OUR ACADEMIC DEPARTEMENTS |
Lesson details
 | DATAMINING | ||
2018-2019 | EnIESEG School of Management
(
IÉSEG
)
| ||
Class code : | 1819-IÉSEG-AP1S2-MKT-APD-CE03UE | MARKETING | |
| Level | Year | Period | Language of instruction |
|---|---|---|---|
| - | 1 | S2 | EnEnglish |
| Academic responsibility | S.NASINI |
|---|---|
| Lecturer(s) | - |
- This class exists in these courses :
- IÉSEG > IESEG Degree - Programme Grande École > Semester 2 > 2,00 ECTS
Prerequisites
Thi course has two important prerequisite:
- Student have complete the Data Camp course Introduction to R before the start of the course, and show the obtained certificate to the professor (failing to obtain this certificate before the start of the course will imply a zero grade in the Individual Assignment).
- Students are expected to have completed a basic course in Statistics and/or Data Analysis.
Learning outcomes
1) Understanding the main methodologies for data analysis.
2) Understanding the idea of statistical modeling to assess business decisions.
3) Selecting among datamining techniques to estimate the impact of business strategies.
Course description
This is a course in datamining, whose main content is the statistical modelling and analysis applied to business data. The couse is designed to enable students to correctly assess business strategies, based on the use of statistical methods, supporting decision makers to select the optimal strategy under a variety of market conditions. In particular, the course introduces the students to data selection, inferential statistics, regression analysis and cluster analysis, which allow estimating the impact of business strategies on sales and forecasting the impact of future strategies. Statistical software, such as R, SAS and Excel, are used to study empirical cases.
Class type
Class structure
| Type of course | Numbers of hours | Comments | |
|---|---|---|---|
| Independent work | |||
| E-Learning | 8,00 | ||
| Reference manual 's readings | 8,00 | From the list of recommended reading | |
| Face to face | |||
| Interactive class | 8,00 | ||
| lecture | 16,00 | ||
| Independent study | |||
| Group Project | 8,00 | Students are assigned to small groups | |
| Total student workload | 48,00 | ||
Teaching methods
- Coaching
- E-learning
- Research
Assessment
The students evaluation is based on an individual assignment (the Data Camp certificate) and a group project.
| Type of control | Duration | Number | Percentage break-down |
|---|---|---|---|
| Continuous assessment | |||
| Participation | 0,00 | 0 | 20,00 |
| Others | |||
| Group Project | 8,00 | 1 | 40,00 |
| Individual Project | 8,00 | 1 | 40,00 |
| TOTAL | 100,00 | ||
Recommended reading
- Gerald J. Tellis, Chapter 24, Modeling Marketing Mix, publisher University of Southern California Online available at www-bcf.usc.edu/~tellis/mix.pdf -
- Consumer Theory: http://www.columbia.edu/~md3405/IM_CT.pdf -
Internet resources
- Marketing Mix Lab: Multicollinearity and Ridge Regression
- Marketing Mix Modeling Explained – With R
- Introduction to R
* This information is non-binding and can be subject to change
