Introduction_to_Data_Visualization for financial analytics

Introduction_to_Data_Visualization for financial analytics

• 1.
  Introduction to Data Visualization Overview,Benefits, Types, and Tools
• 2.
  Introduction • Data visualizationis 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 ofrepresenting 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 DataVisualization • 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.
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  Benefits of DataVisualization • 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 GraphicalVisuals • 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 VisualizingData • Tableau • Google Charts • Dundas BI • Power BI • Jupyter • Infogram • Chart Blocks • D3.js • Fusion Charts • Grafana
• 8.
  Types of DataVisualization • Bar charts • Line charts • Scatter plots • Pie charts • Heat maps • Table • Chart or graph • Gantt chart • Geospatial visualization • Dashboard
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  Detailed Visualizations • LineGraph: 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 DataVisualization • 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 visualizationturns data into understandable visuals. • Improves decision-making and engagement. • Various tools and techniques suit different needs. • Choosing the right visualization type is key.