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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:
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)
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.
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.
During the course, students will be asked to make a review of a scientific contribution, This can be:
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):
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 |
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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 | 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 | Woped |
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: liveness | |
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 |
TO BE DEFINED
Date | Time | Room | Info | |
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