DATA VISUALIZATION
Instructional goals
The course aims to develop advanced skills in designing and evaluating data visualizations for marketing applications. Students will learn how to transform complex data into clear, effective, and responsible visual representations, with particular attention to data integrity, interactivity, and strategic communication. The goal is to prepare professionals capable of selecting and building visualizations tailored to real-world decision-making contexts.
Intended learning outcomes
Knowledge and understanding: The course will provide the latest concepts, techniques, and tools to develop the ability to accurately and effectively illustrate data. Through the use of real-world marketing examples, students will learn how to extract, simplify, and communicate meaningful insights from raw data using modern data visualization tools. They will understand how to choose the most appropriate visual format depending on both the data type and the target audience. Applying knowledge and understanding: At the end of the course, the student is expected to: • Understand the principles of effective data representation (e.g., how to encode variables, use perceptual cues, design for clarity) • Analyze and critique visualizations based on their purpose and audience • Use modern visualization tools (e.g., TABLEAU, Power BI, KNIME) to design compelling data stories • Apply foundational principles of interaction design to create interactive web-based visualizations Making judgements: Students will develop strategic thinking about how to best visualize marketing data to simplify complexity while emphasizing key messages. They will also be able to recognize when visualizations distract, confuse, or mislead, and suggest improved alternatives to better represent the same information. Communication skills: Students will practice storytelling through data, learning to select the right visual strategies to communicate clearly and persuasively across diverse audiences. Communication will be fostered through class discussions, structured critique, and collaborative group work. Learning skills: The course will equip students with both conceptual and technical tools to independently explore data, identify trends and outliers, and derive strategic insights. By mastering visualization platforms, they will enhance their ability to extract value from data and support more informed decision-making in a marketing context.
Course Contents
- Foundations of data visualization in marketing - Visual perception, attention design, and communication clarity - Ethics, human-centered design, and data humanism - Data cleaning, reduction, and structuring - Visualization of multivariate, geographic, and network data - Visual storytelling and web-first design - Interactive dashboards - Funnel analysis, campaign performance, and customer behavior
Reference Books
Lecture notes, research papers and course material will be made avaible on the e-learning platform.
Teaching Methods
The course consists of lectures complemented by practical lab sessions and group project works.
Assessment Method
Individual assessment during course - 1/3 of the final grade Final individual exam + project discussion - 2/3 of the final grade
Thesis assignment criteria
To discuss with the professor.
Week 1
Introduction to the course and Fundamentals of Visual Thinking * Visual literacy in business: what makes a visualization strategic vs. descriptive * Comparing dashboards for exploration vs. communication; types of marketing data (campaign, funnel, CRM, web, social)
Week 2
Visual Perception and Ethics in Visualization * Cognitive load theory, pre-attentive attributes, visual attention models * Visual ethics: misleading techniques (cherry-picking, truncated axes, omission), and responsible framing of uncertainty
Week 3
Data Cleaning and Structuring for Visualization * Types of data errors (missing, duplicate, inconsistent); * Data shaping for visualization: aggregation, time formatting, categories
Week 4
Data Humanism and Ethical Representation * Introduction to data humanism, visualizing people not abstractions * Ethical framing and inclusion: showing uncertainty, absence, or nuance
Week 5
Visualizing High-Dimensional Data * Visual encoding for >2 variables: shape, color, facets, small multiples * Dimensionality reduction: PCA, clustering, and visual segmentation
Week 6
Exploratory Visual Analytics and Pattern Recognition * EDA concepts: pattern vs. noise, guided exploration, segmentation * Visual uncertainty and challenging hypotheses with new views
Week 7
Designing Interactive Dashboards Monitoring Tools * Understanding stakeholder roles and dashboard needs * Adding interactivity: slicers, bookmarks, conditional logic
Week 8
Geographic Data Visualization for Market Strategy * Spatial data types, choropleth vs proportional symbol maps * Pitfalls in map design: scale normalization, area bias, color gradients
Week 9
Visualizing Networks and Relational Data * Fundamentals of network graphs: nodes, edges, and structure * Visualizing social networks, customer influence paths, or product recommendation flows
Week 10
Designing for the Web: Interactive and Responsive Visuals * design web-first visual stories and dashboards. Topics include mobile responsiveness, interactivity, accessibility (e.g., color contrast, alt text), and export options. * experiment with layout tools and learn how to embed interactive visualizations in lightweight webpages.
Week 11
Customer Behavior and Lifecycle Visualization * Visualizing customer journeys: lifecycle grids, frequency plots * Detecting habits and anomalies: churn curves and segment patterns
Week 12
Visualizing Conversion Funnels and Campaign Performance * Visualizing marketing funnels: conversion steps, drop-offs, and metrics * Comparing campaign performance using time-based and segmented visuals