Instructional goals
Providing the students with the main multivariate statistical tools for performing effective marketing data analyses
Prerequisites
Students are expected to be familiar with the topics addressed in any Introductory Statistics class
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 (supervised and unsupervised machine learning models) and present the main results
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. Spatial Statistics and Geo-Marketing applications
11. Panel data analysis
12. Neural Networks, Transformers and Generative AI model
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
The assessment is based on a continuous assessment method. Specifically, attending students will be evaluated through project work, which will contribute 33.3% to the final grade. The remaining 66.7% will be determined by a final examination held at the end of the course. The exam aims to assess the knowledge and competencies acquired throughout the course and will consist of multiple-choice questions and practical exercises.
Thesis assignment criteria
The degree thesis involves the application of statistical methodologies in business.
Week 1
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
Settimana 2/ Week 2 Contenuto sessioni on line e on campus / On line and on campus lectures content PROGR_LEZ_2 3800 Sì 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
- 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
Panel data analysis
Week 5
Structured data and Databases. Introduction and basic concepts. Laboratory in R
Week 6
Segmentation and Hierarchical cluster analysis. Marketing applications in R and discussion of case studies
Week 7
Non-Hierarchical cluster analysis. K-means algorithm. Marketing applications in R and discussion of case studies
Week 8
ML supervised algorithms. Classification problem and predition. Marketing application in R and case studies discussion
Week 9
Regression: assumptions of Multiple
Linear Regression. Regression Tests. Variable selection strategies(Forward/backward/stepwise). Laboratory in R.
Week 10
Market Basket Analysis, basic concepts and R application.
Principal Component Analysis (PCA), theory and application in R
Week 11
Geo Marketing, Basic concepts and marketing applications.Spatial Statistics and Geo-marketing case studies.
Data Enrichment from geographical information
Week 12
Neural Networks, Transformers and Generative AI model
Fine-tuning a pre-trained AI model
Calling API systems