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ADVANCED DATA ANALYSIS

2016-2017

IESEG School of Management ( IÉSEG )

Class code :

1617-IÉSEG-BA3S2-QMS-B3-CE08UE

QUANTITATIVE METHODS


Level Year Period Language of instruction 
Bachelor3S2English
Academic responsibilityT.TESSITORE , T.TESSITORE
Lecturer(s)Catherine JANSSEN, Tina TESSITORE, M.Verschelde


Prerequisites

Students should be knowledgeable about basic concepts in statistics. Some knowledge of Marketing Research is also recommended.

Learning outcomes

At the end of the course, the student should be able to :
1. Have a deeper understanding of the different data analysis techniques available;
2. Understand the use of these different data analysis techniques for marketing-oriented research and business problems;
3. Identify the relevant statistical test(s) to perform;
4. Apply the different data analysis techniques and interpret the results of statistical outputs;
5. Know how to use a data analysis software such as SPSS.

Course description

The course of Advanced Data Analysis focuses on different data analysis techniques, that will be applied in a marketing context. Students will learn when and how to use these different techniques, as well as how to report and present results of statistical analyses in a professional manner.
To get acquainted to this, students will perform several exercices in class using the data analysis software SPSS (in-class assignments), and solve a challenging business case in groups based on real-life data (group project). The course focuses on the application of data analysis techniques for real business purposes, and more specifically, marketing-oriented ones.
The course will cover the following topics: Introduction to the SPSS environment (data preparation, dealing with missing data, exploring data with graphs…), hypothesis testing, descriptive analysis, statistical tests (Chi-square, T-Test, ANOVA…).


Class type

Class structure

Type of courseNumbers of hoursComments
Face to face
Interactive class8,00   The 16 course hours will be used for both interactive sessions (theory and examples) and in-class exercices, during which active participation from students is expected.
Tutorials8,00   In-class exercises, which consist of the (supervised) application of the theory to research-oriented examples and preparation for the group project.
Independent study
Group Project16,00   Group project, that will be the object of a presentation and preparation of a written report
Estimated personal workload5,00  
Total student workload37,00  

Teaching methods

  • Tutorial
  • Presentation
  • Project work
  • Interactive class


Assessment

Students will be evaluated based on:
- Class participation
- In-class exercises done during each course session. In these exercices, students will have to apply the data exploration and analysis techniques covered in class.
- Group project: students will execute the analysis of real business data. Deliverables include a written management report and a group presentation
- Final exam

Type of controlDurationNumberPercentage break-down
Continuous assessment
Participation16,00125,00
Others
Individual Project0,00150,00
Final Exam
Written exam2,00125,00
TOTAL     100,00

Recommended reading

  • Recommanded book: Andy Field (2013), "Discovering statistics using IBM SPSS Statistics", Sage. -


Internet resources

  • IESEG Online

    The couse website will be used to make the slides of the course, class exercices, and materials for the group project available to students.



 
* This information is non-binding and can be subject to change
 
 
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