B0064 - INTRODUZIONE ALLA DATA SCIENCE E AL PENSIERO COMPUTAZIONALE

Learning outcomes

At the end of this course, the students will have a good knowledge and understanding of the fundamental principles of algorithmics and of programming in Python, with specific focus on problems with large amount of data. The students will be able to apply the acquired knowledge to read, write, and test programs using the Python programming language. Moreover, the student will be able to understand prototypes and programs written by other people.

Thanks to the acquired knowledge, the student will be able to evaluate pros and cons of a prototype or of an implementation in the Python language of a program using typical algorithmic techniques to handle data structures coming from the web or from open repositories. 

Course contents

• To define a solid and homogeneous base of the structure of the computational process.
• To structure the concept of programming and of programming languages.
• Elaborate the concept of digital transformation.
• Evidence the concept of production of software, both from a organizational and from a cognitive viewpoint.
• Present how the use of computational thinking and of the informatics tools could help solving problems of organizing systems, of understanding complex structures, and of forecasting possible events.
• Use text processing and its understanding as a paradigmm of the process of digital transformation

Readings/Bibliography

The course does not have a mandatory textbook. Here below there is a list of suggested readings:

• Dirk Hovy, Text Analysis in Python for Social Scientists - Discovery and Exploration, Cambridge University Press, 2021, ISBN 978-1108873352
• Sofía De Jesús , Dayrene Martinez, Applied Computational Thinking with Python, Packt Publishing, 2020, ISBN 978-1839219436
• Autori multipli, The LaTeX Wikibook, Wikibooks community, https://en.wikibooks.org/wiki/LaTeX

Assessment methods

The student may decide the evaluation method most suitable for the personal learning profile among the following:

• overall oral,

• preparation of a report on a specific topic covered during the course, followed by a focused oral.

• preparation of a report on distributed cognition that would evidence the content of the material presented in the course

Teaching tools

This course is managed in strict synchrony with the one of Laboratorio di Programmazione. For the detailed syllabus, the precise timetable, and all other information, please refer to the online syllabus [https://github.com/GiancarloSucci/UniBo.IDSEPC.A2022/blob/main/A2022.IDSEPC.Sillabo.pdf] on GitHub [https://github.com/GiancarloSucci/UniBo.IDSEPC.A2022] where is present also all the teaching material.

The instructor can be reached by email (g.succi@unibo.it) and telegram ( t.me/G14nc4r10 [https://t.me/G14nc4r10] ).

Office hours are on demand, on request of the student to be done primarily on telegram.

The main communication vehicle in the course is the telegram group, where the students are advised to enrol at their earliest convenience:  https://t.me/+J0pZPw-QoBRiZjM0 .

 

Office hours

See the website of Giancarlo Succi