Advanced Business Analytics

• Identify business and societal impact opportunities related to effective data utilization
• Summarize the identifying characteristics of advanced machine learning approaches for descriptive, predictive and prescriptive analytics
• Select and apply appropriate machine learning (regression, classification, reinforcement learning, clustering) and data management tools (e.g Pandas)
• Conduct small-scale machine learning experiments and understand related scaling principles
• Apply one or more explainable AI techniques (e.g SHAP, Lime) in data-driven decision advisory situations
• Conduct basic analysis of information in a natural language form
• Understand the technical principles and potential of foundation models (e.g. LLMs)
• Quantify uncertainties in predictive modelling, through quantile regression and heteroskedasticity model
• Understand and be able to explain causal vs correlational relationships between variables
• Be able to provide a clear and informative summary (executive summary) for data-driven analyses and tools, including insights for business and critical questions

The classes are taught in an interactive manner, with theoretical parts, intermingled with practical exercises. The practical exercises are done in Python.

The main topics covered in the course includes web data mining; natural language processing; recommender systems; explainable AI; deep learning; reinforcement learning; spatio-temporal prediction models; ensemble models; survival analysis; prediction uncertainty and causality.

Lectures, practical laboratories and online learning, i.e. self-learning with online resources (e.g. with iPython notebook).