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| GEST3034-1 | Multivariate Analysis for Marketing
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| Duration : | 30h Th |
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| Number of credits : |
| Master in Management Sciences, in-depth approach, 1st year | | Second semester | | 5 |
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| Master in Management Sciences, didactic approach, 1st year | | Second semester | | 5 |
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| Master en sciences de gestion, à finalité spécialisée en banking and asset management, 1st year | | Second semester | | 5 |
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| Master in Management Sciences, professional Focus in Entrepreneurship, 1st year | | Second semester | | 5 |
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| Master en sciences de gestion, à finalité spécialisée en financial analysis and audit, 1st year | | Second semester | | 5 |
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| Master in Management Sciences, professional Focus, 1st year | | Second semester | | 5 |
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| Master en sciences de gestion, à finalité spécialisée en management humain et organisation, 1st year | | Second semester | | 5 |
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| Master in management Sciences, professional focus in management, 1st year | | Second semester | | 5 |
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| Master en sciences de gestion, à finalité spécialisée en marketing and strategic intelligence, 1st year | | Second semester | | 5 |
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| Master en sciences de gestion, à finalité spécialisée en management des entreprises sociales, 1st year | | Second semester | | 5 |
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| Master degree in management Sciences, professional focus on Strategic Intelligence and Marketing, 1st year | | Second semester | | 5 |
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| Lecturer : | Gentiane Haesbroeck, Michael Schyns |
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| Coordinator : | Michael Schyns |
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Language(s) of instruction :
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| French language |
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Course contents :
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| The course is divided into two parts:.
The first part presents the main technics of multivariate statistics that are useful in management and the second one considers advanced tools for data exploration (datamining).
The first part starts with the presentation of some descriptive techniques and then goes on with the main dimension reduction methods (PCA, canonical analysis).
Specific methods used in Marketing will be presented. The courses ends with the consideration of different classification techniques (discriminant analysis, logistic regression,...).
The second part is about dataming. When is it usefull? What kind of analyses can we perform? What are the most usual methods? Emphasis will be put on decision trees. |
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Learning outcomes of the course :
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| At the end of the course, the students are expected to be able to
- find out which method would be most appropriate for analysing a multivariate data set.
- use the statistical software illustrated at the practicals
- interpret the results of the analyses. |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| Descriptive statistics, probability theory and inferential statistics. |
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Planned learning activities and teaching methods :
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| After each theory lecture, practicals in a computer room will be organised. The students will be asked to analyse real data sets using a given statistical software. |
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Mode of delivery (face-to-face ; distance-learning) :
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| At the end of the second part, the students will be given homeworks in order to get familiar with the statistical software as well as the performed analyses. |
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Recommended or required readings :
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| Slides used during the lectures will be used as available lecture notes (available on the web campus Lol@).
Some chapters of the book "Introduction to Data Mining", written by Tan, Steinbach et Kumar (Pearson Edition) will be covered during the second part of the course. |
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Assessment methods and criteria :
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| The final mark will be a weigthed mean based on the following two results:
1) Assessments of the homeworks.
2) Written exam with the help of the statistical software. |
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Contacts :
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| M. SCHYNS, HEC-ULg, Building N1,
tel: 04/366.31.91
email: M.Schyns@ulg.ac.be
G.HAESBROECK, Institute of mathematics, Building B37, room 0/60, tel: 04/366-95-94, email: G.Haesbroeck@ulg.ac.be |
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