96143 - METODI E MODELLI DI DATA ANALYTICS M

Academic Year 2021/2022

  • Docente: Fabio Grandi
  • Credits: 6
  • SSD: ING-INF/05
  • Language: Italian
  • Moduli: Fabio Grandi (Modulo 1) Andrea Borghesi (Modulo 2)
  • Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Engineering Management (cod. 0936)

    Also valid for Second cycle degree programme (LM) in Engineering Management (cod. 0936)

Learning outcomes

Knowledge of the main tools for the analysis of very large quantities of data, aimed at efficiently supporting the decision processes. The course deals with the study of the two mainstream technologies avaliable for the extraction of strategic information:Data Warehousing and Data Mining.

Course contents

Owing to the sharing of learning modules effective in the 2021/2022 A.Y., this Web Guide covers the following two courses, for which the composition in modules is shown:

 

69499 - BUSINESS INTELLIGENCE M

  1. Data Warehousing (Stefano Rizzi)
  2. Data Mining (Fabio Grandi)

96143 - DATA ANALYTICS METHODS AND MODELS M

  1. Data Mining (Fabio Grandi)
  2. Artificial Intelligence Methods (Andrea Borghesi)

 

In the following, the Contents of the component Modules are described 

_____________________________________________________

 

DATA WAREHOUSING

 

Requirements/Prior Knowledge

A prior knowledge and understanding of database systems and relational model is required to attend with profit this course. These notions are normally achieved by giving an exam of Databases or Information Systems.

Fluent spoken and written Italian is a necessary pre-requisite: all lectures and tutorials, and all study material will be in Italian.

 

Course Contents

  • the role of BI in the corporate information system;
  • the BI pyramid
  • introduction to data warehousing 
  • architectures
  • techniques for data analysis: reporting and OLAP
  • lifecycle
  • data source analysis
  • requirement analysis
  • conceptual design
  • workload and data volume
  • logical design
  • design of loading procedures

 

_____________________________________________________

 

DATA MINING

 

Requirements/Prior Knowledge

A prior knowledge and understanding of database systems and relational model is required to attend with profit this course. These notions are normally achieved by giving an exam of Databases or Information Systems.

Fluent spoken and written Italian is a necessary pre-requisite: all lectures and tutorials, and all study material will be in Italian.

 

Course Contents

  • introduction to data mining
  • associative rules
  • clustering algorithms
  • typologies of data
  • decision trees
  • statistical methods
  • neural networks
  • evaluation of the results
  • analysis of time series
  • outlier detection

_____________________________________________________

 

ARTIFICIAL INTELLIGENCE METHODS

 

Requirements/Prior Knowledge

A base knowledge of optimization and linear and integer programming is required to attend with profit this course.
These notions are normally achieved by giving an exam of Operations Research or Algorithms for Decision and Resource Management Support.

Fluent spoken and written Italian is a necessary pre-requisite: all lectures and tutorials, and all study material will be in Italian.

 

Course Contents

  • constraint programming
  • deep learning
  • automatic selection and configuration of algorithms
  • predictive maintenance
  • outlier detection
  • time series
  • prescriptive maintenance
  • use cases

 

Readings/Bibliography


DATA WAREHOUSING

  • Course slides.
  • M. Golfarelli, S. Rizzi. Data Warehouse Design: Modern principles and methodologies. McGraw-Hill, 2009.

Recommended readings:

  • B. Devlin. Data warehouse: from architecture to implementation. Addison-Wesley Longman, 1997.
    W.H. Inmon. Building the data warehouse. John Wiley & Sons, 1996.
  • M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis. Fundamentals of data warehouse. Springer, 2000.
  • R. Kimball, L. Reeves, M. Ross, W. Thornthwaite. The data warehouse lifecycle toolkit. John Wiley & Sons, 1998.



DATA MINING

  • Course slides.
  • I.H. Witten, E.Frank, M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques (third edition), Morgan Kaufmann, 2011.

Recommended readings:

  • M. Berry, G. Linoff. Data mining techniques for marketing, sales, and customer support. John Wiley & Sons, 1997.
  • R. Roiger, M. Geatz, Data Mining: A Tutorial-Based Primer, McGraw-Hill, 2003.



ARTIFICIAL INTELLIGENCE METHODS

  • Course slides.
  • I. Goodfellow, Y. Bengio, A. Courville, Deep Learning book, MIT Press, 2016. https://www.deeplearningbook.org/ 
  • T. Rossi, P. van Beek, T. Walsh, Handbook of Constraint Programming, Elsevier, 2006.

Teaching methods

  • Classroom lectures and exercises are given with the help of slides (through PC+projector presentations). Lectures could be possibly delivered in online or mixed modality using the Teams platform.
  • The program will be integrated by seminars from enterprise consultants.
  • The Data Warehousing module provides for the execution of group exercises on virtual collaborative blackboards.

Assessment methods

The final exam aims at verifying the knowledge acquired by the student, with regard to the specific contents of each teaching module, of the main available tools for the analysis of very large quantities of data to be used for efficiently supporting the decision processes. Exames will be conducted in the presence or online, according to the health situation and provisions of the University. In the online case, the Zoom and EOL tools will be employed.

For all modules, final examinations are carried out separately. Each module examination is made of a 1-hour written test to be done without the aid of books or written notes. For the Data Warehousing module, the test is composed of a practical part, invoving the solution of data warehouse conceptual and logical design exercises, and of a theoretical part, including questions on the whole course program. For the Data Mining module, the test consists in two (out of three) open-answer questions concerning the main course topics. For the Artificial Intelligence Methods module, the test consists in two (out of three) open-answer questions concerning the main course topics. Each module test is passed if it receives a 18/30 score on a total score of 32/30. Further details will be communicated during the lectures and in the "notes" appearing in the exam sessions published on AlmaEsami.

In order to take an exam, registration through AlmaEsami is required within the assigned deadlines. Students who won't be able to register within the deadline are bound to promptly (and anyway before the official closing of the registration lists) notify the issue to the didactic secretariat. It will be faculty of the teacher to admit them to the test. The tests of the two component modules of a course can be taken in the same or in different scheduled sessions, in any order. Once the outcome of a test has been published, each student is given a week to communicate via email to the teacher his/her intention to refuse the mark obtained. The final exam grade of a course is the average of the scores obtained for the two component modules.

To obtain a passing grade, students are required to at least demonstrate a knowledge of the key concepts of the subject, acquired autonomous design skills, and a comprehensible use of technical language. Higher grades will be awarded to students who demonstrate an organic understanding of the subject and a clear and concise presentation of the contents, a high ability for problem solving, and consistent design capabilities. A failing grade will be awarded if the student shows knowledge gaps in key-concepts of the subject, inappropriate use of language, logic failures in the analysis of the subject, inadequate operational and design skills.

Teaching tools

  • Materials on the course topics are available on the Virtual platform.
  • Teams platform for remote teaching.
  • Miro platform for exercises.

 

Office hours

See the website of Fabio Grandi

See the website of Andrea Borghesi