2.1 Visualization-and-Data-Exploration

1
Visualization Data Exploration

by Jitendra Tomar
2
2. Data Visualization

• Data visualization helps in understanding and
  communicating insights effectively.
• Some common techniques
• Univariate Visualizations (Single Variable)
  Histograms, box plots, density plots.
• Bivariate Multivariate Visualizations Scatter
  plots, heatmaps, pair plots.
• Time-Series Visualizations Line charts for
  trends over time.
• Categorical Data Representation Bar charts, pie
  charts, treemaps.

3
3. Advanced Visualization Techniques

• Interactive Dashboards
• Power BI, Tableau, Plotly (Python), Shiny (R).
• Geospatial Visualization
• Maps using GIS, Folium, or Google Maps API.
• 3D Visualizations
• For high-dimensional data using Matplotlib,
  Plotly.
• Network Graphs
• For relationships, using tools like Gephi or
  NetworkX.

4
4. Tools Libraries for Data Visualization

• Python
• Matplotlib, Seaborn, Plotly, Bokeh
• R
• ggplot2, Shiny
• Business Intelligence Tools
• Tableau, Power BI, Looker
• Big Data Visualization
• D3.js, Kibana, Grafana

5
5. Introduction to Data Exploration
Visualization
Data exploration and visualization are
fundamental steps in data analysis that help in
understanding patterns, trends, and relationships
in data before applying statistical models or
machine learning algorithms.
Key Goals

• Detect patterns, anomalies, and outliers

• Summarize large datasets effectively

• Facilitate decision-making through visual insights

6
6. Types of Data Exploration

• Univariate Analysis
• Analyzing a single variable (e.g., histograms,
  boxplots)
• Bivariate Analysis
• Studying the relationship between two variables
  (e.g., scatter plots, correlation heatmaps)
• Multivariate Analysis
• Examining multiple variables together (e.g.,
  pair plots, parallel coordinates)

7
7. Data Visualization Techniques

• Basic Visualizations
• Bar Charts Used for categorical comparisons.
• Histograms Show frequency distributions.
• Box Plots Help in understanding spread and
  detecting outliers.
• Scatter Plots Show relationships between two
  numerical variables.
• Line Charts Useful for trends over time.

8
7. Data Visualization Techniques

• Advanced Visualizations
• Heatmaps Represent correlation between
  variables.
• Violin Plots Show distribution and density in
  addition to boxplot features.
• Treemaps Represent hierarchical data.
• Geospatial Maps Used for location-based data
  (e.g., choropleth maps).

9
7. Data Visualization Techniques

• Interactive Dynamic Visualizations
• Tools like
• Tableau,
• Power BI,
• Plotly,
• D3.js, and
• Dash
• allow users to interact with data, filter
  specific values, and uncover deeper insights.

10
8. Tools for Data Exploration Visualization

• Python Libraries Matplotlib, Seaborn, Plotly,
  Altair
• R Libraries ggplot2, Shiny, Lattice
• BI Tools Tableau, Power BI, Google Data Studio
• Excel Pivot charts, conditional formatting

11
9. Best Practices for Effective Visualization

• Choose the Right Chart Avoid misleading visuals
  by selecting appropriate chart types.
• Keep It Simple Avoid clutter highlight key
  insights.
• Use Color Effectively Use consistent color
  schemes for categories.
• Label Properly Ensure clear axes, legends, and
  titles.
• Interactive Elements Use filters and tooltips to
  make visualizations more informative.

12
10. Applications in Business Sustainability

• Risk Management Visualizing financial risk and
  fraud detection.
• Sustainability Analytics Tracking carbon
  emissions using dashboards.
• Marketing E-commerce Customer segmentation and
  sales trend analysis.
• AI Smart Tech Data visualization in AI-driven
  decision-making.

13
Thats all folks

• Learning is an ART