DATA ANALYSIS AND DATA VISUALIZATION

Academic year
2025/2026 Syllabus of previous years
Official course title
DATA ANALYSIS AND DATA VISUALIZATION
Course code
EM1703 (AF:570402 AR:319141)
Teaching language
English
Modality
On campus classes
ECTS credits
6
Degree level
Master's Degree Programme (DM270)
Academic Discipline
SECS-S/06
Period
2nd Term
Course year
1
Where
VENEZIA
The objective is to introduce students to some statistical techniques for marketing research, conceived as a tool to support the decision making process.
In particular, the course will concentrate on market segmentation and positioning techniques also giving attention to some practical examples.
1. KNOWLEDGE AND UNDERSTANDING
1.1 Understand and distinguish the main quantitative approaches to marketing research)
1.2 Know and understand the main procedures to obtain valuable data (in particular surveys)
1.3 Know and understand the main statistical techniques to analyse the data at hand
2. APPLYING KNOWLEDGE AND UNDERSTANDING
2.1 Recognize the type of problem at hand and the data set on which the quantitative analysis is based
2.2 Elaborate questionnaire to obtain data compatible with the type of analysis
2.3 Analyse data adopting the correct statistical procedure
3. MAKING JUDGMENTS
3.1 Given the problem at hand, being able to recognize the most adequate statistical procedure
3.2 Being able to distinguish data sources and quality
3.3 Being able to interpret the statistical results and their implications
Statistics (basics of descriptive statistics and inference).
What is Data Mining?
Data Visualization
Evaluating Classification and Predictive Performance
Near Neighbor and Naive Bayes Methods
Classification and Regression Trees
Regression Review and Selection of Variables
Logistic Regression
Discriminant Analysis
Neural Networks
Cluster Analysis
Bagging and Boosting
Dimension Reduction and Penalty Methods
Shmueli, Bruce, Yahiv, Patel, and Lichtendahl, Data Mining for Business Analytics: Concepts, Techniques, and Applications with R, 1st Edition (2017)
Ch. Balakrishna, B. Santhosh Kumar, S. Sathishkumar, Gopika G S, "Beginning Data Science in R Data Analysis, Visualization, and Modelling: Hands-On Tutorials".
Assessment methods: The final test consists of a written theoretical and practical exam, with open and close (crosses) answers, on the contents developed during the course.
written
The grading system (how the grades will be assigned) is the same for attending or non-attending students:
A. scores in the 18-22 range will be awarded in the presence of:
- sufficient knowledge and ability to understand applied in relation to the programme;
- sufficient ability to apply knowledge and understanding and judgment;
- sufficient communication skills, especially in relation to the use of specific language relating to the subject;
B. scores in the 23-26 range will be awarded in the presence of:
- good knowledge and ability to understand applied in relation to the programme;
- good ability to apply knowledge and understanding and judgement;
- good communication skills, especially in relation to the use of specific language relating to the subject;
C. scores in the 27-30 range will be awarded in the presence of:
- excellent knowledge and ability to understand applied in relation to the programme;
- excellent ability to apply knowledge and understanding and judgment;
- excellent communication skills, especially in relation to the use of specific language relating to the subject.
Theoretical lectures alternating with practical examples.
English
Definitive programme.
Last update of the programme: 20/03/2025