Course 2024-2025 a.y.

20946 - PRINCIPLES OF BUSINESS ANALYTICS

Department of Decision Sciences

Course taught in English

Student consultation hours
Class timetable
Exam timetable
Go to class group/s: 1 - 2 - 3 - 4 - 5 - 6
M (6 credits - I sem. - OB  |  3 credits SECS-S/01  |  3 credits SECS-S/06) - IM (6 credits - I sem. - OB  |  3 credits SECS-S/01  |  3 credits SECS-S/06)
Course Director:
EMANUELE BORGONOVO

Classes: 1 (I sem.) - 2 (I sem.) - 3 (I sem.) - 4 (I sem.) - 5 (I sem.) - 6 (I sem.)
Instructors:
Class 1: SERGIO VENTURINI, Class 2: ELENA POLI, Class 3: MATTIA VITTORIO ORESTE COZZI, Class 4: ALESSANDRO RECLA, Class 5: MAURIZIO POLI, Class 6: EMANUELE BORGONOVO


Mission & Content Summary

MISSION

We live in the era of the data-driven economy. Through increased connectivity and digitalization private users and companies are generating an unprecedented amount of data which is changing how we think about the economy. In a communication to the European Parliament on 2 July 2014, the European Community communicated the need to train 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 course aims to give students a first introduction to business analytics and is divided into two parts. In the first part, participants are exposed to prescriptive analytics methods, aiming to allow them to approach the use of models and translate business problems into mathematical models. In the second part, descriptive analytics methods are discussed, which allow students to extract information data to make better-informed business decisions.

CONTENT SUMMARY

  • 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.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • 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.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • 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".

Teaching methods

  • Lectures
  • Practical Exercises

DETAILS

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.  


Assessment methods

  Continuous assessment Partial exams General exam
  • Oral individual exam
     x
  • Written individual exam (traditional/online)
     x
  • Individual Works/ Assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

Assessment, both for attending and non-attending students, is based on two assignments and a final 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.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • E. Borgonovo, D. Fein and E. Poli: "Principles of Business Analytics", EGEA, 2024.
  • G.E. MONAHAN, Management Decision Making, Cambridge University Press, 2000.
  • F.S. HILLIER and G.J. LIEBERMAN, Introduction to Operations Research, 2001, Second Edition.
  • J. FOX, Using the R Commander: A Point-and-Click Interface for R, Chapman and Hall CRC, 2016.
  • Notes provided by the teachers.
Last change 24/05/2024 21:58