Introduction to Network Analysis

General information

Type:	OPT	Curs:	3,4	Period:	S semester
ECTS Credits:	3 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 3	Marc Lemus Font	Operaciones, Innovación y Data Sciences	ENG

Group	Teacher	Department	Language
Year 4	Marc Lemus Font	Operaciones, Innovación y Data Sciences	ENG

Prerequisites

While not mandatory prerequisites, certain courses can provide a solid foundation that will enhance their comprehension of the material covered in this course. The following courses are recommended as they offer relevant background knowledge:
- Computing Foundations (BBA & BAIB)
- Information Systems (BBA)

Previous Knowledge

A basic understanding of calculus and algebra, as well as basic Python programming, is required. While the course will cover additional mathematical concepts, no prior knowledge of these topics is necessary as the course is designed to be self-contained.

COURSE CONTRIBUTION TO PROGRAM

Introduction to Network Analysis offers a first approach and valuable insights into the field of network analysis. The course highlights the importance of understanding and leveraging networks to uncover hidden patterns, relationships, and structures within complex data sets. By exploring the fundamental tools of analysis and the underlying mathematics of networks, particularly graph theory, the course will provide a solid foundation for conducting network analysis.

Course Learning Objectives

The aim of the course is to provide the necessary tools to understand networks and be able to conduct a fundamental analysis. At the end, students should:

1. Develop a solid understanding of the principles and concepts of graph theory.
2. Acquire the skills in utilizing a range of analytical tools and centrality measures.
3. Gain the capacity to comprehend the underlying structure of networks, including their connectivity patterns, hierarchical organization, and modular nature.
4. Be able to identify the dynamic nature of networks and comprehend the processes and mechanisms that drive their evolution over time.
5. Develop the skills to conduct comprehensive fundamental network analysis.

Methodology

The course will employ a blended learning approach, combining theoretical lectures with practical sessions to ensure comprehensive understanding and application of the course content. During the theoretical lectures, fundamental concepts and principles for each topic will be introduced. Practice sessions will be dedicated to reinforcing these concepts through hands-on exercises and case studies directly relevant to the topic at hand.

Active participation from all course participants is essential for optimal learning outcomes, and active engagement will be encouraged and expected throughout the course duration.

Assessment criteria

The evaluation process will consider the following key aspects, each of which contributes significantly to the overall assessment of the course:

- Course Engagement and Participation. Active engagement and participation in the course are crucial factors. Regular attendance, active involvement in class discussions, and meaningful contributions to collaborative activities will be taken into account to assess the level of course engagement.

- Assignments. Tasks will be assigned throughout the course to gauge students' comprehension and application of the course material. These assignments will encompass a range of activities, such as problem-solving exercises, case studies, and data analysis tasks.

- Final Exam. A comprehensive final exam will be administered to assess the overall understanding and mastery of the course content. The exam will cover the key concepts, methodologies, and analytical techniques taught during the course.

By considering these three factors, the evaluation process will determine students' progress and achievements throughout the course, ensuring a fair and comprehensive assessment of their knowledge and skills in network analysis.

Bibliography

- Newman, M. (2010). "Networks: An Introduction." Oxford University Press.
- Barabási, A.-L. (2016). "Network Science." Cambridge University Press.
- Wasserman, S., & Faust, K. (1994). "Social Network Analysis: Methods and Applications." Cambridge University Press.
- Easley, D., & Kleinberg, J. (2010). "Networks, Crowds, and Markets: Reasoning About a Highly Connected World." Cambridge University Press.

Timetable and sections

Group	Teacher	Department
Year 3	Marc Lemus Font	Operaciones, Innovación y Data Sciences

Timetable Year 3

From 2024/1/9 to 2024/1/26:
Tuesday, Thursday and Friday from 14:45 to 17:15.

Group	Teacher	Department
Year 4	Marc Lemus Font	Operaciones, Innovación y Data Sciences

Timetable Year 4

From 2024/1/9 to 2024/1/26:
Tuesday, Thursday and Friday from 14:45 to 17:15.

Introduction to Network Analysis (2235.YR.015766.1)