Insegnamento a.a. 2021-2022

20486 - FONDAMENTI DI BUSINESS ANALYTICS / PRINCIPLES OF BUSINESS ANALYTICS

Dipartimento di Scienze delle Decisioni / Department of Decision Sciences


Per la lingua del corso verificare le informazioni sulle classi/
For the instruction language of the course see class group/s below
Vai alle classi / Go to class group/s: 1 - 2 - 3 - 4 - 5 - 6 - 7 - 99
M (6 cfu - I sem. - OB  |  3 cfu SECS-S/01  |  3 cfu SECS-S/06)
Docente responsabile dell'insegnamento / Course Director:
EMANUELE BORGONOVO

Classi: 1 (I sem.) - 2 (I sem.) - 3 (I sem.)
Docenti responsabili delle classi:
Classe 1: GABRIELE GURIOLI, Classe 2: MICHELE IMPEDOVO, Classe 3: MICHELE IMPEDOVO

Classe/i impartita/e in lingua italiana

Mission e Programma sintetico

MISSION

In questi anni stiamo assistendo alla rivoluzione della data driven economy. L’aumento della connettività, l’aumento esponenziale dei dati generati da utenti privati e aziende sta cambiando il modo di pensare alle attività economiche. La stessa Comunità Europea, in una comunicazione al Parlamento Europeo già il 2 luglio 2014 comunica la necessità di formare una generazione di managers che sappia utilizzare in modo naturale le informazioni derivanti dai dati e dai modelli quantitativi a supporto delle decisioni. Tali metodi vengono comunemente chiamati metodi di business analytics. Lo scopo del corso è quindi quello di fornire agli studenti una prima introduzione ai temi della business analytics ed è diviso in due parti. Nella prima parte si analizzano metodi di prescriptive analytics, con lo scopo di avvicinare gli studenti all’uso di modelli e alla traduzione di problemi di business in termini di un corrispondente modello matematico. Nella seconda parte, si trattano metodi di descriptive analytics, che consentono agli studenti di ottenere le informazioni contenute in dataset e rilevanti per decisioni di business.

PROGRAMMA SINTETICO

  • Decision analysis: diagrammi di influenza e alberi decisionali.
  • Valore dell'informazione: EVSI e EVPI.
  • Programmazione lineare.
  • Modelli predittivi per risposta numerica: regressione lineare.
  • Diagnostiche del modello di regressione lineare (multicollinearità, eteroschedasticità, analisi dei residui).
  • Modelli predittivi per risposta categorica: regressione logistica.

Risultati di Apprendimento Attesi (RAA)

CONOSCENZA E COMPRENSIONE

Al termine dell'insegnamento, lo studente sarà in grado di...
  • Riconoscere modelli appropriati per la soluzione di problemi di business e di gestione.
  • Identificare la corretta metodologia per la soluzione di problemi di business e di gestione.
  • Distinguere tra modelli deterministici e non deterministici.

CAPACITA' DI APPLICARE CONOSCENZA E COMPRENSIONE

Al termine dell'insegnamento, lo studente sarà in grado di...
  • Organizzare le informazioni per costruire un modello quantitativo coerente con le ipotesi poste.
  • Tradurre un problema di decisione in un corrispondente modello quantitativo.
  • Utilizzare i software Excel (Solver), TreePlan, R al fine di determinare le soluzioni del problema.
  • Interpretare le soluzioni derivate dall'implementazione del modello prescelto al fine di definire le decisioni ottimali.
  • Analizzare i modelli con strumenti di analisi di sensibilità per ottenere "managerial insights".

Modalità didattiche

  • Lezioni frontali
  • Lezioni online
  • Esercitazioni (esercizi, banche dati, software etc.)

DETTAGLI

L'attività di insegnamento-apprendimento di questo corso si articola in lezioni frontali in cui vengono esposti problemi manageriali e vengono proposti e discussi modelli di soluzione mediante metodi quantitativi. Lo studente viene guidato:

  • Alla identificazione del modello quantitativo, di cui vengono illustrati principi e proprietà.
  • All'implementazione tramite software dedicato.
  • Alla soluzione del problema.
  • All'interpretazione della soluzione.
  • All'analisi della variabilità delle soluzioni in funzione dei parametri in input.

Vengono in particolare utilizzati in aula EXCEL (Solver), TreePlan, R. Sono previste due esercitazioni in aula durante le quali gli studenti svolgono con il loro portatile attività sia individuale sia di gruppo finalizzate al percorso descritto (identificazione del modello, implementazione dei dati, soluzione e analisi di sensibilità). Tali esercitazioni servono come autovalutazione dell’apprendimento degli aspetti indicati. 


Metodi di valutazione dell'apprendimento

  Accertamento in itinere Prove parziali Prova generale
  • Prova individuale scritta (tradizionale/online)
     x
  • Assignment individuale (relazione, esercizio, dimostrazione, progetto etc.)
 x    

STUDENTI FREQUENTANTI E NON FREQUENTANTI

La valutazione, identica sia per studenti frequentanti sia per studenti non frequentanti, è affidata interamente (100% del voto) a un assessment su piattaforma online organizzato per problemi e mediante analisi dei dati, articolato in domande numeriche a risposta aperta e domande a risposta multipla. La prova mira a verificare:

  • La capacità di identificare un modello in modo coerente con le ipotesi e con i dati assegnati.
  • La capacità di implementare il modello con il software opportuno.
  • La capacità di interpretare l'output del software.
  • La capacità di valutare la sensibilità delle soluzioni rispetto ai parametri in input. 

Materiali didattici


STUDENTI FREQUENTANTI E NON FREQUENTANTI

  • G.E. MONAHAN, Management Decision Making, Cambridge University Press, 2000.
  • F.S. HILLIER and G.J. LIEBERMAN, Introduction to Operations Research, 2001, Second Edition.
  • F. IOZZI, Un'introduzione ai modelli matematici nel management, 2015 (disponibile in pdf sull'e-learning del corso).
  • 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
  • Note distribuite dai docenti.
Modificato il 22/08/2021 21:36
IM (6 cfu - I sem. - OB  |  3 cfu SECS-S/01  |  3 cfu SECS-S/06)
Docente responsabile dell'insegnamento / Course Director:
EMANUELE BORGONOVO

Classes: 6 (I sem.) - 7 (I sem.) - 99 (I sem.)
Instructors:
Class 6: EMANUELE BORGONOVO, Class 7: MAURO D'AMICO, Class 99: TO BE DEFINED

Class group/s taught in English

Mission & Content Summary

MISSION

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.

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

  • Face-to-face lectures
  • Online lectures
  • Exercises (exercises, database, software etc.)

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
  • Written individual exam (traditional/online)
     x
  • Individual assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

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.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • 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.
Last change 22/08/2021 21:43
M (6 cfu - I sem. - OB  |  3 cfu SECS-S/01  |  3 cfu SECS-S/06)
Docente responsabile dell'insegnamento / Course Director:
EMANUELE BORGONOVO

Classes: 4 (I sem.) - 5 (I sem.)
Instructors:
Class 4: MATTEO ROCCA, Class 5: GIOVANNI CRESPI

Class group/s taught in English

Mission & Content Summary

MISSION

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.

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

  • Face-to-face lectures
  • Online lectures
  • Exercises (exercises, database, software etc.)

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
  • Written individual exam (traditional/online)
     x
  • Individual assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

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.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • G.E MONAHAN, 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.
     
Last change 22/08/2021 21:45