Information, data, and quantitative models increasingly characterize modern decision-making processes. Knowing how to effectively interpret and read the data available to a company and create appropriate models for identifying optimal strategies is often the key to greater competitiveness. Business Analytics refers to the skills, technologies, and methods that enable the analysis of both current and past business performance data in order to guide decision-making and planning. The course provides a broad introduction to Business Analytics, allowing students to become familiar with various types of problems and the quantitative methods that are most commonly used in solving economic and management problems and developing decision-making strategies. An application-oriented approach will be favored. Specific attention will be given to the practical implementation of the proposed methodologies through software packages commonly used in business practice (Excel), an introduction to the use of Python with applications to managerial cases in the field.
Dublin descriptors:
Elements of Mathematics and Statistics
Lectures, Laboratory activities at the Decision LAB, Seminars and Workshops
The exam consists of a written test and, if passed, an oral exam that will cover all the topics covered in the program.
PART 1
- Peccati L., Salsa S., Squellati A.,: "Matematica per l’ Economia e l’ Azienda", Ed. Egea, ISBN - 978-8823822696 - Milano, 2018. Capitoli: 8-9-10
- Bertini C., Gambarelli G., Stach I.,: "STRATEGIE" , G. Giappichelli Editore, ISBN - 9788892104136 - Torino, 2019. Capitoli: 1-2-3-4-6-11
MONOGRAPHIC PART
Iozzi F., "An introduction to Mathematical Models in Management", Bocconi University, Milano, 2015
PART I - Advanced Linear Algebra, Linear and Nonlinear Mathematical Programming
1. Advanced Linear Algebra: metric spaces and Euclidean distance, vector spaces, subspaces, generator systems and bases, linear functions, eigenvalues and eigenvectors, quadratic forms, diagonalization.
2. Linear programming: solution theory, the simplex method, the dual. Models and management applications.
3. Functions of several variables: spherical neighborhood, limits and continuity, partial derivatives, differentiable functions, tangent plane, Hessian matrix.
4. Optimization in Rn: existence of solutions, Weierstrass theorem, first-order condition, second-order condition, models.
5. Constrained optimization with rigid constraints: Lagrange theorem, second-order condition (bounded Hessian), economic applications. Nonlinear programming. Kuhn-Tucker conditions and related problems
PART II - MATHEMATICAL MODELS IN MANAGEMENT (IN ENGLISH)
• Introduction to Advanced Probability
• Decision under Uncertainty: Influence Diagrams, Decision Trees, Utility
Assessment criteria:
30 cum laude: comprehensive, in-depth, and critical knowledge of the topics, excellent language skills, comprehensive and original interpretative skills, full ability to independently apply knowledge to solve the problems proposed;
28 - 30: comprehensive and in-depth knowledge of the topics, excellent language skills, comprehensive and effective interpretative skills, ability to independently apply knowledge to solve the problems proposed;
24 - 27: knowledge of the topics with a good degree of mastery, good language skills, correct and confident interpretative skills, good ability to correctly apply most of the knowledge to solve the problems proposed;
20 - 23: adequate knowledge of the topics but limited mastery of them, satisfactory language skills, correct interpretative skills, more than sufficient ability to independently apply knowledge to solve the problems proposed;
18 - 19: basic knowledge of the main topics, basic knowledge of technical language, sufficient interpretative skills, sufficient ability to apply the basic knowledge acquired;
Insufficient: does not have an acceptable knowledge of the topics covered during the course.
