Learning objectives
The course has the aim of guiding the student towards the following objectives:
D1 - KNOWLEDGE AND UNDERSTANDING
- to know and understand the theoretical basics, the lexicon and the main instruments of modern computer science
- to know the structure of a computer and of a telecommunication network
- to know binary arithmetic
- to know how to structure and formalize the solution procedure of any problem, both simple and complex
D2 - APPLYING KNOWLEDGE AND UNDERSTANDING
- to be able to use in a competent way computer science in the context of veterinary sciences.
- to be able to use autonomously databases to analyze data and create simple graphs and simple elaborations of data.
- to be able to perform bibliographic research in the main databases concerning veterinary sciences.
D3 - MAKING JUDGMENTS
- to be able to use the knowledge and understanding of the course topics to solve simple problems, autonomously evaluating the better solution for the problem itself.
- to be able to analyze quantitative data and to control research hypotheses through simple statistical analyses or models.
D4 - COMMUNICATION SKILLS
- to be able to communicate in a competent way when speaking about information technologies or about tools for the analysis and elaboration of data.
Prerequisites
Basics of mathematics and logic. Basic understanding of a text in English.
Course unit content
The first lectures deal with general considerations about information representation.
The second part of the module explains how computers and networks of computers work.
The third part of the course delves into databases and software applications.
In the fourth part of the lectures, students will be introduced to Python and PowerBI with specific focus on data analysis and visualization.
Full programme
Introduction to information technologies
• Information Representation
• Hardware & Operating Systems
• Networking and storage
• Databases
• Software
Data analytics with Python
• Python fundamentals
• Data loading and transformations
• Exploratory Data Analysis
• Statistics
• Modelling
Data visualization
• With python
• With PowerBI
Bibliography
P. Curtis et al. Informatica di Base, Mc Graw Hill, 2016
Deitel and Deitel. Intro to Python for Computer Science and Data analysis Learning to Program with AI, Big Data and The Cloud, Pearson, 2019
Alberto Ferrari and Marco Russo. Introducing Microsoft Power BI, Microsoft Press, 2016
Teaching methods
The course consists of 60 hours of classes, mostly dedicated to illustrating the course theoretical subjects. Due to the practical nature of programming, over 30 hours of the course will be dedicated to guided practical exercises carried out in the informatics lab and using also the students' own laptops, to provide them with practical knowledge of the most common software applications.
Assessment methods and criteria
The assessment of the achievement of the objectives of the course will include a final project versed on data analysis, which will have to be presented to the class, with oral examination on the theoretical contents of the course. Through questions about the contents of the course it will be determined whether the student has achieved the goals of knowledge and understanding of the content. Through practical exercises in lab regarding the analysis of data it will be determined whether the student has achieved the aim of applying the acquired knowledge.
Other information
Laptop is required for most lessons.
2030 agenda goals for sustainable development
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