Visual Analytics for Beginners | IABAC

Visual analytics for beginners involves using data visualization tools to interpret complex data. It combines statistical analysis with interactive visual representations, enabling users to uncover patterns, trends, and insights. Key skills include using charts, graphs, and dashboard software for effective analysis.

1. iabac.org
Visual Analytics
for Beginners

2. Table Of Contents
What is Visual Analytics?
Why Visual Analytics is Important
Key Concepts in Visual Analytics
Common Visualization Techniques
Tools for Visual Analytics
Step-by-Step Guide to Getting Started
The Future of Visual Analytics
iabac.org

3. Definition: Visual analytics combines data
visualization and analysis to understand and
interpret data.
Purpose: Converts raw data into visual formats
(charts, graphs) to make data more accessible
and actionable.
Key Benefit: Helps identify patterns and insights
that are hard to spot in raw data.
iabac.org
What is Visual Analytics?

4. Clarity: Simplifies complex datasets.
Speed: Visuals enable quicker decision-making by highlighting key
insights.
Communication: Makes findings easy to understand for non-
technical audiences.
Pattern Recognition: Reveals trends and correlations in data that might
go unnoticed.
Why Visual Analytics is Important
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5. Data Types:
Numerical (Quantitative): e.g., age, income.
Categorical (Qualitative): e.g., gender,
nationality.
Time-series Data: e.g., stock prices over time.
Visualization Techniques:
Bar Charts, Line Graphs, Pie Charts, Scatter
Plots, Heatmaps.
Key Concepts in Visual Analytics
iabac.org

6. Bar Charts: Compare categories or groups.
Line Graphs: Show trends over time.
Pie Charts: Display proportions of a whole.
Scatter Plots: Highlight correlations between
two variables.
Heatmaps: Show patterns in large datasets.
Common Visualization Techniques
iabac.org

7. Microsoft Excel: Ideal for beginners with built-in charts and pivot tables.
Tableau: Powerful for interactive dashboards and real-time visualizations.
Google Data Studio: Free tool for creating dashboards from multiple
data sources.
Power BI: Business intelligence tool for combining multiple datasets.
Python : Advanced, customizable visualizations for coders.
Tools for Visual Analytics
iabac.org

8. Collect and Understand Data
Choose the Right Visualization
Create the Visualization
Add Interactivity
Interpret Results.
Step-by-Step Guide to Getting Started
iabac.org

9. AI and Machine Learning: Emerging tools will automatically
suggest visualizations and detect patterns.
Importance: Visual analytics is increasingly crucial in data
science and decision-making.
Certification: Programs like IABAC help professionals build
skills and advance their careers in this field.
The Future of Visual Analytics
iabac.org

10. Visit www.iabac.org
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