Data Visualization

Data Visualization

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Duration

2 Days

Certified Instructor

Course Id

nextec1206

Course Description

Course Overview

The Data Visualization course offers a comprehensive study of techniques and algorithms that enable the creation of effective visualizations. Drawing upon principles from graphic design, visual art, perceptual psychology, and cognitive science, this course provides students with the knowledge and skills needed to generate impactful visual representations of data. The course covers various aspects of data visualization, including data exploration, design principles, visualization tools, and storytelling with data. Students will learn how to create compelling visualizations, choose appropriate visual encodings, and present data-driven narratives.

Prerequisites

  • Basic understanding of data analysis and statistics
  • Familiarity with spreadsheets and data manipulation
  • Some programming skills (preferred but not mandatory)
  • Curiosity and interest in data visualization and storytelling

Methodology

The course adopts a combination of theoretical lectures, hands-on exercises, and practical labs to provide a comprehensive learning experience. The lectures cover the fundamental concepts and principles of data visualization. The hands-on exercises allow students to practice creating visualizations using various tools and techniques, while the labs provide opportunities to work on data visualization projects. The course encourages creativity, critical thinking, and effective communication of data insights.

Course Outline

Introduction to Data Visualization

Overview of data visualization and its importance

Perception and cognition in visual representation

Ethical considerations in data visualization

Exploratory Data Visualization

Basic visualization techniques (e.g., bar charts, scatter plots)

Data mapping and visual encodings

Interactive visualizations and user engagement

Data Visualization Design Principles

Gestalt principles and visual perception

Color theory and effective use of color in visualization

Typography and layout considerations

Visualization Tools and Technologies

Introduction to popular visualization tools (e.g., Tableau, D3.js)

Choosing the right tool for different visualization tasks

Integrating visualizations into web and mobile applications

Chart Types and Advanced Visualizations

Advanced chart types (e.g., heatmaps, treemaps, network diagrams)

Hierarchical and temporal visualizations

3D visualizations and interactive storytelling

Interactive Data Dashboards

Designing and building interactive dashboards

Filtering, drilling down, and highlighting data

Dashboard usability and user experience (UX) considerations

Storytelling with Data

Data narrative and story structure

Creating a compelling data-driven narrative

Visualizing data to support storytelling

Data Visualization Best Practices and Critique

Evaluation and critique of visualizations

Ethical considerations in data visualization

Data visualization trends and emerging technologies

Outcome

Upon completing the course, students will:

  • Understand the fundamental principles, techniques, and best practices of data visualization.
  • Be proficient in creating effective visualizations using various chart types and visual encodings.
  • Gain hands-on experience with popular data visualization tools and technologies.
  • Develop skills in designing interactive dashboards and data-driven narratives.
  • Enhance their ability to effectively communicate insights and tell stories with data.
  • Apply data visualization techniques to present data-driven narratives and support decision-making processes.

Labs

The course includes hands-on labs and projects to reinforce the theoretical concepts and provide practical experience. The labs may include:

  • Creating basic visualizations (e.g., bar charts, scatter plots) using a visualization tool.
  • Designing and building interactive data dashboards to explore and analyze datasets.
  • Developing custom visualizations using JavaScript and D3.js library.
  • Critiquing and evaluating existing visualizations for their effectiveness and clarity.
  • Creating a data-driven narrative and visualizing it using storytelling techniques.
  • Designing and presenting a comprehensive data visualization project.

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