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Lecturer: Filippo Chiarello

Contact: email - phone 050 2217318

Office hours: TO BE DEFINED


  • Lecture start:
    THe first lecture will be Monday the 13th of September, 14:15‑16:00 Aula Fib C
  • As the course starts:
    Each student should send an email to the professor from his/her favourite email account with subject D4DS21 and the following data
    (by doing so, the account will be included in the class mailing-list, where important announcements can be sent):
    1. first name and last name (please clarify which is which, to avoid ambiguities)
    2. enrolment number (numero di matricola)
    3. bachelor degree (course of study and university)

Objectives of the course

The course is focused on practical skills. Students will learn to apply quantitative methods for solving design and management problems in the context of data science and artificial intelligence. The students will acquire knowledge that is transversal to the Master 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

• Understand the differences between research projects and a development process

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

The course has been co-designed with three companies of the innovation ecosystem of Pisa:

These companies have helped to select the content for the course and are open have students for traineeships and work collaborations.

The course is also in synergy with the research team B4DS, where some students interested in doing research can find placement too:

Intended Behaviours

The course has a fo us on different soft-skills. Some of these skills (i.e. creativity and critical 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. During the activities of the course (lessons and project activities) the students will also develop the following behaviours:

• Be able to work 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

• Listen and discuss actively in a team


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)


Exam for attending students

The grade for the exam will be computed as follows:

Project Document: 30%, evaluated by the teacher Project Presentation: 20%, evaluated by the teacher Project Document: 10%, evaluated by peers students Peer Evaluation: 20%, evaluated by the teacher Report Review: 20%, evaluated by the teacher

Registration to the exam is mandatory.

Exam for 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 of the exam, especially for the preparation of the project. Also, students are encouraged to work in teams even if they are not attending the course.

The percentages for non-attending students are the following:

Project Document: 30%, evaluated by the teacher Project Presentation: 20%, evaluated by the teacher Paper Review: 50%, evaluated by the teacher

Registration to the exam is mandatory.


The students will be asked to make a teamwork project, where they will design a data-science based product or service. Students will be followed in the development of the project, towards the final discussion, thanks to class activities. Attending students will also be asked to participate in the peer-to-peer evaluation of the project activities.

Scientific Contribution Review

During the course, students will be asked to make a review of a scientific contribution, This can be:

  • A scientific paper
  • A book
  • An article on a blog or medium
  • A video on youtube
  • Others

The student will decide the content to review and send an e-mail to the professor at least one week before the date of the exam. The report will be in the form of a short report (two pages).

Here is a list of already discussed contents (can not be taken again):

Project FAQ

  • What do I need to start the project? You need a team and a *users' need*. The need has to be discussed with the teacher, during a lecture or during office time.
  • How many people can work together on the same project? Projects are assigned to groups of two or three people. Exceptionally, single students can be allowed (especially non-attending students), but a solid motivation is needed.
  • When is the deadline for submitting the project? The project document and presentation need to be sent to the teacher at least one week before the exam date.
  • What does it happen if we miss the deadline? You will have to wait for the next exam session.
  • What does it happen if a person leaves the group? You must notify immediately the teacher and choose if you prefer to complete the work individually or change the composition of the team
  • How do we deliver the project? You must send the teacher a project report in .pdf following the. decided structure and page limits. Optionally, you can submit annexes.
  • When do we discuss the project? Preferably, at the first exam session that follows the delivery of the project.
  • Is it necessary to have already delivered the project document in order to register for the exam? No it is not. But of course, the project must be delivered in time for being presented and discussed at the actual oral exam.
  • What if the project document is not sufficient?A negative evaluation of the project document may require to rework some parts, submit a revised document and schedule a new exam (possibly at the next exam session). In this case, the teacher will communicate the decision to the students before the date of the exam. If no communication after the project document presentation is received, it means that the project has received a positive evaluation. A positive evaluation of the project allows each group member to access the oral part of the exam.

Exam FAQ

  • How do we register for the exam? As usual, you must enter your choice on the ESAMI portal.
  • What are the dates of the exam? THE DATES ADVERTISED IN THE ESAMI PORTAL ARE NOT NECESSARILY THOSE OF THE ACTUAL EXAM. After you register for the exam, on the basis of the projects received and any other constraint communicated by each student, the teacher will post a tentative schedule on this web page and will inform you by email or on Teams.
  • How is the exam organized? The exam is oral and is organised around the project and the report. After the team presentation of the project, we will have a discussion starting from the project, the report and (eventually) some of the topics covered by the course.
  • How is the project discussion organized? ALL GROUP MEMBERS MUST BE PRESENT TO THE PROJECT DISCUSSION. All group members must demonstrate they have participated to all the activites of the project.


