ANALYSIS OF MARKETING DATA
cod. 1001407

Academic year 2021/22
3° year of course - First semester
Professor
- Marco RIANI
Academic discipline
Statistica (SECS-S/01)
Field
Statistico-matematico
Type of training activity
Characterising
56 hours
of face-to-face activities
8 credits
hub: PARMA
course unit
in ITALIAN

Learning objectives

The purpose is to deal in a quantitative way the relevant information for
the firm. The data can come from different sources (internal or sample
surveys). The final goal is to provide a rational support for decision
making.

Knowledge and understanding
At the end of the course, the students will have acquired skills on the
following topics:
- Basic statistical analysis;
- Multivariate statistical analysis;
- Dimension reduction techniques;
- Classification using supervised and unsupervised methods.
Skills and understanding skills applied
At the end of the course the student will be able to:
- understand the different phases which are at the root of statistical analysis of data
- translate the conceptual tools into empirical rules for the management of data coming from different sources and in different formats;
- plan and manage a statistical survey and understand what are the advantages and disadvantages of the different techniques of data collection
- develop distinctive skills in the area of statistical data analysis.
Independence of judgment
At the end of the course the student will be able to:
- evaluate the best statistical techniques to use;
- identify the best practices in managing data coming from different sources;
- evaluate the effectiveness of the different statistical techniques.
Communication skills
Through lectures managed in an interactive way, company testimonials
and group work, the student will be able to:
- clearly communicate, in a concise, timely and coherent manner, to
different interlocutors (both academic and business), information and
concepts (including complex ones) related to statistical data analysis;
- communicate effectively using an appropriate technical language;
Learning skills
The course aims to transfer the ability to translate the statistical principles into empirical rules of decision. The main topics are detailed
through the presentation of successful data analysis case studies. At the end of the course the students
will have gained the ability to expand and update the level and range of
the knowledge acquired from lessons and course textbooks.

Prerequisites

Basic knowledge of mathematics and statistics

Course unit content

Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to MATLAB.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis.

Full programme

Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to MATLAB.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis and correspondence analysis

Bibliography

S. ZANI – A. CERIOLI, Analisi dei dati e Data Mining per le decisioni
aziendali, Giuffrè Editore, Milano, 2007

Teaching methods

Frontal lessons also with PC.


Teaching materials (course slides and data for replicating analyses) are provided through http://www.riani.it/ADM .
Further materials (data for individual practical work and research readings) are also provided through http://www.riani.it/ADM , as well as details on the course timetable, and links to the youtube channel of the course.

Assessment methods and criteria

Computer exam
The knowledge and understanding achieved
will be assessed with n. 3 open-ended questions, each worth 10 points.
The duration of the exam is 75 minutes. The final written test is evaluated on a 0-30 scale

Other information

Additional information can be found from the web site http://www.riani.it