The course develops the knowledge and skills needed to design data analyses, interpret statistical methodology, and apply the results of statistical models to real-world business applications. At the end of the course,…
Scheda dell'insegnamento
Obiettivi formativi
The course develops the knowledge and skills needed to design data analyses, interpret statistical methodology, and apply the results of statistical models to real-world business applications.
At the end of the course, students will be able to:
Identify and interpret the theoretical and conceptual foundations of exploratory data analysis, data modeling for information representation, sustainable and ethical decision making in business applications (CILO 1.1 - knowledge).
Identify and interpret the theoretical and conceptual foundations to critically recognize the modern challenges related to complex modeling methods based on machine learning for business applications (CILO 1.2 - knowledge).
Systematically categorize and correctly frame real-world business challenges through data modeling, analysis and visualization (CILO 3.1 – intellectual skill).
Understand the implications and the causal chain that connects business plans, data strategies and individual behavioral patterns towards data (CILO 3.2 – intellectual skill).
Select and apply, using the R statistical software and, where appropriate, AI-assisted tools, the most suitable statistical methods to extract, analyse, and summarise information from different types of data (CILO 4.1 – practical skill).
Answer business research questions using the statistical techniques presented during the course (CILO 4.2 – practical skill).
Develop managerial decision making by recognizing, assessing and analyzing complementary or competitive decisional options to address data-driven insights and the subsequent ethical and sustainable business solutions (CILO 5.1 – transferable skill).
Programma
Module 1
The first module introduces students to statistical methods in quantitative business and marketing. The goal is to learn and apply quantitative techniques that support data-driven decisions. The module is interdisciplinary and concentrates on data analysis from a largely applied perspective. Using the R software, students will manipulate data, create visualisations, and apply statistical models to highlight the relationships between variables.
Module 1 (Data Analysis and Visualization)
Intro to the concept of data science.
Introduction to R
Review of descriptive statistics and inference
Exploratory data analysis: data visualization and manipulation
Simple and multiple linear regression
Logistic regression
Responsible use of AI in data science
Module 2
The second module introduces students to analytics, data strategy, and AI strategy in real-world business contexts. The goal is to understand how data, statistical reasoning, AI-enabled tools, and managerial decision-making can be aligned to address business challenges in an ethical, sustainable, and strategically coherent way.
The module is interdisciplinary and concentrates on the managerial, organisational, and applied implications of data-driven decision-making. Building on the quantitative mindset and analytical skills developed in Module 1, students will examine how data and AI can support business research questions, inform strategic choices, shape organisational behaviours, and generate actionable insights for different professional roles and business functions.
Using case histories, business cases, use cases, and AI-assisted tools where appropriate, students will learn to frame business problems through data, distinguish among different forms of analytics, evaluate the alignment between corporate strategy, data strategy, and AI strategy, and reflect on the ethical, cultural, and behavioural conditions required for effective data-driven transformation. The module directly supports the development of conceptual knowledge, intellectual skills, practical skills, and transferable managerial capabilities related to data modelling, analytics, AI, decision-making, ethics, and sustainability.
Module 2 (Analytics and Data Strategy)
Data Strategy, AI Strategy and Analytics
Descriptive, Diagnostic, Predictive, Prescriptive and Proactive Analytics
Narrow AI, AI, Generative AI – Gen AI Tools and their integration in personal and business workflows
Corporate Strategy, Data and AI Strategy Alignment
HR, Leadership and Communication
Ethics
Data Literacy and Data Culture
Wrap-Up
The course combines lectures, coding and data analysis (60 hours in total). Theory is followed by extensive hands-on exercises, and invited guests will present real-world business cases. A laptop is required in class so that students can try out the concepts as they are explained.
Modalità d'esame
Students can choose between a “multiple assessment route” and a “single assessment route”.
Multiple assessment route
Students are evaluated through two written tests, one per module. Both tests are scheduled to the end of each module, that is, one test at the end of module 1, and the other test at the end of module 2. The module 1 test consists of a written exam with empirical exercises in both open-ended (weight: 50% of the test grade) and multiple-choice formats (weight: 50% of the test grade). The module 2 test consists of a number of multiple-choice questions (weight: equal weight for each question totalling 100% of the test grade). The final mark is computed as the simple mean of the two grades reported in written tests, rounded to the first integer. For a PASS in the multiple assessment, the following conditions must be met: (i) the two written tests are mandatory; (ii) each test must be passed individually (i.e., a minimum grade of 18 is required for each test). Students who do not pass the multiple assessment are evaluated according to the single assessment. Even though attendance is not mandatory for a PASS in the multiple assessment, it is strongly recommended.
Single assessment route
The single-assessment route consists of a summative written test made up of open and multiple-choice questions covering the whole course programme. Attendance is not mandatory for a PASS in the single assessment, but it is strongly recommended.
The student's preparation is evaluated according to the following grading scale:
Grade
Common description
Multiple-choice questions
Open-ended empirical exercises
30 - 30 cum laude
Excellent quality showing a full achievement of learning outcomes beyond normal expectations.
Near-perfect accuracy across the full breadth of the syllabus, including the most challenging items. Full command of statistical concepts, methods and terminology.
Fully correct description and application of statistical tools. Output is interpreted with rigour, with original and critical arguments that connect statistical evidence to business and ethical implications.
27-29
Very good quality showing that learning outcomes are surpassed.
Very high accuracy across the syllabus, with only minor errors on challenging items. Clear command of statistical concepts and terminology.
Correct description and application of statistical tools, with minor imperfections. Output is interpreted clearly and accurately, with some original and critical arguments linking results to business implications.
24-26
Good quality showing that learning outcomes are achieved and to some extent surpassed.
Good accuracy on standard items, with some errors on more challenging questions. Adequate command of statistical concepts.
Generally correct description and application of statistical methods, with some inaccuracies. Output is interpreted in a complete and coherent way, with limited originality and critical reasoning.
21-23
Basic quality with clear evidence that learning outcomes are achieved.
Acceptable accuracy on standard items, with several errors. Basic command of statistical concepts with some inadequacies.
Basic description and application of statistical methods, with notable inadequacies in code interpretation or methodological understanding. Interpretation of output is present but limited, without original or critical arguments.
18-20
Sufficient quality showing that learning outcomes are being achieved.
Sufficient accuracy on basic items only; difficulty with more challenging questions. Partial knowledge of statistical concepts.
Partial and imperfect description and application of statistical methods. Interpretation of output is framed but limited; business and ethical implications are barely addressed.
0-17
Unsatisfactory quality showing that learning outcomes are not achieved.
Absent or poor accuracy across the syllabus. Absent or wrong knowledge of statistical concepts.
Absent or wrong description and application of statistical methods. Interpretation of output is absent or incorrect; no connection to business or ethical implications.
If evidence emerges suggesting the use of AI tools in the written examination, the student may be required to take an additional oral examination to assess their actual knowledge and understanding of the subject.
Testi di riferimento
Required readings
Cetinkaya-Rundel, M., and J. Hardin. Introduction to Modern Statistics (2e). Freely available at https://openintro-ims.netlify.app. (Multiple assessment: Selected Chapters; Single assessment: Selected Chapters).
James, G., D. Witten, T. Hastie, and R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Freely available at https://www.statlearning.com. (Multiple assessment: Selected Chapters; Single assessment: Selected Chapters).
Additional readings
Additional readings are detailed in the teaching materials published in Blackboard.
Codice insegnamento business-analytics