing the programme, students will be able to:
- Organise and analyse corporate financial, market and operational data.
- Build financial analysis models and compare returns and risks under different scenarios.
- Combine financial statements, industry research and public information to assess business performance.
- Identify data anomalies, financial risks and potential digital threats.
- Present findings in clear reports to inform investment and business decisions.
- Independently complete a financial data analysis or business intelligence project and explain its methods, conclusions and limitations.
MSc in Financial Data Analysis & Business Intelligence: curriculum
Five courses in each of the first three semesters. The fourth semester is devoted to the internship and final assessment.
Year 1 · Semester 1
| Course |
|---|
| 1. Business Research Methods and Academic Writing |
| 2. Business Data Analysis and Statistical Methods |
| 3. Financial Analysis and Business Decisions |
| 4. Corporate Strategy and Organisation Management |
| 5. Business Ethics, Compliance and Data Governance |
Year 1 · Semester 2
| Course |
|---|
| 1. Business Modelling and Scenario Analysis |
| 2. Enterprise Risk Management and Internal Control |
| 3. Business Information Systems and Data Visualisation |
| 4. Financial Data Processing and Modelling |
| 5. Multi-Source Business Intelligence Analysis |
Year 2 · Semester 3
| Course |
|---|
| 1. Project Management and Organisational Change |
| 2. Investment Appraisal and Performance Management |
| 3. Applied Research and Final Project Design |
| 4. Financial Risk Governance and Digital Threats |
| 5. Financial Decision Support and Model Validation |
Year 2 · Semester 4: internship and final assessment
Professional internship, practice report, final project or dissertation, and oral defence. The final project is prepared during semester 3 and completed alongside the internship.
Employed students may apply to use a relevant workplace project for the practice component, subject to academic approval, supervision and assessment.
Projects and practice
Study business and digital risks in a financial organisation, submitting analytical models, evidence and decision recommendations.
Project submissions document the problem, process, individual contribution, validation evidence and limitations. Individual contributions are assessed within team projects. Placements and external projects depend on actual arrangements; no particular employer or placement is guaranteed.
Teaching and assessment
Business cases, data laboratories, scenario analysis and team projects use public, simulated or authorised information. Cases may draw on finance, insurance, industry, telecommunications and healthcare.
Module assignments, cases, practical work, staged projects and the final oral defence assess conceptual accuracy, methods, evidence quality, reliability, communication and reflection. Weightings, reassessment and extension rules are published before teaching begins. Moodle can support resources, assignments, quizzes and feedback; practical activities are recorded and assessed separately.
Entry requirements
Provide evidence of a relevant undergraduate qualification or equivalent experience in business, management, economics, finance, data or a related field. Explain your statistical, analytical and organisational background. Technical investigation pathways also assess networking and data skills.
Submit education evidence, transcripts, a CV, a motivation statement and any required project portfolio. Teaching languages, language evidence and translation requirements are specified in the intake announcement. Website language options do not determine teaching language.
Diploma
Complete the required modules, assessments, final project and applicable academic requirements to receive an institution-issued diploma.
Career development after the MSc in Financial Data Analysis & Business Intelligence
Financial data analyst; business intelligence analyst; digital risk analyst. Employment depends on skills, experience and employer requirements; regulated roles also require applicable qualifications and authorisations. Further study is subject to the receiving institution’s admission requirements.