DATA ANALYTICS - BMEGT20MN49

The aim of the course is to enable students to identify the business problems in which data analysis or data science methods can be applied and by prototyping them to support and communicate the inherent business advantage. The course introduces students to the theoretical and practical foundations of data analysis methods to inform economic decisions. Another important objective is to discuss the appropriate micro- and macroeconomic applications of data science. The subject provides quantitative knowledge for the analysis and prediction of the studied phenomena. In addition to the transfer of the necessary theoretical knowledge, the subject focuses primarily on the solution of practical tasks, issues of practical application, and the development of a data analysis mindset and approach.

1. Knowsthe most important tasks of business data analysis, the areas of expertise and the tools that can be used in them
2. Know the technical details of the steps required for the main tasks of data analysis (data collection, data preparation, modeling, evaluation, application)
3. Know the most important theoretical models and algorithms of data science, the basic paradigms of supervised and unsupervised machine learning.
4. Know the basic tools and methods of data visualization.
5. Are aware of the basic operation of data-driven decision support tools.
6. Know the most important micro- and macroeconomic applications of data science, data analysis and data visualization, especially in the field of business intelligence
7. Know the learning, knowledge acquisition and data collection methods of the fields of data analysis, their ethical limitations and problem-solving techniques.

1. Are able to identify a business problem to which data science or machine learning solutions can be applied
2. Are able to prototype these possible solutions, visualize their results, and identify business value based on decision making for further analysis.
3. Are able to use the learned theories and methods, explores, systematizes and analyzes facts and basic connections, formulates independent conclusions and critical remarks, makes decision-making proposals, and makes decisions in routine and partly unknown - domestic and international - environments.
4. Are able to determine the complex consequences of economic processes and organizational events.
5. Can apply data analysis problem solving techniques, problem solving methods, their application conditions and limitations.

1. Cooperate with the instructor and fellow students in expanding the knowledge.
2. Expand their knowledge by constantly acquiring knowledge.
3. Are open to the use of information technology tools.
4. In the interest of quality work, demonstrate problem-sensitive, proactive behavior, constructive, cooperative, proactive in a project, in case of group tasks.
5. Strive for accurate and mistake-free problem solving.

1. Are suitable for independent work (selection of methodology, technique; organization, planning, management of work; data collection, systematization, analysis, evaluation; general and professional development),
2. Use a systems approach in your thinking.
3. Take responsibility for their analyzes, conclusions and decisions.
4. As a member of projects, group work, organizational units, they perform the tasks assigned to them independently and responsibly.

Lectures, computational exercises, written and oral communication, use of IT tools and techniques, optional independent and group work, work organization techniques

• Carlo Vercellis: Business Intelligence : Data Mining and Optimization for Decision Making, 2009 John Wiley & Sons
• Olivia Parr Rud: Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management, Wiley
• Gábor Békés, Gábor Kézdi: Data Analysis: Patterns, Prediction and Causality, Cambridge University Press
• Fogarassyné Vathy Ágnes, Starkné Werner Ágnes: Intelligens adatelemzés. ( 2011). Typotex Kiadó
• Dr. Kovács László: Adatelemzési technikák és eszközök (2011) Nemzeti Tankönyvkiadó
• Christoph Molnar (2019) Interpretable Machine Learning. Leanpub
• Thomas Dinsmore (2016) Disruptive Analytics: Charting Your Strategy for Next-Generation Business Analytics
• Matthew North (2019) Data Mining for the Masses, Third Edition: With Implementations in RapidMiner and R
• Vijay Kotu (2018) Data Science: Concepts and Practice
• Cole Nussbaumer Knaflic (2015) Storytelling with data
• Edward Tufte (2007) The visual Dispaly of Quantitative Information
• Scott Berinato (2016) Good Charts

The evaluation of the learning outcomes outlined in point 2.2. is conducted through a mid-term written performance assessment (summative academic performance evaluation) and an optional partial performance assessment (active participation).

1. Written end-term test: during the semester, the course material will be tested with a written end-term test. The test consists of theoretical questions and calculations. At least 50% of the points of the mid-term test must be obtained in order to obtaining the signature and pass the course. 2. Project task: After the semester, a project work/task is required to test the acquisition of practical knowledge that requires the use of all relevant topics. Students carry out the analysis of a selected set of data from the identification of the problem to the visualization of the prototyped business value, covering the syllabus, in small student groups individually. The project task documentation includes data, the performed actions, the obtained results, conclusions and visualizations (eg. a dashboard showing this). End-of-semester assessment is a presentation of this documentation.

1. At least 50% of the points must be achieved for end-term test. The written end-term test can be retaken on week 15th (replacement week). In the event of a retake the test, the result achieved at the replacement enclosure will count towards the final result. Students who do not reach the minimum level of the end-term test after the replacement test may also try to complete the end-term test again during the replacement week, paying a special fee. Thereafter, there is no further opportunity to obtain the signature during the semester.

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.

Part I-III of the Subject Form is to be approved by the Head of Department of Economics named under