The course will be blended (online and in-person). To join a lecture online enter the virtual classroom, go to the Calendar tab and click on the scheduled lecture. The link to MSteams channel will appear soon.

N Date Time Room Lecture notes Topics Links
1 Mon 14/09 11:00-12:45 Microsoft Teams Lecture 1 Course introduction:
course objectives, textbooks,
BPM aim and motivation,
models and abstraction
2 Wed 16/09 16:15-18:00 Microsoft Teams Lecture 2 Introduction to Business Processes:
Taylorism, work units, processes, terminology,
organizational structures
- Mon 21/09 11:00-12:45 Canceled Election day
3 Thu 24/09 16:15-18:00 Microsoft Teams Lecture 2 (2nd part) Introduction to Business Processes:
Processo orientation and reengineering, main definitions,
visual notations
4 Mon 28/09 11:00-12:45 Microsoft Teams Exercises
Examples and Exercises
5 Thu 01/10 16:15-18:00 Microsoft Teams Examples (ctd.) Examples and Exercises
6 Mon 05/10 11:00-12:45 Microsoft Teams Examples and Exercises (ctd.)
Lecture 3
Examples and Exercises

Evolution of Enterprise Systems Architectures:
separation of concerns, sw architectures
individual enterprise applications,
enterprise resource planning system,
siloed enterprise applications,
enterprise application integration,
message-oriented middleware
7 Thu 08/10 16:15-18:00 Microsoft Teams Lecture 3 (2nd part)
Lecture 4
Evolution of Enterprise Systems Architectures:
enterprise service computing

Business Process Modelling Abstractions:
Separation of concerns, horizontal abstraction,
aggregation abstraction, vertical abstraction
8 Mon 12/10 11:00-12:45 Microsoft Teams Lecture 5
Lecture 6
Business Process Methodology:
levels of business processes,
business strategies, operational goals,
organizational BP, operational BP,
implemented BP, design guidelines,
from business functions to processes

Business Processes Lifecyle:
design and analysis, configuration,
enactment, evaluation,
administration and stakeholders

Mathematical background:
Sets, functions, relations
- Thu 15/10 16:15-18:00 Canceled
9 Mon 19/10 11:00-12:45 Microsoft Teams Lecture 7 (1st part) Mathematical background:
predicate logic, induction, recursion

Introduction to Petri nets:
finite state automata
10 Thu 22/10 16:15-18:00 Microsoft Teams Exercises (from Lecture 7)
Lecture 7 (2nd part)
Lecture 8 (1st part)
Introduction to Petri nets:
from automata to Petri nets

More concepts about Petri nets:
multisets and markings
11 Mon 26/10 11:00-12:45 Microsoft Teams Woped basics
Lecture 8 (2nd part)
More concepts about Petri nets:
multisets and markings,
transition enabling and firing, firing sequences,
reachable markings, occurrence graph
12 Wed 28/10 16:15-18:00 Microsoft Teams Exercises (from Lecture 8) Modelling with Petri nets:
Examples and Exercises
13 Thu 29/10 16:15-18:00 Microsoft Teams Exercises (from Lecture 8)
Lecture 9 (1st part)
Modelling with Petri nets:
Examples and Exercises

Behavioural properties:
14 Mon 02/11 11:00-12:45 Microsoft Teams Lecture 9 (2nd part)
Exercises (from Lecture 9)
Behavioural properties:
dead transitions, place liveness, dead places
15 Thu 05/11 16:15-18:00 Microsoft Teams Exercises (from Lecture 9)
Lecture 9 (3rd part)
Behavioural properties:
deadlock freedom, boundedness, safeness, cyclicity

Structural properties:
weak and strong connectedness,
S-systems, T-systems, free-choice nets
16 Mon 09/11 11:00-12:45 Microsoft Teams Exercises (from Lecture 9)
Lecture 10 (1st part)
Nets as matrices:
markings as vectors
17 Thu 12/11 16:15-18:00 Microsoft Teams Lecture 10 (2nd part) Nets as matrices:
incidence matrices, Parikh vectors,
marking equation lemma, monotonicity lemma,
boundedness lemma, repetition lemma

Exam sessions


Date Time Room Info
mds/d4ds/start.1631116927.txt.gz · Ultima modifica: 08/09/2021 alle 16:02 (19 mesi fa) da Filippo Chiarello