Instructional goals
Providing the students with the main multivariate statistical tools for performing effective marketing research
Intended learning outcomes
Students are expected to master the main statistical methods for marketing research, i.e. to the able to plan a research, define the questionnaire, select the best sampling scheme, collect, pre-preprocess and analyze the data using the proper techniques and present the main results
Prerequisites
Students are expected to be familiar with the topics addressed in any Introductory Statistics class
Course Contents
1. Statistical Learning and Machine Learning
2. Introduction to R programming. RStudio
3. Statistical methods and marketing research. Market segmentation, positioning and statistical methods for data analysis.
4. Databases: basic concepts
5. Cluster analysis: Non-hierarchical and Hierarchical clustering. Tandem Analysis
6. Classification Algorithms
7. Regression. Multiple Linear Regression
8. Principal Component Analysis
9. Market Basket Analysis
10. Geo-Marketing applications
Reference Books
An Introduction to Statistical Learning: with Applications in R, G. James et al., Second edition, Springer
Teaching Methods
Classroom teaching, R lab, discussion of case studies
Assessment Method
In order to follow the continuous assessment model, there will be multiple intermediate tests. The assessment method will also include the drafting of a project work.
Students who successfully pass the intermediate tests and the project work will be allowed to take the final exam and will be tested on their project work.
Students who do not pass the intermediate tests and / or the project work will be tested on the whole programme.
Thesis assignment criteria
The degree thesis involves the application of statistical methodologies in business.
Week 1 Contenuto sessioni on line e on campus
Course introduction. Machine Learning. Working tools. R: Introduction to R. Practical introduction, how to install and configure the tools that will be used during the course such as R Studio. Programming language concepts. Description of generic programming language concepts as they are implemented in a high-level statistical language.
Week 2 Contenuto sessioni on line e on campus
Data types and data structures. Understand vectors, matrixes, dataframes and lists in R. Data import and export, data cleaning and manipulation. Data visualization. Graphical representation of information and Data Graphics for multidimensional Data and application in R
Week 3 Contenuto sessioni on line e on campus
- Statistical methods and marketing research: introduction.
- Statistics and marketing research: market segmentation, positioning and statistical methods for data analysis.
- Project work Kick-off
Week 4 Contenuto sessioni on line e on campus
Structured data and Databases. Introduction and basic concepts. Laboratory in R
Week 5 Contenuto sessioni on line e on campus
Segmentation and Hierarchical cluster analysis. Marketing applications in R and discussion of case studies
Week 6 Contenuto sessioni on line e on campus
Non-Hierarchical cluster analysis. K-means algorithm. Marketing applications in R and discussion of case studies
Week 7 Contenuto sessioni on line e on campus
ML supervised algorithms. Classification problem and predition. Marketing application in R and case studies discussion
Week 8 Contenuto sessioni on line e on campus
Regression: assumptions of Multiple
Linear Regression. Regression Tests. Variable selection strategies(Forward/backward/stepwise). Laboratory in R.
Week 9 Contenuto sessioni on line e on campus
Market Basket Analysis, basic concepts and R application.
Principal Component Analysis (PCA), theory and application in R
Week 10 Contenuto sessioni on line e on campus
Deviance decomposition.
Geo Marketing, Basic concepts and marketing applications.
Week 11 Contenuto sessioni on line e on campus
Geo-marketing case studies and R laboratory.
Data Enrichment from geographical information
Week 12 Contenuto sessioni on line e on campus
CRISP Approach to data mining process.
Testimonial TBD.
Project work check-point.