20486 - FONDAMENTI DI BUSINESS ANALYTICS / PRINCIPLES OF BUSINESS ANALYTICS
For the instruction language of the course see class group/s below
Class group/s taught in English
In recent years, we have been witnessing the revolution of the data-driven economy. Through increased connectivity and digitalization, private users and companies are generating an unprecedented amount of data which is changing the way we think about the economy. In a communication to the European Parliament on 2 July 2014, the European Community communicated the need of training a generation of managers who know how to naturally use information derived from data and quantitative models to support decisions. These methods are commonly called methods of business analytics. The aim of the course is to provide students with a first introduction to the topics of business analytics and is divided into two parts. In the first part, methods of prescriptive analytics are analyzed, with the aim of allowing students to approach the use of models and translating business problems in terms of a corresponding mathematical model. In the second part, descriptive analytics methods are discussed, which allow students to extract the information contained in datasets that is significant for business decisions.
- Decision analysis: influence diagrams and decision trees.
- Value of information: EVSI and EVPI.
- Linear programming.
- Predictive models for a continuous response: linear regression.
- Diagnostics of the linear regression model (multicollinearity, heteroscedasticity, residual analysis).
- Predictive models for a categorical response: logistic regression.
- Recognize appropriate models to solve business and management problems.
- Identify the correct methodology for solving business and management problems.
- Discern between deterministic and non-deterministic models.
- Organize information to build a quantitative model in line with the input posed.
- Translate a decision problem into a corresponding quantitative model.
- Use the software Excel (Solver), TreePlan, R in order to determine solutions to a problem.
- Interpret solutions derived from implementing the chosen model in order to make optimal decisions.
- Analyze models with sensitivity analysis tools to obtain "managerial insights".
- Face-to-face lectures
- Online lectures
- Exercises (exercises, database, software etc.)
Teaching and learning activities for this course are divided into face-to-face lectures during which management problems are explained and solution models through quantitative methods are proposed and discussed. Students are assisted in:
- Identifying the quantitative model, whose principles and properties are described.
- Implementation through dedicated software.
- The solution to the problem.
- Interpreting the solution.
- Analysis of the variability of solutions on the basis of input parameters.
In particular, Excel (Solver), TreePlan and R are used in the classroom. Two in-class exercise sessions are held during which students complete both individual and group activities with their laptops, aimed at the described procedure (identifying a model, implementing data, solutions and sensitivity analysis). These exercises are used as self-assessment of learning of the aspects indicated.
|Continuous assessment||Partial exams||General exam|
Assessment, both for attending and non-attending students, is based entirely (100% of the grade) on an assessment on an online platform with problems to solve and through data analysis, divided into open-ended numerical questions and multiple-choice questions. The exam aims to verify:
- The ability to identify a model in line with the hypothesys theories and data assigned.
- The ability to implement the model with the appropriate software.
- The ability to interpret the software’s output.
- The ability to assess the sensitivity of the solutions compared to the input parameters.
- G.E. MOMAHAN, Management Decision Making, Cambridge University Press, 2000.
- F. IOZZI, Un'introduzione ai modelli matematici nel management, 2015 (disponibile in pdf sull'e-learning del corso).
- F.S. HILLIER and G.J. LIEBERMAN, Introduction to Operations Research, 2001, Second Edition.
- D.J. CAMM, J.J. COCHRAN, M.J. FRY, et al., Essentials of Business Analytics, Cengage, 2015.
- J. FOX, Using the R Commander: A Point-and-Click Interface for R, Chapman and Hall CRC, 2016.
- Notes provided by the teachers.