Bachelor in Financial Data Analysis & Business Intelligence
Explore this Bachelor programme at FINCOM Paris, a business school in Paris connecting analytical skills with business decisions.
Programme overview
Study finance, statistics and data analysis to understand the information needs and risks of financial organisations. Public and authorised datasets support source verification, indicator analysis and reporting, alongside an introduction to digital threats and responsible information use.
FINCOM Paris — Paris College of Commerce and Financial Management. Duration: 3 years. Programme reference: ESPAST-26-B.
The programme progresses from foundations and method exercises to staged projects, enabling students to define problems, select methods and explain findings.
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
- Apply finance fundamentals to markets, statements and organisations, documenting methods, evidence and the basis for professional judgement.
- Apply statistical methods to probability, indicators and uncertainty, documenting methods, evidence and the basis for professional judgement.
- Apply data processing to cleaning, querying and visualisation, documenting methods, evidence and the basis for professional judgement.
- Apply intelligence collection to authorisation, sources and verification, documenting methods, evidence and the basis for professional judgement.
- Apply risk analysis to credit, market and cyber risks, documenting methods, evidence and the basis for professional judgement.
- Apply analytical reporting to conclusions, evidence and decisions, documenting methods, evidence and the basis for professional judgement.
Learning content and competency modules
| Module | Learning content | Assessment evidence |
|---|---|---|
| Finance fundamentals | Markets, statements and organisations. Guided exercises and applied cases. | Problem definition and analytical report |
| Statistical methods | Probability, indicators and uncertainty. Guided exercises and applied cases. | Reproducible case or model file |
| Data processing | Cleaning, querying and visualisation. Guided exercises and applied cases. | Data and operational records with quality checks |
| Intelligence collection | Authorisation, sources and verification. Guided exercises and applied cases. | Option comparison and testing evidence |
| Risk analysis | Credit, market and cyber risks. Guided exercises and applied cases. | Project plan and collaboration records |
| Analytical reporting | Conclusions, evidence and decisions. Guided exercises and applied cases. | Final report and oral defence |
Study progression
- Year 1: build foundations in finance fundamentals and statistical methods, with guided cases, academic reading and communication.
- Year 2: develop data processing and intelligence collection through pathway projects and evaluation of methods and results.
- Year 3: integrate risk analysis and analytical reporting in a final project, documented portfolio and oral defence.
Projects and practice
Analyse public financial data and produce a business intelligence briefing explaining sources and methods.
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 secondary education or equivalent study. A foundation in mathematics, logical reasoning and written communication is recommended. Business, data or social-research projects may support the application.
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
Financial data assistant; business intelligence assistant; risk analysis assistant. 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.
Tuition and fees
Contact admissions for the tuition and payment terms applicable to your chosen programme before enrolment.
Applications
Select this programme and study level when submitting a preliminary application. The admissions office reviews materials and contacts the applicant. A preliminary application is not an admission decision or a payment commitment.
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