Corvinus University of Budapest

What you'll study

This Graduate Diploma covers the foundations and applied techniques of business data analytics, emphasising practical tools and business-relevant interpretation. The curriculum typically combines modules in statistics, data management, programming, machine learning and business intelligence, with case studies drawn from marketing, finance, operations and strategy.

• Foundations of Data Analysis – descriptive and inferential statistics, hypothesis testing and experimental design tailored to business problems.
• Data Management & Databases – relational databases, SQL querying, data cleaning and pre-processing for analysis.
• Programming for Analytics – practical training in languages commonly used in analytics (for example R or Python) for data manipulation and reproducible workflows.
• Machine Learning and Predictive Modelling – supervised and unsupervised methods, model evaluation, cross-validation and model deployment considerations in a business setting.
• Business Intelligence & Visualisation – dashboard design, visual communication of insights and use of BI tools to support decision making.
• Econometrics for Business – applied regression techniques and time series methods for economic and financial datasets.
• Analytics in Functional Areas – applied modules or case seminars showing how analytics is used in marketing analytics, credit scoring, supply chain optimisation and HR analytics.
• Capstone Project or Applied Case Study – an industry-linked project or integrative case where students apply tools to a real or realistic dataset and present recommendations.

Teaching methods commonly include lectures, hands‑on computer labs, group work on real datasets and guest sessions with practitioners. Assessment typically combines coursework, project reports and practical tests rather than solely end-of-term exams.

Entry requirements

Applicants are usually expected to hold a recognised first degree (bachelor’s level) in economics, business, mathematics, statistics, computer science or a related field. Candidates with strong quantitative skills gained through work experience or a different academic background may also be considered.

• Evidence of quantitative ability: prior coursework in mathematics, statistics, econometrics or programming, or relevant professional experience.
• Proficiency in English demonstrated by an appropriate qualification or other evidence, as the programme is taught in English.
• Curriculum vitae and a motivation letter outlining analytics interest and career goals.
• Some applicants may be invited for an interview or asked to complete a short technical test or sample assignment.

Precise entry criteria and acceptable evidence of prior learning or experience are published by the university and may include recognition of prior learning routes for professionals.

Career prospects

Graduates typically move into analytical and data-driven roles across a range of industries. The programme prepares students for positions that require both technical skills and business acumen.

• Data Analyst / Business Analyst – analysing datasets to support operational and strategic decisions.
• Data Scientist (entry-level) – building predictive models and extracting insights to solve business problems.
• Business Intelligence Specialist – developing dashboards and reporting systems that inform management.
• Marketing Analyst / Customer Analytics – using data to support customer segmentation, campaign measurement and lifetime-value modelling.
• Risk and Credit Analyst, Financial Analytics – applying quantitative methods in banking and insurance contexts.
• Consulting roles – working in advisory teams that translate analytics into actionable business recommendations.

Alumni typically find roles in multinational companies, consultancies, banks, fintech firms, start-ups and public sector analytics teams. The combination of applied technical skills and business-focused training also provides a pathway to further study, such as a master’s in data science, business analytics or related fields.

Why study at Corvinus University of Budapest

Corvinus combines a strong tradition in economics and business education with growing expertise in data-driven methods. The university’s programmes emphasise applied learning, often linking classroom work with industry projects and guest lecturers from Hungary’s and Central Europe’s business community.

• Interdisciplinary environment bridging economics, business and quantitative methods, useful for real-world analytics problems.
• Access to practitioners and regional employers through career services and corporate partnerships, supporting internships and project collaborations.
• Small-group teaching in practical lab sessions ensures hands-on experience with contemporary analytics tools and datasets.
• Location in Budapest provides exposure to a dynamic regional business hub with strong finance, consulting and tech sectors.

Prospective students should review the official programme page at Corvinus for the most current information on admission processes and course structure.