Computer Knowledge and E-Skills
A.Y. 2019/2020
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 cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
First semester
Course syllabus
The course (48h) consists of four modules and aims at providing students with basic knowledge and skills in computer science, social informatics and statistics.
Module 1 - Understanding New Media
- Media, New Media and Computer-mediated Communication;
- Web 2.0 and Social Media;
- Identity and Social Media;
- Web's Risks and Opportunities;
- Digital Inequalities and Digital Divide;
- Digital Skills and Benefits.
Tools: Google Docs, LinkedIn.
Module 2 - Getting the Information You Need
- What is Statistics?
- Basic Concepts;
- Data Collection: Sources;
- Data Collection: Sampling;
- Data Collection: Techniques and Innovative Tools.
Tools: Dati Istat, Google Trends, Landbot.io, Google Forms.
Module 3 - The Importance of Descriptive Statistics
- Qualitative and Quantitative Data organization: Tables, Grouped Frequency Distribution and Graphs;
- Measures of Central Tendency;
- Measures of Dispersion;
- Measures of Central Tendency and Dispersion from Grouped data;
- Measures of Position, Outliers and Explorative Data Analysis (Five-number Summary and Box-and-whisker Plots);
- Bivariate Analysis: Scatterplot and Pearson Correlation Coefficient;
- Bivariate Analysis: Squares Regression;
Tools: Microsoft Excel and Google Sheet (Data Organization and Formatting, Statistical Functions, Graphs, Pivot Tables, Macros, VBA/Google Apps Script), BoxPlotR, Canva.
Module 4 (self-learning) - Discovering Innovation: AR and AI
- Agriculture 4.0;
- Environmental IT;
- Robotics for an Eco-sustainable World;
- Artificial Intelligence and Augmented Reality for the Mountain Areas.
Module 1 - Understanding New Media
- Media, New Media and Computer-mediated Communication;
- Web 2.0 and Social Media;
- Identity and Social Media;
- Web's Risks and Opportunities;
- Digital Inequalities and Digital Divide;
- Digital Skills and Benefits.
Tools: Google Docs, LinkedIn.
Module 2 - Getting the Information You Need
- What is Statistics?
- Basic Concepts;
- Data Collection: Sources;
- Data Collection: Sampling;
- Data Collection: Techniques and Innovative Tools.
Tools: Dati Istat, Google Trends, Landbot.io, Google Forms.
Module 3 - The Importance of Descriptive Statistics
- Qualitative and Quantitative Data organization: Tables, Grouped Frequency Distribution and Graphs;
- Measures of Central Tendency;
- Measures of Dispersion;
- Measures of Central Tendency and Dispersion from Grouped data;
- Measures of Position, Outliers and Explorative Data Analysis (Five-number Summary and Box-and-whisker Plots);
- Bivariate Analysis: Scatterplot and Pearson Correlation Coefficient;
- Bivariate Analysis: Squares Regression;
Tools: Microsoft Excel and Google Sheet (Data Organization and Formatting, Statistical Functions, Graphs, Pivot Tables, Macros, VBA/Google Apps Script), BoxPlotR, Canva.
Module 4 (self-learning) - Discovering Innovation: AR and AI
- Agriculture 4.0;
- Environmental IT;
- Robotics for an Eco-sustainable World;
- Artificial Intelligence and Augmented Reality for the Mountain Areas.
Prerequisites for admission
No prior knowledge required.
Teaching methods
Classes are participatory and supported by digital tools, authentic learning activities, gamification and webquests.
Teaching Resources
- Slides;
- Stella, R., Riva, C., Scarcelli C.M. & Drusian, M. (2018) Sociologia dei New Media, UTET: 59-114;
- Sullivan III, M. (2018) Fundamentals of Statistics, Pearson: part 1, part 2;
- D'Avanzo, W. (2019) "Le applicazioni dell'intelligenza artificiale a tutela dell'ambiente", in: Diritto e Giurisprudenza Agraria, Alimentare e dell'Ambiente, 2/2019;
- Quarato, C. (2018) FerMentor connected system: a sustainable approach for innovating traditional farms. Master of Science Thesis (Polytechnic University of Turin): 13-59.
