DATA MANAGEMENT

Digital data management is a key skill for challenging complexity. The quality of decisions and strategies involving individuals and organizations increasingly depends on the ability to extract, filter and assemble digital data from which to infer operational information to solve practical problems.
The objective of the module is to provide methodological, theoretical and application guidelines to learn how to effectively manage the phases of acquisition, storage, processing and representation of digital data, with a specific focus on the potential of Machine Learning and on the main functions of Data Analysis tools.
At the end of the course, students will be able to develop a digital data management project for solving a practical problem assigned by the teacher.

1. Knowledge and understanding:
- students will be able to describe the characteristics of digital data and the criteria for evaluating the quality of the data
- students will be able to describe the six stages of the digital data management process
- students will be able to describe the fundamental models for data processing
- students will be able to list some software applications for digital data management

2. Ability to apply knowledge and understanding:
- students will be able to use software applications for research and acquisition of digital data
- students will be able to use programs for archiving and indexing data
- students will be able to apply basic methods to process digital data
- students will be able to implement tools for data visualization and representation

3. Judgment skills:
- students will be able to contextualize the knowledge acquired, identifying the models, methods and software most suitable for the desired output

4. Communication skills:
- students will be able to effectively present the results of data analysis
- students will be able to interact with colleagues and the teacher, according to the objectives of the course

5. Learning skills:
- students will be able to use and integrate information from notes, handouts, slides and practical exercises
- students will be able to evaluate their level of preparation through practical and laboratory activities

The module is aimed at those who want to enhance their specific skills through elementary techniques for researching, organizing, interpreting and viewing digital data in the various disciplinary areas, in order to improve the quality of their forecasts and decisions. Therefore, beyond basic computer skills, no specific technical knowledge in coding or software for information processing is required, nor are mathematical skills higher than those of high schools, technical and professional institutes.

The module is divided into two parts and ten didactic units.

PART ONE: The sunset of the analog universe

1. The discreet appeal of digital
- Distinction between "analog" and "digital"
- Information life cycle management
- Relationship between Man and Machine

2. Journey to the center of Oasis
- Effects of digitization on reality
- The six spheres: astronomical, ecological, political, economic, social and individual
- The principles of the new digital age

3. Hercules at the crossroads: decision, future and complexity
- Stages and characteristics of the decision-making process
- Methods for forecasting the future
- Analysis of complex systems

4. The new digital intelligence
- The concept of "digital intelligence"
- The six dimensions: data acquisition, memory, calculation, representation, activation and adaptation
- Human intelligence, AI and hybrid intelligence

PART TWO: Six-dimensional digital intelligence

5. From primordial chaos to the realm of bits
- "Datafication" and principles of Data Science
- Methods of acquisition and conversion of digital data
- The logical structure of a dataset

6. In search of the lost data
- Features and functions of the digital memory
- Four types of logical memory architecture
- Relationship between digital memory and organization

7. Physiology of positronic thinking
- From digital data to information
- Computing and systems modeling techniques
- Machine Learning algorithms: classification, regression, clustering and time series analysis

8. The color of the data
- Process of representation and communication of data and information
- Types of graphs, diagrams and infographics
- Multimedia principles of Data Visualization

9. The geometric construction of decisions
- Activation and mechanical decision models
- Dashboard to support decision making
- How to turn data into decisions

10. Digital intelligence in the mirror
- Adaptation and evolution of a dynamic system
- Quality of individual and collective digital intelligence
- Monitoring and overall evaluation of digital intelligence

[1] G.B. Ronsivalle, "La nuova intelligenza digitale. Come trasformare i dati in decisioni per progettare il futuro", Maggioli Editore, Collana Apogeo Education, 2022.
[2] G.B. Ronsivalle, I. Baccan, A. Bersan, "The Orange Box. Il nuovo laboratorio di Machine Learning", Edizioni Wemole, 2023 (in press).

The final exam is divided into three steps:

Step 1 - Taking an online written test on basic theoretical knowledge (max score = 15 points; minimum passing threshold = 9 points). The test involves the administration of a digital questionnaire consisting of structured tests of different types (multiple choice-single answer, multiple choice-multiple answer, correspondence, etc.).

Step 2 - Development of a project work focused on the application of elementary digital data management techniques (max score = 15 points; minimum exceeding threshold = 9 points). The project consists in the creation and presentation of a short report in which students must describe the various phases of the project for the acquisition, storage, processing and representation of data functional to the resolution of a practical problem assigned by the teacher.

Step 3 (optional) - Oral interview on the topics of the course book (max score = 3 points, to be added to the score achieved in the previous steps).

The lectures alternate (a) theoretical presentation sessions supported by multimedia slides, (b) interaction activities on the topics of the course, (c) case studies, (d) individual and group exercises using Data Science platforms , (and) simulations. The course also includes the publication of videotutorials to present the characteristics and functions of data management software.

Italian

written and oral