The course aims to provide students with the following competences: to understand main analytical and statistical methods to analyze big data and open data for sustainability (Knowledge and understanding skills); to adopt the necessary methodological and practical tools to analyze different kinds of data (Applied knowledge and understanding skills); develop autonomous judgements in selecting the proper technique and compare results obtained by competing methods (Autonomy of judgement); discuss and present the main methodologies and the expected results of the statistical techniques (Communication skills); be able to understand the key literature about BDA for sustainability and social inclusion (Learning skills).

A basic knowledge of descriptive statistics and quantitative methods is recommended. Students should be familiar with basic statistical inference, including confidence intervals and hypothesis testing.

Big Data (BD) and Open Data (OD) are increasingly recognized by governmental institutions, firms, and research organizations as indispensable for addressing sustainability challenges. Big data finds widespread application across various industries, driving competitiveness and innovation in business and industrial sectors. Big data analytics (BDA) tools, in particular, are leveraged for consumer profiling, targeted advertising, and personalized services or goods.
However, these methodologies can also serve broader societal objectives, such as obtaining real-time information on people's well-being and directing humanitarian aid to those most in need.
New data sources including, new technology, and new analytical tools may be used responsibly and efficiently in order to enhance evidence-based decision-making for sustainable and inclusive policies as well as monitoring the progress toward the Sustainable Development Goals (SDGs).
The module will offer both theoretical and practical insights into BDA-based methodologies within the context of social sustainability and inclusion. Real-life case studies will be used to illustrate in practice the methodology and discuss the obtained results. Particular attention will be given to discuss the added value of using BDA technology to achieve social Sustainable Development Goals (SDGs) 2030 agenda promoted by the United Nations.

Selected chapters from:
Rob Kitchin (2014) The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences, Sage
Aggarwal, C. (eds) (2011) Social Network Data Analytics. Springer, Boston, MA.

A detailed bibliography and suggested readings will be given out at the beginning of the course.

The module covers the following topics:

1. Small Data, Data Infrastructures and Data Brokers
2. Open and Linked Data
3. Big Data
4. Enablers and Sources of Big Data
5. Data Analytics
6. Statistical models and methods for big data
7. Social networks data and related methods

Lectures, individual, and/or group research activities, as well as software laboratories and practical sessions.

The exam will be composed of a written part and a practical part concerning all the topics of the course.
The written exam will last maximum 100 minutes and will count 20 points. It consists of ten true/false and multiple choice questions (covering the theoretical aspects of the course, and each correct answer is worth 0.5 points) as well as at least two exercises on statistical survey methodology and statistical modeling for which is required to describe the adopted approach (15 points in total).
The practical part concerns in a presentation of the analysis of a case study (agreed with the teacher), written in a report of maximum 15 pages and summarized by means of slides. (maximum 12 points).

To pass the exam, the student must demonstrate sufficient knowledge of the topics related to data analysis approaches for sustainability and submit a sufficiently well-structured report.

The course will contribute to some SDGs of the Agenda 2030.