Computer Knowledge and E-Skills
A.Y. 2024/2025
Learning objectives
The course aims to provide students with basic knowledge and skills in computer science and statistics, applying concepts to the reading, synthesis, analysis and interpretation process of complex phenomena.
We will introduce basic concepts of descriptive statistics, such as methods for data collection, representation, and analysis, measures of central tendency, measures of dispersion, relationship between two statistical variables (bivariate statistics, correlation, linear regression).
In order to develop statistical and computational thinking, these topics will be transferred into the application field through the use of software tools.
A cross-cutting objective is to provide students with new skills they can immediately use in their studies and that are crucial to enter the job market.
We will introduce basic concepts of descriptive statistics, such as methods for data collection, representation, and analysis, measures of central tendency, measures of dispersion, relationship between two statistical variables (bivariate statistics, correlation, linear regression).
In order to develop statistical and computational thinking, these topics will be transferred into the application field through the use of software tools.
A cross-cutting objective is to provide students with new skills they can immediately use in their studies and that are crucial to enter the job market.
Expected learning outcomes
At the end of the course, students will be able to:
- collect data using information gathering tools and public sources;
- represent data both graphically and through appropriate summary values;
- interpret data by exploring the relationship among variables;
- use software and write scripts for managing, processing, automating, representing and archiving datasets;
- design and produce useful multimedia content for third-party users;
- adopt an ethical approach to the use of Information and Communication Technologies;
- critically use collaborative and productivity tools.
- collect data using information gathering tools and public sources;
- represent data both graphically and through appropriate summary values;
- interpret data by exploring the relationship among variables;
- use software and write scripts for managing, processing, automating, representing and archiving datasets;
- design and produce useful multimedia content for third-party users;
- adopt an ethical approach to the use of Information and Communication Technologies;
- critically use collaborative and productivity tools.
Lesson period: First semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Computer science unit
· Microsoft Office Word (word processing): document viewing, saving and printing; text formatting; insert images, tables, shapes, icons and graphics; layout and pagination; spell checking tools; references and review.
· How to write a Final Degree Project.
· Microsoft Office Excel (data calculation and analysis): spreadsheet display and saving; logical, search and reference, mathematical and trigonometric functions, relative and absolute references, conditional formatting, data sorting according to criteria, filters, graphs. Macros and databases.
· PowerPoint (presentations): document viewing and saving; slide management and object insertion; animated slideshows, transition effects, "slideshow" mode; do's and don'ts to make a presentation captivating. How to prepare the presentation of the Final Degree Thesis.
· Tools for bibliographic research.
Statistics unit
Introduction to statistics
· Definition, usefulness and uses of statistics;
· Basic concepts (population, sample, variable);
· Statistical language;
Data handling
· How to organize data;
· Secondary data and sources;
· Primary data and sampling;
· Survey techniques;
· Datasets, tables and graphs;
Data analysis
· Data distribution;
· Qualitative data organization;
· Quantitative data organization;
· The data central position indices (mean, median and mode);
· The scatter indices (range, mean absolute deviation, variance, standard deviation, interquartile range, properties of typical intervals)
· The shape indices for unit data and data grouped into classes (Pearson skewness index, first and second Pearson skewness coefficient, Fisher skewness index, Pearson-Fisher skewness coefficient, kurtosis index);
· The non-central position indices (z-scores, quartiles);
· Outliers;
· Univariate Analysis-The Exploratory Data Analysis (boxplot, five-number synthesis);
· Univariate Analysis - How to describe a distribution;
· Bivariate Analysis - The Correlation;
· Bivariate Analysis - The simple linear regression.
· Probability and normal distribution;
· Multivariate data analysis, basic concepts. Principal component analysis (PCA);
· Software di programmazione utili per la gestione, l'elaborazione, l'automazione, l'archiviazione la rappresentazione dei dati.
· Microsoft Office Word (word processing): document viewing, saving and printing; text formatting; insert images, tables, shapes, icons and graphics; layout and pagination; spell checking tools; references and review.
· How to write a Final Degree Project.
· Microsoft Office Excel (data calculation and analysis): spreadsheet display and saving; logical, search and reference, mathematical and trigonometric functions, relative and absolute references, conditional formatting, data sorting according to criteria, filters, graphs. Macros and databases.
