Innovative business data science - BMEGT20VVV1002-00

The aim of the course is to enable students to identify business problems in which data analysis or data science methods can be applied, and - through prototyping - to substantiate and communicate the business value inherent in them. The course introduces students to the theoretical and practical foundations of data analysis methods that support economic decision-making, ranging from descriptive statistics through AI-based predictive modeling to interactive visualization. A key objective is for students to experience the full lifecycle of data analysis in practice, working with a single, comprehensive business database in a modern, innovative software environment. In addition to providing the necessary theoretical knowledge, the course emphasizes the development of analytical thinking, data-driven decision support, and the mindset of “storytelling with data.”

1. Has knowledge of the main tasks and steps of business-oriented data analysis (data collection, exploration, modeling, evaluation, visualization) and the innovative software tools that can be used for these purposes.
2. Understands the basic concepts of descriptive statistics and the importance of examining relationships between variables.
3. Is familiar with the key theoretical models of data science and AI, as well as the fundamental paradigms of supervised and unsupervised machine learning.
4. Knows the tools and principles of modern data visualization and the basics of dashboard design (in the Power BI environment).
5. Is aware of the possibilities of applying data-driven decision support tools in the field of business intelligence (BI).

1. Is able to identify business problems for which data analysis or machine learning solutions can be applied.
2. Is able to use their knowledge to learn and apply statistical and machine learning software tools for data exploration, visualization, and building predictive models.
3. Is able to translate the results of basic data science analyses into clear and understandable business language.

1. Is open to learning about and using the latest information technology tools (AI, ML, BI software).
2. Demonstrates a problem-sensitive and proactive attitude in order to ensure high-quality work.
3. Continuously expands their knowledge through ongoing learning and keeps track of trends in data science.
4. Strives for ethical and responsible data use, as well as error-free task execution.

1. Is able to work independently (choosing appropriate software techniques and learning how to use them).
2. Uses a systems-based, "data-driven" approach in thinking.
3. Is able to take responsibility for the conclusions drawn from analyses and the decision-making proposals based on them.
4. As a member of projects, tries to carry out the tasks assigned to him/her, especially the analysis of a specific segment of a comprehensive business database, independently and responsibly.

Lectures (theoretical foundation), interactive computer exercises (demonstration of software usage), group or individual problem solving and theoretical assessment (test).

• Órai anyagok, PPT-t, kiadott adatbázisok és elemzési fájlok / Lecture materials, ppts, shared data bases and analysis files
• Cole Nussbaumer Knaflic (2015): Storytelling with data
• Fogarassyné Vathy Ágnes, Starkné Werner Ágnes (2011): Intelligens adatelemzés
• Gábor Békés, Gábor Kézdi (2020): Data Analysis: For business, economics and policy, Cambridge University Press

1. Class activity: According to the schedule announced at the beginning of the semester, every student is required to participate in a class activity once during the semester. This is worth 25 points in the final grade. The minimum score for class activity is 12 points. 2. Summative academic performance assessment: According to the schedule announced at the end of the semester, a midterm must be written once, which contains test-type or case study-type questions covering all four topic areas presented during the semester. The midterm is worth 75 points in the grade established at the end of the semester. The midterm does not have a minimum score.

• Class activity: 25
• Midterm: 75
• Total: 100

Both classroom activities and miterm can be replaced/repeated once, both according to the schedule announced at the end of the semester.

Consulted with the Faculty Student Representative Committee, approved by the Vice Dean for Education, valid from: 04.05.2026.

Subject includes the topics detailed in the course syllabus to ensure learning outcomes listed under 2.2. to be achieved. The schedule of topics in the course curriculum in each semester may be affected by the calendar and other constraints.