MACHINE LEARNING AND ANALYTICS

The Machine Learning and Analytics module is one of the mandatory courses within the doctoral program in Management School. The aim of the module is to equip students with theoretical and applied tools for effectively managing, analyzing, and visualizing data, enabling the construction of predictive models to extract insights from vast datasets within the business realm. Throughout the course, participants will delve into the fundamental principles and complexities of machine learning, gaining insights into various approaches and their respective strengths and limitations.

At the end of the course, students will be able to:
- Identify business problems and design a strategy to approach them
- Demonstrate the ability to discern and implement appropriate machine learning algorithms tailored to real-world applications
- Cultivate critical thinking skills in evaluating quantitative approaches and models, exhibiting a deep comprehension of the nuances and obstacles inherent in machine learning and data-driven analytics. Students will be encouraged to assess advantages and drawbacks of different methodologies presented during lectures.
- Enhance communication proficiency by grasping key terminology and concepts, enabling them to present ideas, findings, proposals, analyses, and critical reasoning effectively within the domain of business analytics. Emphasis will be placed on honing oral presentation and pitching skills during group projects, as well as on crafting empirical papers.

Descriptive and inferential statistics, basics of econometrics and coding in R.

The module covers the following topics:
-what is machine learning and why it is relevant for business and management studies
- example of application of ML in business and management studies
- differences between causal modeling in econometrics and predictive modeling in ML
- review of the most popular algorithm in supervised and unsupervised learning
- introduction to the use of Natural Language Processing in business studies

Békés, G., & Kézdi, G. (2021). Data analysis for business, economics, and policy. Cambridge University Press. (selected chapters)
Slides and further materials are available on Moodle

The assessment consists of group exercises in R (70%) during the module and a final written exam (30%).

scritto

Il/la docente ha il dovere di vigilare affinché siano rispettate le regole di autenticità e originalità delle prove d'esame. Di conseguenza, nei casi in cui vi sia il sospetto di un comportamento irregolare, l'esame può prevedere un ulteriore approfondimento, contestuale alla prova d'esame, che potrà essere realizzato anche in modalità differente rispetto alle modalità sopra riportate.

18-20: Sufficiente – conoscenze minime richieste
21-23: Discreto – preparazione accettabile con alcune lacune
24-26: Buono – competenze solide, con qualche imprecisione
27-29: Ottimo – conoscenza approfondita e ben esposta
30-30 e lode: Massimo riconoscimento – conoscenza superiore ed elaborazione originale

The lectures encompass both theoretical lessons and discussions on real business problems that necessitate the utilization of R for data processing and analysis. Hands-on activities will cover about 50% of classes.

Ca’ Foscari University applies Italian Law (Law 17/1999; Law 170/2010) concerning the support services and accommodations available to students with disabilities or specific learning disorders. If you have a motor, visual, or hearing disability or any other disability (Law 17/1999) or a specific learning disorder (Law 170/2010) and require support (classroom assistance, technological aids for examinations, individualized exams, accessible format materials, note-taking assistance, specialized tutoring to support studying, interpreters, or other services), please contact the Disability and Specific Learning Disorders Office at disabilita@unive.it.