Data Analysis and Visualization

Recommended Prerequisites

Data Visualization.

Teaching Methods

Classes that combine theory and practice (TPs) are taught, presenting and thoroughly discussing concepts, methods, and visualization techniques for data analysis. In the second part of each class, practical demonstrations of applying theoretical contepts are conducted
through group exercises in the form of small workshops. The primary goal of the theoretical-practical classes is to establish the connection between theoretical concepts and their practical application.
Additionally, practical classes (PLs) are also taught with the aim of developing a set of skills that enable the creation of effective analytical visualization applications. Emphasis is placed on the development of projects that apply fundamental concepts in creating solutions for visual analysis of large volumes of data.

Learning Outcomes

This course provides a comprehensive understanding of data visualization techniques, tools, and best practices while emphasizing practical skills through hands-on project and case studies. In particular, state-of-the-art visualization tehcniques for visualization of tabular, multivariate, geo-referenced, and time-oriented data are studied. Aditionally, advanced techniques for visualization of networks are considered.
By the end of the course, students will have theoretical knowledge and practical skills in designing and implementing visualizations to effectively analyse data. The students will aqcuire necessary skills to design, implement, test, and validate visualizations using industrystandard tools and best practices.
Main competencies to be developed are: analysis and synthesis; problem solving; critical thinking; practical application of the theoretical knowledge; research.
Secondary competences are: organizing and planning; work in teams; autonomous learning; creativity.

Work Placement(s)

No

Syllabus

1. Introduction
a. Overview of concepts
b. Introduction to tools
c. Principles of visual perception and cognition
d. Basic visualization types
2. Advanced Techniques
a. Advanced visualization for tabular data
b. Techniques for multivariate data analysis
c. Dimensionality reduction and visualization
3. Visualization for spatial and temporal data
a. Spatial structures
b. Temporal structures
c. Spatio-temporal structures
4. Visualization for networks and trees
a. Node-link diagrams
b. Adjacent matrix
c. Hierarchy marks
5. Exploratory Data Analysis
a. Exploring relationships between data variables
b. Identifying patterns, trends, and anomalies
6. Principles of Visualization Design
a. Gestalt principles and visual design
b. Colour theory
c. Use of visual channels
d. Validation
7. Interactive Visualization and Dashboards a.User tasks and interaction; b.Visual analytics and interactive visualization; c.Construction of analytical applications d. Dashboard design.

Head Lecturer(s)

Evgheni Polisciuc

Assessment Methods

Assessment
Exam: 30.0%
Project: 70.0%

Bibliography

Munzner, T.: Visualization Analysis and Design, 2014.

Cairo, A.: The Art of Insight: How Great Visualization Designers Think, John Wiley & Sons, 2023.

Tufte, E.: Seeing with Fresh Eyes: Meaning, Space, Data, Truth, Graphics Press LLC, 2020.

Meirelles, I: Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. Rockport Publishers, 2013

Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press.

Schwabish, Jonathan. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press, 2021