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Web Applications

Thu, 08 Aug 2024 11:00:00 +0000

Aims

This course introduces the foundations of full-stack web application development through two complementary areas: Frontend Development and Backend Development.

On the frontend, students learn how to create structured, interactive, and accessible user interfaces using HTML, CSS, and JavaScript. Topics include semantic markup, responsive layout, form handling, client-side validation, and media embedding. Students progressively develop skills in DOM manipulation, event-driven programming, and asynchronous operations.

On the backend, the course introduces Node.js and the Express.js framework for building RESTful services, managing middleware, and handling persistence with SQLite. By the end of the course, students are able to design and implement full-stack applications that integrate client-side interfaces with server-side logic and data storage.

Hands-on activities with cURL, Postman, and structured assignments support practical learning throughout the course.

Teacher

Michael Soprano - Course Instructor

The course consists of 24 lectures for a total of 48 hours. It is delivered in the second semester. Assessment is based on the design, implementation, and discussion of a full-stack project.

Topics Covered

Web Fundamentals and Communication

Architecture of the World Wide Web
Client-server model and user agents
Domain names, IP addressing, and URLs
The HTTP protocol and the request-response lifecycle
Methods, status codes, headers, and metadata
URL query strings and GET parameter passing
Data exchange with JSON
Practical use of cURL and Postman

Frontend Development

HTML: Structure and Semantics

Core syntax and document structure
Headings, paragraphs, and text-level semantics
Lists, tables, links, and images
The DOM and the HTML parsing model
Embedding external and multimedia content with <iframe>, <video>, and <audio>

HTML Forms and Validation

Input types such as text, email, number, date, checkbox, and radio
Structural and grouping elements in forms
<textarea>, <select>, and option handling
HTML5 validation attributes and input constraints
Form design best practices

CSS: Styling and Layout

Basic syntax, selectors, and combinators
Cascading rules, specificity, and inheritance
The box model: margin, padding, border, sizing, and overflow
Typography: fonts, spacing, and line height
Color management and transparency
Responsive layout with Flexbox
Positioning, layout strategies, and component patterns

JavaScript: Behavior and Interaction

Core language constructs and syntax
Script inclusion and module structure
DOM selection, traversal, and manipulation
Event handling and listener registration
Event flow: bubbling and capture
Asynchronous JavaScript with callbacks, promises, and the Fetch API
Case study: calling a public API

Backend Development

Node.js and Express.js

Introduction to Node.js and non-blocking I/O
Setting up an Express.js server
Defining routes and handling HTTP methods
Sending responses and managing request data
Middleware architecture and request pipelines
Building RESTful endpoints
Interfacing with SQLite for data persistence
Final integration project with full-stack CRUD operations

Learning Approach

The course combines conceptual explanations, practical demonstrations, and progressive hands-on activities. Students use developer tools, cURL, Postman, browser APIs, and server-side JavaScript to connect frontend interfaces with backend services.

Assignments and exercises gradually move from isolated examples to complete application components. The final project consolidates the main topics of the course through the implementation of a full-stack application.

Assessment

Assessment is based on a project in which students design and implement a full-stack web application. The project is discussed to assess design choices, implementation quality, and understanding of the technologies used.

Reading Material

J. Duckett, HTML & CSS: Design and Build Websites. Wiley, 2011
J. Duckett, JavaScript & jQuery: Interactive Front-End Web Development. Wiley, 2014
E. Holmes, D. Harms, Express in Action: Writing, Building, and Testing Node.js Applications. Manning Publications, 2016

Information Analysis and Processing for Training

Wed, 07 Aug 2024 11:00:00 +0000

Aims

These courses address the analysis and processing of information for sport and training from two complementary perspectives. They share the same broad teaching area, but they are distinct courses offered at different degree levels and organized around different learning goals, tools, and assessment methods.

The Bachelor’s Degree course introduces the foundations needed to work with data in sport contexts. Students learn what data are, how information is represented in digital systems, and how structured datasets can be prepared, analyzed, summarized, and communicated with spreadsheet tools.

The Master’s Degree course develops a more structured and reproducible view of data work. Students learn how to frame data mining problems, design relational databases, implement them with a DBMS, and query them with SQL to obtain reusable datasets.

The two courses follow a coherent progression from understanding and representing data to organizing, querying, and reusing data in controlled workflows. Their shared goal is to help students transform raw data into meaningful information that can support interpretation and decision making in sport and training contexts.

Courses

Bachelor’s Degree Course: foundational data concepts, digital representation of information, and spreadsheet based data analysis with Microsoft Excel
Master’s Degree Course: data mining foundations, relational database design, database implementation, and SQL querying

Teacher

Michael Soprano - Course Instructor

The two courses consist of 12 lectures for a total of 24 hours.

