Explore interactive visual analytics techniques for handling large datasets in this GOTO 2014 conference talk. Dive into the technical and human factor challenges of building effective big data analytic solutions for data scientists and analysts. Learn about interactive analysis support, visualizations for big data, scripting requirements, and addressing typical analyst task flows. Discover dynamic data visualization methods and fundamental principles of good visual design. Examine case studies demonstrating the application of these concepts in real-world scenarios. Gain insights into integrated analysis visualization environments, serverside processing, pattern matching, and various analysis techniques such as aggregation, opacity, and heatmaps. Understand the importance of visual design principles, including Edward Tufte's rules and techniques for managing data complexity. Explore tools for querying large datasets, auto-updates, and creating analyst-friendly interfaces. Acquire practical knowledge to enhance your ability to work with and derive insights from big data through interactive visual analytics.
Explorations in Interactive Visual Analytics
Overview
Syllabus
Introduction
Agenda
Big Data
Data Scientist
IV Workbench
Challenges
Case Study
Exploring Relationships
What We Learned
Integrated Analysis Visualization Environment
Data Visualization
The Latch Model
Leverage Serverside Processing
Example Case Study
Rendering on Clients
Pattern Matching
Analysis Techniques
Aggregation
Opacity
Scatter
Heatmap
Histogram
Results
Visual Design
Edward Tufte
The 7 Rule
Top 80 Keywords
Gaps
Filters
Plot Ratio
Hiding Controls
Data Complexity
Managing Data Complexity
Large Data Sets
Query Tool
Auto Updates
Execution
Scripting
Analyst Interface
Summary
Taught by
GOTO Conferences
