69499 - BUSINESS INTELLIGENCE M

Course Unit Page

Academic Year 2017/2018

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

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

  1. Business intelligence:
  • the role of BI in the corporate information system;
  • introduction to data warehousing;
  • introduction to data mining and KDD;
  • what-if analysis.
  • Data Warehousing:
    • architectures;
    • techniques for data analysis;
    • lifecycle:
      • data source analysis;
      • requirement analysis;
      • conceptual design;
      • workload and data volume;
      • logical design;
      • design of loading procedures.
  • Data Mining:
    • associative rules;
    • clustering algorithms;
    • typologies of data;
    • decision trees;
    • statistical methods;
    • neural networks;
    • evaluation of the results;
    • analysis of time series.

     

    Readings/Bibliography

    • Slides.
    • M. Golfarelli, Stefano Rizzi. Data Warehouse Design: Modern principles and methodologies. McGraw-Hill, 2009.
    • R. Roiger, M. Geatz,  Data Mining: A Tutorial-Based Primer, McGraw-Hill, 2003.
    Recommended readings:
    • M. Berry, G. Linoff. Data mining techniques for marketing, sales, and customer support. John Wiley & Sons, 1997.
    • 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.
    • I.H. Witten, E.Frank, M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011.

    Teaching methods

    • Classroom lectures and exercises are given with the help of slides (through overhead projector or PC+projector).
    • The program will be integrated by seminars from enterprise consultants.

    Assessment methods

    The final exam aims at verifying the knowledge acquired by the student of the main available tools for the analysis of very large quantities of data to be used for efficiently supporting the decision processes.

    For both modules (Data Warehousing and Data Mining), the final exam is made of a written test, containing theory questions aimed at evaluating the student's knowledge of the whole course program and practical exercises on the design aspects.

    The written test concerning each module, with a 1-hour duration to be done without the aid of books or written notes, is passed if it receives a 18/30 score on a total score of 33/30. In each scheduled exam, it is possible to take the exam concerning both modules.

    The written test of the two modules can be taken in different scheduled exams. The final exam grade is the average of the scores obtained for the two 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

    Downloadable informal notes on the course topics are available. 

    Links to further information

    http://www-db.disi.unibo.it/~fgrandi/

    Office hours

    See the website of Fabio Grandi

    See the website of Stefano Rizzi