Bachelor’s Degree Course

The Bachelor’s Degree course introduces the basic vocabulary and tools needed to work with data in sport and training contexts. Students first learn how to describe data, variables, and measurements. They then study how different kinds of information are represented digitally. The final part of the course focuses on Microsoft Excel as a tool for preparing, analyzing, visualizing, and summarizing structured data.

Topics Covered

Module 1 - Introduction to Data Science in Sport

Data, information, knowledge, and the DIKW pyramid in sport and training contexts
Structured and unstructured data from sport-related sources
Sensors, SportsTech, and examples of data-driven decision making in sport
From raw data to information through preparation, analysis, visualization, and communication
Tables, observations, variables, and values
Populations, samples, units of analysis, and levels of measurement
Qualitative and quantitative variables
Sport-related data types, including biometric, position and movement, subjective, and performance data

Module 2 - Representation and Management of Data

Basic computer architecture: memory, storage, peripherals, CPU, and operating system
Data storage in the filesystem
Files, folders, metadata, hierarchical organization, and paths
Bits, bytes, binary code, and digital encodings
Text: character sets, encodings, ASCII, Unicode, and UTF-8
Images: raster graphics, vector graphics, color representation, and main file formats
Sound: wave characteristics, sampling, quantization, channels, MIDI messages, and audio formats
Video: sequences of images, movement, frame rate, resolution, and video formats
The Shannon-Weaver model of communication
Entropy and redundancy of information
Why data compression is useful
Lossless and lossy compression

Module 3 - Data Analysis with Microsoft Excel

Elements of the Excel interface, workbooks, and worksheets
Managing cells, rows, columns, and ranges
Entering, editing, deleting, and formatting data
Applying numeric, date, time, text, and currency formats
Working with contiguous, non-contiguous, and multi-sheet ranges
Managing rows and columns and navigating large worksheets
Relative, absolute, and mixed references
Introduction to formulas and functions
Formula syntax: references, operators, precedence, and common errors
Function syntax: arguments, result types, nesting, and basic summary functions
Practical exercise on Boston Marathon 2025 data, including data cleaning, derived columns, summary statistics, and a mini dashboard
Importing structured data from text files, including delimiters, encodings, and common import problems
Introduction to Power Query for importing and preparing data
Working with Excel tables
Creating, managing, and formatting charts
Examples with line, scatter, and area charts
Basic measures of correlation
Introduction to PivotTables and PivotCharts
Using PivotTables to summarize and explore Fitbit data

Learning Approach

The Bachelor’s Degree course is organized around foundational concepts and applied spreadsheet activities. Students first acquire the vocabulary needed to describe datasets, then study how different types of information are represented digitally, and finally practice data analysis in Excel through guided examples and exercises.

Assessment

The Bachelor’s Degree course is assessed through a written exam with multiple-choice questions.

Reading Material

Peter O’Donoghue, Lucy Holmes, Data Analysis in Sport. Routledge Studies in Sports Performance Analysis, First edition, 2014
Michael Alexander, Dick Kusleika, Excel 365 Bible. Wiley, First edition, 2022

Master’s Degree Course

The Master’s Degree course moves toward structured, reproducible, and technically controlled data workflows. The course connects data mining, relational modeling, database implementation, and SQL querying.

Topics Covered

Preparatory Recall - Data Science Foundations

Data, information, and knowledge in reproducible data workflows
Sport-related data types, including biometric, movement, subjective, and performance data
Tables as representations of observations, variables, and values
Units of analysis, variables, scales of measurement, and metadata
Data quality, coherent types, identifiers, and first normal form
Privacy and protection of personal and health-related data

Module 1 - Introduction to Data Mining

Data science as a reproducible process from data to decision
Project workflow: understanding, preparation, exploration, modeling, interpretation, and deployment
Populations, samples, representativeness, and sampling error
Variables, features, targets, and units of analysis
Problem formulation in sport-related scenarios
Typical data mining tasks: classification, regression, clustering, association, anomaly detection, and time series analysis
Learning paradigms: supervised, unsupervised, and other basic settings
Baselines, training and test data, validation strategies, and evaluation on unseen data
Metrics for model evaluation and comparison
Methodological issues: missing values, outliers, temporal consistency, leakage, imbalance, and data quality
Interpretability, reproducibility, and documentation of modeling choices

