Programma non disponibile in italiano

Code: 1075I	Credits: 6	Semester: 1
Lecturers: Chiarello Filippo, Martini Antonella

Learning outcomes

Knowledge

The students will acquire knowledge that is transversal to the Master's Degree in Data Science and Business Informatics. In particular, the students at the end of the course will: 

•  Be aware of the whole process of value generation in a data science process

•  Know available methods for designing data-driven products and services

•  Use critically Generative AI to support the Design Process

•  Be aware of the business, environmental and social impact of data science solutions

Assessment criteria of knowledge

The students will be able to demonstrate this knowledge by discussing related topics with the teachers and in peer-to-peer discussions.

Skills

The course is focused on practical skills. Students will learn to apply quantitative methods for solving design and management problems. In particular, at the end of the course, students will learn to: 

•  Use methods to think creatively and critically

•  Build a search query for mapping the relevant documents in a technological or knowledge domain 

•  Use methods to understand and map users' needs

•  Map and classify available data science tools (methods and technologies) 

•  Use methods to translate users' needs in technical specification

•  Measure and evaluate the users’ needs

•  Choose the best tool (methods and technologies) to solve a data science problem

•  Know and use prEtotyping techniques

•  Use Generative AI to support the Design Process

•  Develop methods to assess data science competencies (personal and of the team)

•  Know and use methods to communicate the project results

Assessment criteria of skills

The students will apply these skills in teamwork, where they will be asked to design a data-science solution. Both attending and non-attending students will be followed in the development of the project, towards the final discussion, thanks to mid-term deliveries. Where possible, students will also be asked to participate in the peer-to-peer evaluation of the project activities.

Behaviors

The course has a fo us on different soft-skills. Some of the these skills (i.e. creativity and crhticial-thinking) will be faced using methodological approaches, to help students develop behaviours towards the use of methods (using the approach developed in the European Project Ulisse, https://ulisseproject.eu/). During the activities of the course (lessons and project activities) the students will also develop the following behaviours:

• Be able to work and in a diverse, multi-cultural and interdisciplinary team

•  Be positive and methodological towards complex socio-technical problems

•  Be curious about the continuous development of the data science sector

•  Work in a team of students to design and implement a project

•  Listen and discuss actively in a team 

Assessment criteria of behaviors

Students will be helped to develop these behaviours thanks to class activities and peer-to-peer evaluations. Students will not be assessed for the behaviours directly, but these will help show knowledges and skills. 

Prerequisites

No prerequisites in particular. The course is in fact code-free, for this reason accessible also to students without a background in computer science. Anyway, sone attitudes will help students have success in the course: 

- Curiosity and self-motivation

- Openness to new approaches and ideas

- Reading, watching and listening actively

 

Teaching methods

The course will be taught using a Problem Based Learning Approach. The approach will be implemented using a dynamic classroom approach in which students will actively explore real-world challenges and problems. The lessons will mix standard explanations by the teacher and exercises/activities that the students will do in teams. 

During the course, the teams for the project will be asked to work together during the lesson and to mix the member.

Syllabus

(This is a tentative syllabus, that will be adapted considering the course scheduling)

Lesson 1 - Introduction to the course and overview of the design process (2 hr)
Lesson 2 - The scientific method (1 hr)
Lesson 3 - How to define research questions (1 hr)
Lesson 4 - Research vs Development (1 hr)
Lesson 5 - Products vs Services (1 hr)
Lesson 6 - Scope Definition (1 hr)
Lesson 7 - Project Kick-Off (1 hr)
Lesson 8 - Objective and OKR (1 hr)
Lesson 9 - Project Management for Data Science: Introduction (2 hr)
Lesson 10 - Project Management for Data Science: Methods (2 hr)
Lesson 11 - Project Management for Data Science (Lab) (2 hr)
Lesson 12 - Methods for user needs analysis (2 hr)
Lesson 13 - Methods for user needs analysis (Lab) (2 hr)
Lesson 14 - Quality function deployment for data science (lab) (2 hr)
Lesson 15 - Quality function deployment for data science (lab) (2 hr)
Lesson 16 - Writing product and services specifications (2 hr)
Lesson 17 - Project Review (1 hr)
Lesson 18 - Query Design (2 hr)
Lesson 19 - Technological Mapping (2 hr)
Lesson 20 - Methods for assessing technical and economical feasibility (2 hr)
Lesson 21 - Pre-Totyping (4 hr)
Lesson 22 - Project Review (1 hr)
Lesson 23 - Writing Reports (2 hr)
Lesson 24 - Writing Reports (Lab) (2 hr)
Lesson 25 - Project Review (1 hr)
Lesson 26 - Conflict Management (2 hr)
Lesson 27 - Creativity (2 hr)
Lesson 28 - Critical Thinking (2 hr)

Bibliography

Relevant books:

The Righ It: Why So Many Ideas Fail and How to Make Sure Yours, Alberto Savoia (2019)

The Signal and the Noise: Why So Many Predictions Fail - but Some Don't, Nate Silver (2015)

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Pedro Domingos (2015)

Relevant websites:

https://www.ideou.com/pages/design-thinking-resources

https://ai.google/education/

https://nadiapiet.com/

Non attending students

Non-attending students are welcome to attend the exam. All the lessons will be recorded, and students will have access to the material of the course. Non-attending students are strongly encouraged to use office hours to interact with the teacher during the preparation for the exam. Also, students are encouraged to work in teams even if they are not attending the course. 

 

Assessment methods

The students will be asked to make a teamwork project, where they will design a data-science based product or service. Both attending and non-attending students will be followed in the development of the project, towards the final discussion, thanks to mid-term deliveries. Where possible, students will also be asked to participate in the peer-to-peer evaluation of the project activities (https://en.wikipedia.org/wiki/Peer_assessment).

 

Fonte: ESSETRE e Portale esami