Overview of analytics in the business context; concepts of strategic and operational analytics; overview of concepts like dimensional modeling, the Model Lifecycle, data mining, big data, KPIs and metrics, ERP and analytics, in-database/memory analytics; real-time analytics and data stream analysis.

SQL, stored procedures and data warehouse architectures; data warehouse requirements definition, design and analysis; overview of the steps in building information-enabled business models; role of data warehousing in data mining and analytics.

Fundamental concepts of machine learning with a focus on its applications in the business domain. Explores the core concepts of different machine learning algorithms in both supervised and unsupervised learning, and applies them to solve complex problems in a variety of industries, from medical diagnostics to credit default prediction. Additionally, covers practical considerations such as feature engineering, model improvement and selection, as well as the ethical implications of using machine learning in business decision making.

Platforms and architectures for building and working with large-scale structured, unstructured and multidimensional data sources and systems with a focus on utilizing these tools in business applications. Building recommender systems and natural language processing, recent advancements in deep learning and large language models and their applications.

Model lifecycle analysis, analytics governance, aligning governance, aligning firm and analytics strategy, big data security issues, enterprise search, applied projects.