APPLIED MATHEMATICS

Degree course: 
Corso di Second cycle degree in GLOBAL ENTREPRENEURSHIP ECONOMICS AND MANAGEMENT
Academyc year when starting the degree: 
2019/2020
Year: 
1
Academyc year when helding the course: 
2019/2020
Course type: 
Compulsory subjects, characteristic of the class
Language: 
English
Credits: 
6
Period: 
Second semester
Standard lectures hours: 
40
Detail of lecture’s hours: 
Lesson (40 hours)
Requirements: 

Basic knowledge of mathematics (linear functions, differential calculus and analytical geometry) and probability will be helpful.

The course "Basic Math" provides some recap on these subjects.

Final Examination: 
Orale

The grade is entirely (100%) awarded by a written exam through an on-line platform (e-learning). The assignment includes both multiple choice questions and open-ended numerical questions, with no specific proportion between them.

The exam aims to certify:
• The ability to identify a model, among those presented in the course, from a simulated real world application.
• The ability to implement the model with the appropriate software.
• The ability to interpret the model’s output.
• The ability to assess the sensitivity of the solutions compared to the input parameters.

Students will take the exam in PC-Labs, using University’s computers.
Exam time is 90 minutes. Total score of the exam is 31. The exam is passed with a score no less than 18. A score of 31 is rewarded by honor grade (30 e lode).

No partial exams will be organized.

Students with learning disability are kindly requested to contact the disability office. Strict observance of University rules are required to access any special option for the exam. Failure to comply will result in standard exam.

Assessment: 
Voto Finale

The aim of the “Applied Mathematics” course is to provide students with a first introduction to the topics of business analytics manageable by decision analysis and optimization tools.
Students should achieve the ability to:
1) Model real world problem;
2) Solve basic formulations of deterministic decision problems;
3) Solve basic formulations of decision making problems under uncertainty;
4) Apprise the solution, performing sensitivity analysis;
5) Read a sensitivity analysis report.

Decision under uncertainty:
• Influence Diagrams;
• Decision Trees;
• Sensitivity Analysis: one-way and two way;
• Tornado diagrams;
• The value of information: perfect information and sample information;
• Risk Preferences: Risk Aversion, The Arrow-Pratt risk aversion measure, Exponential Utility.
Deterministic models for Decision making:
• Linear Programming;
• Examples of management problems and their solution through linear programming;
• Problem Solution with Solver;
• Sensitivity Analysis: Graphical approach;
• Sensitivity Analysis: Reading the SOLVER report;
• Integer linear programming;
• Nonlinear programming: cases and solution with Solver.

Other topics could be added during the course.

Approximately 20 hours, including training sessions are devoted to "Decision under uncertainty". Main topics to be presented are:
• Influence Diagrams;
• Decision Trees;
• Sensitivity Analysis: one-way and two way;
• Tornado diagrams;
• The value of information: perfect information and sample information;
• Risk Preferences: Risk Aversion, The Arrow-Pratt risk aversion measure, Exponential Utility.

Approximately 20 hours, including training sessions are devoted to "Deterministic models for Decision making". Main topics to be presented are:
• Linear Programming;
• Examples of management problems and their solution through linear programming;
• Problem Solution with Solver;
• Sensitivity Analysis: Graphical approach;
• Sensitivity Analysis: Reading the SOLVER report;
• Integer linear programming.

George E. Monahan, Management Decision Making (Spreadsheet Modeling, Analysis, and Application) August 2000, ISBN: 9780521781183

Convenzionale

Teaching and learning activities include face-to-face lectures and practice sessions. The instructor presents management problems modeled through quantitative methods, together with the theoretical background and the solution by dedicated software. Assessment of the solutions obtained is also discussed. In particular, lectures and practice sessions use Excel and its AddIns “Solver” and “TreePlan”. Students are strongly recommended to bring their own PC to classes.
Disclaimer: the course is taught on PC-based machines. The use of Apple machines is possible, but it will be up to students to work out the needed adjustment. Exam requires to be able to use a PC-based machine as those on which practice sessions are taught.

“Businesses that use 'data-driven decision-making' enjoy a 5-6% increase in productivity, Big Data for All: Privacy and User Control in the Age of Analytics, O. Teme/J. Polonetsky, Northwestern Journal of Technology and Intellectual Property 2012”.
The European Parliament issued on 2014 July, 2nd the report “Towards a thriving data-driven economy” to highlight the ongoing revolution in business and economics. Job market demands an increase in quantitative skills, especially for descriptive and predictive data analytics, data processing, simulation, visualization, decision support and the integration of results into new products. Through the two parts of the course, we address this need.