Module 2 - Relational Databases

Databases as persistent, coherent, and shared collections of data
DBMSs and the role of persistence, scale, globality, reliability, efficiency, and privacy
Conceptual, logical, and physical levels of database design
Entity-Relationship modeling: entities, attributes, relationships, cardinalities, identifiers, and constraints
Binary, recursive, and n-ary relationships
Hierarchies and alternative modeling choices in ER schemas
Relational model: relations, tables, tuples, attributes, domains, schemas, instances, and NULL values
Relational constraints: domains, primary keys, uniqueness, and referential integrity
Translation from ER schemas to relational schemas
Many-to-many, one-to-many, one-to-one, and recursive relationships in the relational model
Redundancy, update anomalies, insertion anomalies, and deletion anomalies
Normalization up to 3NF as an operational method for reducing uncontrolled redundancy
Practical case study on road cycling races, from requirements to ER schema and relational schema
SQLite as an embedded relational DBMS based on a single database file
DBeaver as a graphical client for exploring schemas, tables, data, and relationships
SQL scripts and controlled population of a database
DDL for schema definition: CREATE TABLE, DROP TABLE, ALTER TABLE, types, keys, and constraints
DML for data manipulation: INSERT, UPDATE, and DELETE
SQL queries for data retrieval: SELECT, FROM, WHERE, ORDER BY, LIMIT, AS, and calculated columns
Aggregations and grouping with COUNT, MIN, MAX, AVG, SUM, GROUP BY, and HAVING
Joining tables to integrate information, including JOIN and LEFT JOIN
Exporting query results as reusable datasets for subsequent analysis and reporting

Learning Approach

The Master’s Degree course emphasizes structured and reproducible workflows. Students connect data science concepts to relational database design, SQL extraction, and reusable data preparation. The course combines conceptual modeling, database implementation, query writing, and the interpretation of database outputs as datasets for further analysis.

Assessment

The Master’s Degree course is assessed through a written exam with open-ended questions.

Reading Material

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, James R. Foulds, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Fourth edition, 2016
Giorgio M. Di Nunzio, Emanuele Di Buccio, Basi di dati. Progettazione concettuale, logica e SQL. Esculapio, First edition, 2017

Social Computing

Tue, 06 Aug 2024 11:00:00 +0000

Aims

This course introduces the foundations of social computing through two complementary perspectives: social behavior supported by computational systems, and computational systems supported by coordinated human activity. It covers both social media platforms and crowdsourcing environments, combining conceptual foundations with practical tools and hands-on activities.

Students are introduced to social media APIs, network analysis, crowdsourcing systems, and practical tools for experimentation and data analysis. The course is taught in Italian.

Main Topics

Social media APIs and data access
Network analysis and graph structures
Crowdsourcing platforms and task design
Practical tools such as Python, Jupyter, Postman, Angular, and Crowd_Frame

Teaching Team and Contribution by Academic Year

Academic Year 2020/2021

Teaching Team

Stefano Mizzaro - Course Instructor
Michael Soprano - Teaching Assistant

My Contribution

I contributed to the course by teaching the practical applications of the concepts introduced by Professor Stefano Mizzaro in six lectures (12 hours). The practical lectures covered social media APIs, Python-based data access, network analysis with NetworkX, and the use of Crowd_Frame for crowdsourcing task design. I also prepared and assigned course work for the students.

Academic Year 2021/2022

Teaching Team

Stefano Mizzaro - Course Instructor
Michael Soprano - Teaching Assistant

My Contribution

I contributed to the course by teaching the practical applications of the concepts introduced by Professor Stefano Mizzaro in six lectures (12 hours). The practical lectures covered Python and REST fundamentals, social media API access, graph analysis with NetworkX, and crowdsourcing task design with Crowd_Frame. I also prepared and assigned course work for the students.

Academic Year 2022/2023

Teaching Team

Stefano Mizzaro - Course Instructor
David La Barbera - Teaching Assistant
Michael Soprano - Teaching Assistant

My Contribution

I contributed to the course by delivering two lectures on Crowd_Frame, a framework I developed for the design and deployment of crowdsourcing tasks (4 hours). I also prepared the second course assignment for the students.

Academic Year 2023/2024

Teaching Team

Stefano Mizzaro - Course Instructor
David La Barbera - Teaching Assistant
Michael Soprano - Teaching Assistant

My Contribution

Academic Year 2024/2025

Teaching Team

Stefano Mizzaro - Course Instructor
Hafsa Akebli - Teaching Assistant
Michele Lizzit - Teaching Assistant
Michael Soprano - Teaching Assistant

My Contribution

I contributed to the course by delivering a lecture and in-class activity on Crowd_Frame, a framework I developed for the design and deployment of crowdsourcing tasks (2 hours).

Academic Year 2025/2026

Teaching Team

Stefano Mizzaro - Course Instructor
Michael Soprano - Teaching Assistant

My Contribution

I contributed to the course by delivering one lecture on Crowd_Frame, a framework I developed for the design and deployment of crowdsourcing tasks (2 hours). The lecture introduced the crowdsourcing workflow, discussed the limits of platform-specific task design, and showed how Crowd_Frame can support the creation, deployment, and management of complex crowdsourcing tasks. It also included a hands-on activity on the configuration of a truthfulness assessment crowdsourcing task.

Additional Materials

Crowd_Frame: GitHub Repository