79210 - Lab Class on Economic and Business Data

Course Unit Page

  • Teacher Ida D'Attoma

  • Credits 6

  • Teaching Mode Traditional lectures

  • Language Italian

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Statistics, Economics and Business (cod. 8876)

  • Teaching resources on Virtuale


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Decent work and economic growth Reduced inequalities

Academic Year 2021/2022

Learning outcomes

The course has the goal to provide students with the instruments to analyse case studies in economic and market analysis fields from an applied perspective. At the end of the course the student will be able to quantitatively analyse business and economic data, to select the best suited statistical methodology for the problem at hand, to implement the select methodology using the SAS software, to critically interpret empirical results.

Course contents

  • Primary and secondary data on consumer research.
  • Data quality and pre-processing.
  • “Big data”:a new data quality framework.
  • Data visualization and data description.
  • Transactions data and the RFM metric.
  • The behavioral segmentation: case studies.
  • The use of qualitative data for customer profiling: the correspondence analysis.


  • Tufféry, S. (2011) Data Mining and Statistics for Decision Making. John Wiley & Sons, Ltd. (Cap. 3, 4, 7.3, 7.4, 9).  . Note. It is available also the ebook version.
  • Bolasco, S. (Ed. 1999) “Analisi Multidimensionale dei dati”, Carocci (cap. 5, 6.4)
  • Mazzocchi, M. (2008). Statistics for marketing and consumer research. Sage (Cap. 1-4).(Reccomended). Note. It is available also the ebook version.
  • Lecture notes.

Teaching methods

  • Lectures involve the presentation of theoretical and practical issues. After each theoretical session a practical tutorial in the laboratory is devoted to applications on real case studies using the SAS software.
  • Students are invited to solve and discuss case studies. Home assignments will serve to reinforce class concepts and get familiarity with data analysis and interpretation in a business setting. Home assignments will be ungraded. However, solutions (or simply a feedback) will be provided for self-assessment.

Assessment methods

Students have to pass a written qualifying exam.

The written exam has the following objectives:

  • to verify the theoretical knowledge of methods presented during the lessons
  • to verify the ability of applying methods to empirical case studies through the help of SAS software
  • to verify the ability to interpret obtained results

Attending and non attending students will have a written examination consisting in 2 open questions on theory (1/3 of grade) and a practical section requiring the production and/or the interpretation of statistical outputs (2/3 of grade). The open questions aim at testing the student's knowledge of the theoretical topics. The practical section is targeted at testing the ability of producing and interpreting statistical outputs, and their translation into applied conclusions. Typical exam questions will be made available during the course. All the students are given to perform tasks of the same difficulty in the same time. It is a 75 minutes written exam with 2 open questions on theory and 2/3 practical exercises using the SAS software.  The exam is "closed-book". Students are not allowed to consult references and theoretical information sources while performing the task.

Points awarded for correct answers to each question will be available in the exam outline. The maximum score awarded for all correct answers is 5.

The assessment of the  final qualifying exam will be based on the following grid:

<3.5 failed

>=3.5 pass

The evaluation in AlmaEsami will be only pass/fail.

Check the virtual space for further details.

Teaching tools

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students. The teaching material includes:

  • Lecture notes summarising theoretical topics explained in class
  • Open data and lecture notes to follow the practical sessions
  • Miscellanea: exercises, solutions to assignments, sample exams, follow-up materials

Software SAS 9.4 and SAS on Demand for Academics.

Office hours

See the website of Ida D'Attoma