· PowerPoint (presentations): document viewing and saving; slide management and object insertion; animated slideshows, transition effects, "slideshow" mode; do's and don'ts to make a presentation captivating. How to prepare the presentation of the Final Degree Thesis.
· Tools for bibliographic research.
Statistics unit
Introduction to statistics
· Definition, usefulness and uses of statistics;
· Basic concepts (population, sample, variable);
· Statistical language;
Data handling
· How to organize data;
· Secondary data and sources;
· Primary data and sampling;
· Survey techniques;
· Datasets, tables and graphs;
Data analysis
· Data distribution;
· Qualitative data organization;
· Quantitative data organization;
· The data central position indices (mean, median and mode);
· The scatter indices (range, mean absolute deviation, variance, standard deviation, interquartile range, properties of typical intervals)
· The shape indices for unit data and data grouped into classes (Pearson skewness index, first and second Pearson skewness coefficient, Fisher skewness index, Pearson-Fisher skewness coefficient, kurtosis index);
· The non-central position indices (z-scores, quartiles);
· Outliers;
· Univariate Analysis-The Exploratory Data Analysis (boxplot, five-number synthesis);
· Univariate Analysis - How to describe a distribution;
· Bivariate Analysis - The Correlation;
· Bivariate Analysis - The simple linear regression.
· Probability and normal distribution;
· Multivariate data analysis, basic concepts. Principal component analysis (PCA);
· Software di programmazione utili per la gestione, l'elaborazione, l'automazione, l'archiviazione la rappresentazione dei dati.
Prerequisites for admission
A good knowledge of spoken and written Italian is required
Teaching methods
Lectures are held in person with a large part spent in the lab. For both units (Computer Science and Statistics), the teaching includes both theoretical lectures (16+16 h) and exercises (16+16 h) for the practical application of the theoretical concepts covered during the lectures.
Only the statistics lectures are recorded on Microsoft Teams and made available only to students classified as workers.
For students with Specific Learning Disorders (SLD), the regulations provided by the University are followed (https://www.unimi.it/it/studiare/servizi-gli-studenti/servizi-studenti-con-dsa).
Only the statistics lectures are recorded on Microsoft Teams and made available only to students classified as workers.
For students with Specific Learning Disorders (SLD), the regulations provided by the University are followed (https://www.unimi.it/it/studiare/servizi-gli-studenti/servizi-studenti-con-dsa).
Teaching Resources
1. Slides and Lecture Notes;
2. Lecture recordings (statistics unit and working students only);
3. Sullivan III, M. (2018) Fundamentals of Statistics, Pearson: part 1, part 2.
2. Lecture recordings (statistics unit and working students only);
3. Sullivan III, M. (2018) Fundamentals of Statistics, Pearson: part 1, part 2.
Assessment methods and Criteria
The final test involves an assessment based on the performance of a written test. The test lasts 90 minutes and consists of 25 multiple-choice questions and 1 computer exercise in order to verify the use of the digital tools presented during the course. The final result will be expressed as "approved" or "not approved."
Exam registration will be via SIFA only and the application will be closed approximately 7 days before the test date. Students will have the opportunity to register about 14 days before the application closes.
Test results will be communicated via email.
Students with SLD (Specific Learning Disabilities) and with disabilities have to contact the Professor by email at least 10 days before the scheduled exam date to agree on any individualized measures. In the email addressed to the teacher it is necessary to put the respective University Services in CC: [email protected] (for students with SLD) and [email protected] (for students with disabilities).
Exam registration will be via SIFA only and the application will be closed approximately 7 days before the test date. Students will have the opportunity to register about 14 days before the application closes.
Test results will be communicated via email.
Students with SLD (Specific Learning Disabilities) and with disabilities have to contact the Professor by email at least 10 days before the scheduled exam date to agree on any individualized measures. In the email addressed to the teacher it is necessary to put the respective University Services in CC: [email protected] (for students with SLD) and [email protected] (for students with disabilities).
- University credits: 6
Computer room practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professors:
Campagnoni Fabio, Tugnolo Alessio
Educational website(s)
Professor(s)