Introduction_to_Data_Visualization for financial analytics
- 1. Introduction to Data Visualization Overview, Benefits, Types, and Tools
- 2. Introduction • Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. • Helps to understand patterns, trends, or outliers in a dataset. • Presents data in an accessible manner for audiences without technical knowledge. • Example: Health agency providing a map of vaccinated regions.
- 3. Meaning • Process of representing data visually using graphs, charts, or maps. • Communicates complex information intuitively. • Helps identify trends, patterns, and outliers in large datasets. • Delivers visual reporting on performance, operations, or statistics.
- 4. Steps for Data Visualization • Be clear on the question. • Know your data and start with basic visualizations. • Identify messages of the visualization. • Choose the right chart type. • Use color, size, scale, shapes, and labels to direct attention.
- 5. Benefits of Data Visualization • Storytelling: Colors and patterns bring stories within data to life. • Accessibility: Easy-to-understand for various audiences. • Visualize relationships: Easier to spot patterns in data. • Exploration: Encourages exploration and actionable decisions.
- 6. Characteristics of Effective Graphical Visuals • Shows data clearly in an understandable manner. • Encourages comparison between data pieces. • Integrates statistical and verbal descriptions. • Grabs interest and focuses the mind. • Identifies areas needing attention and improvement. • Tells a story efficiently, quicker than text.
- 7. Tools for Visualizing Data • Tableau • Google Charts • Dundas BI • Power BI • Jupyter • Infogram • Chart Blocks • D3.js • Fusion Charts • Grafana
- 8. Types of Data Visualization • Bar charts • Line charts • Scatter plots • Pie charts • Heat maps • Table • Chart or graph • Gantt chart • Geospatial visualization • Dashboard
- 9. Detailed Visualizations • Line Graph: Shows trends, projections, and growth over time. • Bar Graph: Compares values across categories. • Pie Chart: Shows numerical proportions. • Table: Lists metrics by importance. • Funnel: Tracks customer stages. • Number Visualization: Highlights one key metric. • Pipeline: Tracks leads and KPIs. • Progress Bar: Shows progress towards a goal. • Gauge: Shows progress and maximum value. • Compare Visualization: Compares two metrics.
- 10. Categories of Data Visualization • Numerical Data (Quantitative) • - Continuous Data: Measurable values (e.g., height). • - Discrete Data: Countable values (e.g., number of cars). • Categorical Data (Qualitative) • - Binary Data: Yes/No classification. • - Nominal Data: Categories without order. • - Ordinal Data: Categories with order.
- 11. Summary • Data visualization turns data into understandable visuals. • Improves decision-making and engagement. • Various tools and techniques suit different needs. • Choosing the right visualization type is key.