Fundamentals of data visualization

Data Mining and Data Visualization


Unit 6: Fundamental of Data Visualization

Introduction to Data Visualization and Analytics

Data Visualization is the graphical representation of information and data
using visual elements like charts, graphs, maps, and dashboards. It helps
make complex data more accessible, understandable, and usable.

Key Objectives:

 Communicate data clearly and effectively
 Identify trends, patterns, and outliers
 Support decision-making processes

Common Tools:

 Excel, Tableau, Power BI, Python (Matplotlib, Seaborn), R
(ggplot2)


What is Data Analytics?

Data Analytics involves inspecting, cleaning, transforming, and modeling
data to discover useful information, draw conclusions, and support
decision-making.

Types of Data Analytics:

1. Descriptive Analytics – What happened? (e.g., sales reports)
2. Diagnostic Analytics – Why did it happen? (e.g., root cause
analysis)

, 3. Predictive Analytics – What will happen? (e.g., forecasting)
4. Prescriptive Analytics – What should we do? (e.g., optimization
models)

Key Techniques:

 Statistical analysis
 Machine learning
 Data mining
 Data visualization


Importance of Data Visualization in Analytics

 Enhances storytelling with data
 Facilitates quick comprehension of insights
 Makes data more engaging and interactive
 Helps detect errors and outliers visually


Popular Visualization Types:

Visualization Type Purpose
Bar Chart Compare quantities across categories
Line Graph Show trends over time
Pie Chart Show part-to-whole relationships
Scatter Plot Show correlation between variables
Heatmap Show magnitude using color intensity
Dashboard Combine multiple visualizations for monitoring


Applications of Data Visualization & Analytics

 Business Intelligence (BI)