Visualization for Data Science - Creating Views and Dashboards

Overview

Master data visualization techniques for creating impactful views and dashboards, focusing on best practices for effective data presentation and communication in data science applications.

Syllabus

• Introduction to Data Visualization
Importance of data visualization in data science
Overview of visualization tools and software
• Principles of Effective Data Visualization
Understanding the audience and context
Selecting the right charts and graphs
Using color, labels, and annotations effectively
Avoiding common pitfalls and biases
• Exploring Visualization Tools
Introduction to popular visualization tools (e.g., Tableau, Power BI, Plotly)
Hands-on exercises with selected tools
• Designing Compelling Views
Building interactive data dashboards
Balancing detail with simplicity
Incorporating storytelling in data visualization
• Advanced Visualization Techniques
Utilizing advanced chart types (e.g., heatmaps, treemaps, network graphs)
Interactive and dynamic data visualizations
Visualizing complex data sets (e.g., geospatial data, time series)
• Best Practices for Dashboard Design
Dashboard layout and organization
Optimizing for user experience and accessibility
Case studies of effective dashboards
• Data Ethics and Visualization
Ensuring accuracy and honesty in visual representations
The impact of misleading visuals
• Hands-On Project: Building a Data Dashboard
Project setup and data selection
Step-by-step design and implementation
Peer review and feedback session
• Course Summary and Next Steps
Review of key concepts and skills learned
Resources for further learning and exploration in data visualization
Guidance on building a portfolio or using visualizations in professional work

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Subjects

Data Science