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: Scientific knowledge structures, scientific activities/processes. Introduction: Definition of intelligence and business intelligence (BI), basic BI schema, criteria, reasons and areas for adoption, common problems and pitfalls, best practices, definition of business analytics (BA) and some use-cases, review of differences among BI and BA, best practices. Data handling: Data warehousing, data quality, data preparation/enhancement, data migration, data mediation, examples of major pitfalls. Predictive business analytics: Business problem detection, analysis, and definition, analytical modeling for solving business/marketing problems, evaluation and business adoption of modeling results, overview of various industry. Marketing strategies and direct marketing: Business strategies, strategy planning and development, direct marketing strategies (product, offer, media, distribution and creative strategies), business models, analysis of marketing opportunities and environment. Customer/market analysis and research, contact strategies, marketing channels, integration aspects, creative tactics, content personalization, response tracking, marketing performance management, event-driven marketing, real-time marketing, best practice examples in various industries. Game theory and its applications: Simultaneous-move (static) and sequential-move (dynamic) non-cooperative games, Nash equilibrium and how to find it, pure and mixed strategies. Business applications: bargaining, auctions, negotiations. Challenges in software engineering and project implementation: A detailed overview of development of software project with the emphasis on understanding problems that are specific to big software projects. Applications of generative AI in BI. Tools and Solutions: Overview of best-of-breed BI/CI tools and solutions in the marketplace, insight into emerging technologies.