DATA ANALYTICS

Data Analytics provides management with relevant, accurate and valid information to support decision making. This course will be focused on applications in marketing research, considering a variety of practical and technical aspects related to business environment and to quantitative measurement applied in real contexts. Fundamentals of multivariate statistics (such as factor analysis and classification methods) will be illustrated and applied to develop marketing research solutions. The course aims to guide the student in the selection and learning of statistical tools, with particular attention to the interpretation of results in a decision-making perspective.

1. Knowledge and understanding
- To understand how to formulate a research design
- To select the correct technique for the data at hand
- To know the fundamentals of the multivariate techniques presented
- To understand the role of data analytics in the decision-making process

2. Ability to apply knowledge and understanding
- To implement the different multivariate techniques in R, from data imputation and coding to graphical representation
- Integrate secondary and primary data sources to address a business problem

3. Ability to judge:
- To develop marketing research solutions through the appropriate statistical methods

4. Communication skills
- To communicate technically with the team work
- To present the research findings in a comprehensible format ready to be used by the management in the decision-making process

5. Learning skills
- Developing statistical solutions to management puzzles
- Learning by programming in R
- Learning by doing a complete case study of marketing research
- Learning by team working

Basic concepts of statistics and probability. Propaedeuticity: Probability and Statistics

1. Introduction and Research design formulation
a. Types of data
b. Measurement and scaling
d. Sampling
e. Types of research design

2. Data and Analysis
a. Basics of Business Analytics
b. Multivariate Analysis for Marketing Research

Malhotra, N.K., 2018, Marketing Research: An Applied Orientation (7th edition). Pearson.

The main referral text for the methods is:
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J.Statistics for business &
economics. Cengage Learning.

The achievement of the course objectives will be assessed through a a written exam (open ended questions and multiple-choice questions - paper and pencil) and a lab exam (2 exercises to be solved with R), whose marks will be averaged to determine the final mark. A minimum grade of 12 in either part is conditional for passing, provided the average grade is 18.

The written part (30 minutes) and the laboratory part (1 hour) will be held on the same day. Each question in the written part is worth 4 points, for a total of 32 points, and each exercise in the laboratory part is worth 16 points, also for a total of 32 points. To pass, students must achieve an average score of 18, conditional on a score of at least 12 points in both parts. To pass, students must achieve an average score of at least 18, with a minimum of 12 points in both parts. Passing scenarios include:

- Scoring at least 18 points in both parts (e.g., 4 and a half correct questions in the written part and one complete exercise with code and comments, plus part of another exercise in the lab).
- Scoring 12 points in the written part (3 correct questions) and at least 24 points in the lab (for instance 2 exercises fully correct in terms of code and basic comments).
- Scoring 24 points in the written part (6 correct questions) and 12 points in the lab (1 exercise fully correct in terms of code and basic comments, or two partially correct with code and basic comments).

The highest grade will be awarded to students who correctly answer all questions in the written part and provide fully correct coded and thoroughly commented solutions to the lab exercises.

The code required for the exam will be provided with the exam text. Students are not expected to memorize it but must know how to apply and interpret the output.

At the end of the course, a mock exam will be held to familiarize students with the exam format

Lectures will be complemented by R lab sessions applied on a leading example and with R exercises.

English

The moodle page of the course will be constantly updated with Slides, Notes and R Exercises.

written