Tools: Microsoft Excel, Google Sheets, Google Docs, Canva, Dati Istat, Google Trends, Google Forms, Landbot.io, LinkedIn, BoxPlotR.
All course materials, including slides, exercises, videos and exam simulations, are available at https://alessandroiannella.com/info-edolo/2019.
Non-attending students can replace module 1 with one of the following books:
- Fabris, A. (2018) Etica per le tecnologie dell'informazione e della comunicazione, Carocci;
- Floridi, L. (2017) La quarta rivoluzione. Come l'infosfera sta trasformando il mondo, Raffaello Cortina Editore;
- Tegmark, M. (2018) Vita 3.0. Essere umani nell'era dell'intelligenza artificiale, Raffaello Cortina Editore;
- De Biase, L. (2018) Il lavoro del futuro, Codice Edizioni;
- Fontana, A. (2017) #iocredoallesirene. Come vivere (e bene!) in un mare di fake news, HOEPLI;
- Calvani, A., Bonaiuti, G., Vivanet, G. & Menichetti, L. (2017) Le tecnologie educative, Carocci;
- Rivoltella, P. C. (2015) Le virtù del digitale. Per un'etica dei media, Morcelliana.
- Stella, R., Riva, C., Scarcelli C.M. & Drusian, M. (2018) Sociologia dei New Media, UTET: 59-114;
- Sullivan III, M. (2018) Fundamentals of Statistics, Pearson: part 1, part 2;
- D'Avanzo, W. (2019) "Le applicazioni dell'intelligenza artificiale a tutela dell'ambiente", in: Diritto e Giurisprudenza Agraria, Alimentare e dell'Ambiente, 2/2019;
- Quarato, C. (2018) FerMentor connected system: a sustainable approach for innovating traditional farms. Master of Science Thesis (Polytechnic University of Turin): 13-59.
Tools: Microsoft Excel, Google Sheets, Google Docs, Canva, Dati Istat, Google Trends, Google Forms, Landbot.io, LinkedIn, BoxPlotR.
All course materials, including slides, exercises, videos and exam simulations, are available at https://alessandroiannella.com/info-edolo/2019.
Non-attending students can replace module 1 with one of the following books:
- Fabris, A. (2018) Etica per le tecnologie dell'informazione e della comunicazione, Carocci;
- Floridi, L. (2017) La quarta rivoluzione. Come l'infosfera sta trasformando il mondo, Raffaello Cortina Editore;
- Tegmark, M. (2018) Vita 3.0. Essere umani nell'era dell'intelligenza artificiale, Raffaello Cortina Editore;
- De Biase, L. (2018) Il lavoro del futuro, Codice Edizioni;
- Fontana, A. (2017) #iocredoallesirene. Come vivere (e bene!) in un mare di fake news, HOEPLI;
- Calvani, A., Bonaiuti, G., Vivanet, G. & Menichetti, L. (2017) Le tecnologie educative, Carocci;
- Rivoltella, P. C. (2015) Le virtù del digitale. Per un'etica dei media, Morcelliana.
Assessment methods and Criteria
The exam (2 hours) consists of twelve questions, three for each module. The questions can be open questions or exercises.
Each question is evaluated with a score ranging from 0 to 1. To pass the exam it is required to obtain a minimum score of 1.8 /3 in each module.
Passed modules will remain valid for the entire academic year. Unsuccessful modules can also be taken orally.
Each question is evaluated with a score ranging from 0 to 1. To pass the exam it is required to obtain a minimum score of 1.8 /3 in each module.
Passed modules will remain valid for the entire academic year. Unsuccessful modules can also be taken orally.
- University credits: 6
Lessons: 48 hours
Professor:
Iannella Alessandro
Shifts:
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Professor:
Iannella Alessandro