COURSES

Goals

The goal of the course is to provide general and advanced knowledge of business intelligence and business analytics extended with the knowledge and skills for strategic (marketing) decision-making. Firstly, the business intelligence and business analytics grounds will be presented, followed by the goals, objectives, and common problems of their adoption. Strong focus is given to best practices. The students who will successfully complete this course will master the basics and some advanced areas of business intelligence and will be capable of applying these methods in solving demanding business problems and evaluating their results.

Curriculum

Scientific method: Structure of scientific knowledge, scientific activities, and processes. Introduction: Definition of intelligence and business intelligence (BI), basic BI schema, criteria, reasons and areas for implementation, problems and pitfalls during implementation, and best business practices. Definition of business analytics and examples of its application, an overview of the differences between business intelligence and business analytics, along with real-world examples. Data management: Data warehouses, data quality, data preparation and enrichment, data migration, data delivery. Examples of major risks and errors. Business analytics: Identification, analysis, and definition of business problems, intelligent analytical modeling to solve business/market problems, evaluation of results and their transfer into business practice. Overview of typical business problems. Marketing strategies and direct marketing: Business strategies, planning and development of strategies, direct marketing strategies, business models, analysis of marketing opportunities and environment. Market and customer analysis, contact strategies, marketing channels, integration challenges, personalization of marketing content, monitoring customer activities, managing marketing effectiveness, event-based marketing, real-time marketing. Game theory and its application: Antagonistic games with simultaneous and sequential moves, Nash equilibrium and how to find it, pure and mixed strategies. Business applications: bargaining, auctions, negotiations. Computer simulation. Challenges in software system development and project implementation: Presentation of the entire software project development process, with an emphasis on solving problems encountered in larger projects. Use of generative artificial intelligence in BI: Generative artificial intelligence (AI), such as advanced language models (e.g., GPT), brings new opportunities for automating data analysis processes, predicting trends, and generating business reports. The use of generative AI enables faster and more personalized analysis of large datasets and the creation of scenarios based on historical data. Additionally, generative AI systems can support business decisions by offering multiple solution options and predictions, improving the accuracy of strategic decision-making. Tools and solutions: Overview of the best tools and solutions available on the BI/CI market, as well as a glimpse into upcoming technologies.