Insegnamento a.a. 2026-2027

• Definition of Big Data and modern data ecosystems.
• Parallel and distributed computing.
• Machine learning: supervised and unsupervised methods.
• Model evaluation and validation.
• Survival Analysis and time-to-event modeling.
• Natural Language Processing and text mining.
• Artificial Intelligence: Neural Networks and deep learning.
• Introduction to programming in R.
• Machine learning in R.

• dentify business problems where modern data analytics and machine learning can generate actionable insights.
• Distinguish between supervised and unsupervised machine learning methods and select the appropriate approach for a given problem.
• Describe the main techniques for model evaluation and explain how to compare competing models.
• Explain the key challenges of analyzing unstructured textual data and illustrate the main NLP approaches to address them.
• Describe the architecture of Artificial Neural Networks and identify their main components and applications.
• Summarize and communicate data-driven findings to both technical and non-technical audiences.
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• Apply data-analytic thinking to diagnose and solve business problems involving potentially large and/or complex datasets.
• Implement machine learning and NLP pipelines in R, from data preparation through model evaluation.
• Produce data visualizations and statistical summaries that communicate findings clearly to diverse audiences.
• Collaborate in small teams to design, execute, and present a data analysis project in a structured scientific report.​​

• Lectures
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments

• Recorded walkthroughs of R programming labs are made available to students for independent review and home practice.
• Attending students complete a collaborative group assignment in which they apply course methods to a real dataset, culminating in an in-class presentation at the end of the course.

• One group assignment consisting of a technical report and an in-class presentation, representing 80% of final grade. Students apply the methods covered in the course to a realistic dataset or case of their choice, verifying their ability to implement, evaluate, and communicate a data-driven solution — directly assessing the applying-knowledge and transversal (teamwork, communication) outcomes. The in-class discussion also allows the instructor to verify individual understanding through follow-up questions.
• One final multiple-choice written exam representing 20% of final grade, verifying students' acquisition of the core theoretical and conceptual knowledge covered in the course.

• One individual assignment consisting of a technical report and a pre-recorded video presentation, representing 60% of final grade, submitted to the instructor for review. This verifies the same applying-knowledge outcomes as the group assignment, adapted to an individual format since non-attending students do not take part in in-class discussion.
• One final multiple-choice written exam representing 40% of final grade, verifying the same theoretical knowledge outcomes as for attending students; the higher weight compensates for the absence of an in-class discussion component.

Main source:

• Slides provided by the instructor.
• Papers will also be circulated by the instructor.

Additional sources:

• B. BAUMER, D. KAPLAN, N. HORTON (edition by CRC Press), Modern Data Science with R. — Covers the full data science workflow from a business/social science perspective, very well aligned with the course audience.
• M. KUHN, J. SILGE (edition by O'Reilly), Tidy Modeling with R. — A natural companion to the existing Silge & Robinson text; covers model building, evaluation, and cross-validation in a consistent tidyverse framework.
• J. SILGE, D. ROBINSON (edition by O'REALLY),Text Mining with R: A Tidy Approach.
• G. GROLEMUND, H. WICKHAM (edition by O'REALLY), R for Data Science.
• G. GROLEMUND (edition by O'Really), Hands-On Programming with R: Write Your Own Functions and Simulations.

Advanced readings:

• Trevor Hastie, Robert Tibshirani, Jerome Friedman:The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (available in pdf here: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

• G. JAMES, D. WITTEN, T. HASTIE, R. TIBSHIRANI (edition by Springer), An Introduction to Statistical Learning with Applications in R (ISLR). — A more accessible companion to ESL, with R labs directly relevant to the course. Freely available at https://www.statlearning.com. Given that ESL is already listed, ISLR is the natural stepping stone students will actually use.

• E. FRANK, M. HALL, I. WITTEN (edition by Morgan Kaufmann), Data Mining: Practical Machine Learning Tools and Techniques. — Strong on model evaluation and practical ML, good complement to the Hastie et al. texts.

• F. CHOLLET (edition by Manning Publications), Deep Learning with R.
• F. CHOLLET (edition by Manning Publications), Deep Learning with Python.