TOOLS FOR BUSINESS ANALYTICS-1

The course Tools for Business Analytics is a core subject within the quantitative area of the Bachelor's Degree in Business Administration and makes a significant contribution to developing methodological and applied skills related to the collection, processing, analysis, and interpretation of data to support business decision-making, with particular attention to the impact of technological innovation and digitalization on managerial processes.

Within the study plan, the course is offered in the second year and awards a total of 12 CFU credits. It is divided into two parts. The first part, Fundamentals of Inferential Statistics (6 CFU, SECS-S/01), is dedicated to the foundations of inferential statistics. The second part (6 CFU, MAT/09) is structured into two integrated modules: (i) Business Analytics & Data Analysis and (ii) Decision Modelling & Optimization for Analytics.

After an initial methodological introduction in the first module of the course, aimed at providing the necessary conceptual and operational foundations, the course takes on a strongly applied approach and is oriented toward problem solving: starting from a business problem, students are guided in identifying relevant KPIs, constructing a dataset suitable for analysis, developing descriptive analyses and basic predictive models, and formulating prescriptive decision recommendations, using tools that can be implemented in Excel.

At the end of the course, students will be able to design and carry out an “end-to-end” business analysis across the entire process, from problem definition to result interpretation and the formulation of operational recommendations, using Business Analytics tools with particular emphasis on Excel and decision modelling.
In particular, students are expected to achieve the following learning outcomes.
1. Knowledge and understanding
-Understand the basic methodologies of inferential statistics.
-Understand the Business Analytics framework in its main components (descriptive → diagnostic → predictive → prescriptive) and the role of KPIs in business decision-making processes.
-Understand the fundamental principles of data preparation and data quality required to build datasets suitable for analysis.
-Understand the basics of forecasting methods and regression models used for prediction and scenario analysis.
-Understand the core concepts of decision modelling and optimization, with particular reference to objective functions, constraints, trade-offs, and sensitivity analysis.
-Gain an introductory, managerial understanding of how AI/ML tools work, as well as the main risks associated with their use, such as bias, leakage, drift, and issues related to responsible use.
2. Ability to apply knowledge and understanding
-Apply the main inferential statistical techniques critically.
-Translate a business problem into analytical objectives, decision constraints, relevant KPIs, and criteria for evaluating the “cost of errors.”
-Prepare data in Excel through cleaning, transformation, and consistency checks, producing descriptive and diagnostic analyses using tables, pivot tables, and charts.
-Build basic predictive models and decision scoring tools in Excel, evaluating their performance using appropriate error metrics and confusion matrices.
-Formulate and solve prescriptive analytics problems using scenario analysis, what-if analysis, and the Solver tool, for example in budget allocation, product mix, and capacity planning problems.
-Organize and present analysis results through dashboards and concise, structured decision documents.
3. Judgement skills
-Critically evaluate data quality, result consistency, and the robustness of conclusions, making assumptions, limitations, and sensitivity to alternative scenarios explicit.
-Select analytical tools and methods consistent with the decision-making context, operational constraints, and the potential consequences of analytical or forecasting errors.
4. Learning skills
-Independently consult technical materials, manuals, tutorials, and documentation to apply analytical tools to new business cases.
-Continuously update one’s skills in response to the evolution of digital tools and business analytics practices.
5. Communication skills
-Communicate analytical results, assumptions, limitations, and decision implications clearly and effectively, adapting language to non-specialist managerial audiences.
-Synthesize quantitative evidence into communication formats oriented toward decision support, including dashboards, charts, and decision briefs.

Have passed the Statistics exam.
In addition, a basic background in mathematics and matrix algebra is required.

The course is divided into two parts.
The first part, methodological in nature and preparatory to the subsequent applied developments, is dedicated to the fundamentals of inferential analysis.

Fundamentals of Inferential Statistics
1. Estimation theory and confidence intervals
2. Hypothesis testing on the mean of a population and on proportions
3. Hypothesis testing for the comparison between two populations
4. Independence tests between two categorical variables
5. Linear regression

The second part is structured into two integrated modules and combines theoretical lectures with practical exercises based on business cases.
Business Analytics & Data Analysis
1. The Business Analytics framework: descriptive, diagnostic, predictive, and prescriptive analytics; definition of objectives, decision constraints, KPIs, and cost of errors.
2. Data preparation: data quality, cleaning, transformations, consistency checks, and the construction of “analysis-ready” datasets suitable for analysis.
3. Exploratory Data Analysis (EDA) and descriptive/diagnostic analysis using tables, pivot tables, and charts; initial analysis of key drivers.
4. Inferential analysis in Excel using add-ins and supporting tools.
5. Linear regression in Excel for predictive and interpretative purposes: coefficients, goodness of fit (R²), residuals, and main practical applications.
Decision Modelling for Analytics
6. Customer analytics: RFM analysis (Recency, Frequency, Monetary) and segmentation for managerial purposes.
7. Scoring and decision support: confusion matrices, threshold selection, and evaluation of error costs.
8. Introductory concepts on the use of artificial intelligence in Business Analytics: interpretation of model outputs, main risks (bias, leakage, drift), and responsible use.
9. Prescriptive analytics and decision modelling: formulation of decision problems, objective function, constraints, and what-if/scenario analysis.
10. Use of Excel Solver for formulating and solving simple allocation, planning, and optimization problems.

Main texts/resources (supporting lectures and practical sessions):
- Betty Thorne - Paul Newbold - William L. Carlson, Statistics - 9th ed. (2021), Chapters 8–13, Pearson
- Lecture notes and Excel files provided by the instructor via Moodle.

The exam aims to assess the acquisition of conceptual and operational tools and the ability to apply them to business cases.

The exam consists of two parts:
- The first part is a written test consisting of 6 quizzes and 1 structured exercise, for a total of 22 points.
- The second part involves a practical empirical analysis of a case study provided by the instructor, to be taken after the first part, for a total of 10 points.
Access to the second part is granted only if a score of at least 14 is achieved in the first test.

written

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

A. Grades in the 18–22 range: sufficient understanding of concepts and guided application of tools; basic analysis and communication.
B. Grades in the 23–26 range: good understanding and generally correct application; fair autonomy of judgement; clear communication.
C. Grades in the 27–30 range (and honors): excellent understanding, autonomous and robust application; ability to evaluate trade-offs and limitations; effective and professional communication (dashboard + decision memo).

The course is delivered through a combination of lectures, applied exercises, and case discussions. Each topic is introduced starting from a business problem and developed through examples, real or realistic datasets, and guided activities in Excel, with the aim of fostering the practical application of Business Analytics and decision modelling tools.
Group project activities and in-class discussions are also included, aimed at developing the ability to interpret results, justify analytical choices, and effectively communicate their decision-making implications.

Students are required to register on the course page on the university’s Moodle platform in order to access teaching materials, datasets, and any assignments scheduled during the course.
During the lectures, information will also be provided regarding office hours, the schedule of any assignments, and operational instructions related to the software tools used.