Learning objectives
a. Knowledge and understanding
The purpose is to deal in a quantitative way the relevant information for
the firm. The data can come from different sources (internal or sample
surveys). The final goal is to provide a rational support for decision
making.
b. Applying 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.
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.
c. Making judgements:
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.
d. 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;
e. 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.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis.
Full programme
Multivariate data analysis: data warehouse and data mining.
Exploratory data analysis: missing values and outliers
Introduction to MATLAB.
Dimension reduction: principal component analysis. Applications to
marketing problems.
Statistical methods for market segmentation: cluster analysis and correspondence analysis.
Particular emphasis is given to the computational aspects using Excel and MATLAB
Bibliography
S. ZANI – A. CERIOLI, Analisi dei dati e Data Mining per le decisioni
aziendali, Giuffrè Editore, Milano, 2007
Teaching methods
Link to join the classes (beginning date 16/09/2020)
https://teams.microsoft.com/l/team/19:7f4893661a8f4da19b9cdacf31d9e59f@thread.tacv2/conversations?groupId=48bec449-b654-40a1-8aad-de647f3257f3&tenantId=bb064bc5-b7a8-41ec-babe-d7beb3faeb1c
Frontal lessons also with PC.
Teaching materials (course slides and data for replicating analyses) are provided through http://www.riani.it/ADM .
Further materials (data for individual practical work and research readings) are also provided through http://www.riani.it/ADM , as well as details on the course timetable.
Assessment methods and criteria
Computer exam. The students must send a file in .xlsx format or in .m format or .mlx format (or .zip format in case multiple files are sent). The name of the file must follow the following rules: surname_name_numeromatricola. Students with accents in the first name or surname must omit them in the name of the file.
The knowledge and understanding achieved will be assessed with n. 3 open-ended questions, each worth 10 points.
The duration of the exam is 75 minutes. The final test is evaluated on a 0-30 scale.
The vote "30 cum laude" will be awarded to particularly deserving students who, in addition to having complied with the requisites necessary to obtain the full evaluation, in the performance of the test have overall demonstrated an appreciable systematic knowledge of the topic, an excellent ability to apply the knowledge acquired to the specific problem in question, a considerable autonomy of judgment, as well as a particular care in the formal drafting of the essay.
Written answers will be assessed overall on five criteria: knowledge, the
ability to apply knowledge, the capacity to communicate with appropriate
technical language and terminology, independence of judgment and
capacity to learn. During the exams the student are allowed to browse the textbook. Notes, phone are not permitted. Results will be published in the usual web platform ESSE3 within 10 days.
The exam will be on line through Microsft Teams platform. The link to upload the documents on line and the link to join the exam will be shown in the web page of the course http://www.riani.it/ADM
Other information
Additional information can be found from the web site http://www.riani.it
2030 agenda goals for sustainable development
- - -
