BIG DATA STATISTICS FOR BUSINESS (MOD.II)
The course aims to introduce the main features of Social Networks analysis. In particular, the most common methods for exploratory data analysis are described, focusing on the text mining methods. The introduction to the methods is accompanied by a practical guide to the use of the statistical software R. The acquisition of knowledge is aimed at achieving two main objectives: (i) to summarise the main aspects of a social network (ii) to develop a critical evaluation of the implications of the analysis results.
The learning objectives of the course can be declined as follows:
Knowledge and understanding:
The student will be able to understand the main features of a social network. The student is also expected to be able to communicate the results of the analysis.
Ability to apply knowledge and understanding:
The student will be able to interpret the analysis of a social network, recognising its usefulness in achieving the required objectives. He will also be able to present the analysis and create reports, including tables and graphs, to communicate information to third parties.
Autonomy of judgement
The student will be able to understand the meaning of a social network
At the end of the course, the student will have the ability to explain to both a general and specialist audience all the key aspects of a social network and to interpret the results.
At the end of the course, the student will be able to identify and read relevant bibliographic references and specialist texts to further their knowledge.
The course requires skills acquired in a basic statistics course (descriptive statistics, inferential statistics).
Social Networks Analysis: descriptive statistics, visualisation and structure measures.
The course adopts a variety of teaching methods:
- classroom lectures and laboratory exercises with application of the studied techniques to economic and business data-sets to acquire the basic knowledge of statistical techniques.
- presentation and discussion of real cases aimed at developing interpretative skills of the methods and techniques of practical implementation.
- elaboration of an individual or group project: the general objective is to apply the analysis techniques discussed during the classoroom lessons to a real data set of interest to the working group. Data for the project can be obtained from Internet sites or developed by the students.
Attending students. The project is prepared in a group (maximum 4 people). During the course, datasets will be assigned for the project and indications will be given on the preparation of a final report. Groups can be formed freely. Those who have difficulty forming a group communicate their name via email (
Non-attending students: it is necessary to prepare: 1. A project proposal - maximum one page (indicatively at least one month before the exam) that contains:
- The names of the members of the group (only if the project is not individual)
- Description of the project (objectives, steps for its completion, etc.)
- Description of a data set (dimensions, variable names with their description) that will be used for the project.
Each group (or student) is invited to discuss with the teacher to individuate the difficulties that may exist in finding the data, in preparing the proposal, in identifying the suitable technique, in the analysis phase, in the phase of drafting the report.
One week before the exam the project must be delivered to
. It includes:
- the report contains the summary of the results (maximum 4 pages).
- the R code briefly commented.
The final grade is expressed in 30ths, with a maximum grade equal to 30 / 30ths. Honors are assigned at the discretion of the teacher, as a distinctive element of excellence in the work performed.
At the end of the course, the examination will consist of an oral interview based on the content of the syllabus and will cover
- Methodological aspects
- Discussion of the results of the project previously sended.
The discussion of the methodological aspects has a weighting of 70% on the overall assessment and aims to test understanding of the theory and the ability to interpret in practice the results of the methodologies discussed in the course.
The final group report has a weighting of 30% on the overall assessment.
The examination is passed with a minimum score of 18/30. In order to obtain the maximum score, the student will have to demonstrate, in addition to an excellent knowledge of the proposed methods and a thorough interpretation of the results of the project, a correct use of the specialist vocabulary.
Hanneman, Riddle, Introduction to Social Network Methods, Riverside AC, 2005 (available at http://faculty.ucr.edu/~hanneman)
Csárdi, Nepusz, Airoldi, Statistical Network Analysis with Igraph, Springer, 2016.