STATISTICS FOR THE DIGITAL ECONOMY
cod. 1006335

Academic year 2024/25
3° year of course - First semester
Professor
Marco RIANI
Academic discipline
Statistica (SECS-S/01)
Field
Statistico-matematico
Type of training activity
Characterising
44 hours
of face-to-face activities
6 credits
hub: PARMA
course unit
in ITALIAN

Learning objectives

The purpose is to deal in a quantitative way the relevant information for
the firm using advanced computer programming. The data can come from different sources (internal or sample
surveys or web sites) . The final goal is to provide a rational support for decision
making using computer programming.


Knowledge and understanding
At the end of the course, the students will have acquired skills on the following topics:
- Basic statistical analysis;
- Multivariate statistical analysis;
- Dimension reduction techniques;
- Classification using supervised and unsupervised methods.
Skills and understanding skills applied
At the end of the course the student will be able to:
- understand the different phases which are at the root of statistical analysis of data
- translate the conceptual tools into empirical rules for the management of data coming from different sources and in different formats;
- plan and manage a statistical survey and understand what are the advantages and disadvantages of the different techniques of data collection
- develop distinctive skills in the area of statistical data analysis.
Independence of judgment
At the end of the course the student will be able to:
- evaluate the best statistical techniques to use;
- identify the best practices in managing data coming from different sources;
- evaluate the effectiveness of the different statistical techniques.
Communication skills
Through lectures managed in an interactive way, company testimonials and group work, the student will be able to: - clearly communicate, in a concise, timely and coherent manner, to different interlocutors (both academic and business), information and concepts (including complex ones) related to statistical data analysis;
- communicate effectively using an appropriate technical language;
Learning skills
The course aims to transfer the ability to translate the statistical principles into empirical rules of decision. The main topics are detailed through the presentation of successful data analysis case studies. At the end of the course the students will have gained the ability to expand and update the level and range of the knowledge acquired from lessons and course textbooks.

Prerequisites

Basic knowledge of mathematics and statistics

Course unit content

Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to MATLAB and to computer programming.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis. Sentiment analysis. Introduction to machine learning.

Full programme

Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to MATLAB and to computer programming.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis. Introduction to machine learning sentiment analysis and textual analysis.

Bibliography

Material downloadable from web site http://www.riani.it/SDE

Teaching methods

Frontal lessons with PC and practical lesson using Matlab. Additional material can be downloaded from the web site of the course http://www.riani.it/SDE

Assessment methods and criteria

Exam using the computer.

Knowledge and understanding are assessed by
methodological questions. The ability of
applying knowledge and understanding are assessed by questions on
the interpretation of results. Learning skills
are assessed by questions on the conclusions to be drawn from an
analysis.
Details on examination procedures are provided to the class and made available through http://www.riani.it/SDE before the start of the course.

Other information

Additional information can be found from the web site http://www.riani.it/SDE together with link to the associated youtube channel.

2030 agenda goals for sustainable development

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