Live Course Module: D3.js Course for Data Analytics

Total Duration: 30 Hours (5 Weeks)

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Week 1: Introduction to D3.js and Web Data Visualization (6 Hours)

• Topics:

  • Overview of D3.js and its role in data visualization

  • Setting up the D3.js environment (HTML, CSS, JavaScript basics)

  • Understanding SVG (Scalable Vector Graphics) in D3

  • DOM manipulation and data binding concepts

  • Drawing basic shapes (lines, rectangles, circles) with D3

• Outcome:
  Learners will understand D3’s structure, environment setup, and how to create simple visual elements bound to data.

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Week 2: Working with Data and Scales (6 Hours)

• Topics:

  • Loading external data (CSV, JSON, TSV)

  • Data binding and dynamic updates

  • Using scales (d3.scaleLinear, d3.scaleOrdinal, d3.scaleTime)

  • Axes creation and customization

  • Handling datasets with different data types

• Outcome:
  Learners will efficiently import, transform, and scale data for visual mapping in D3.js.

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Week 3: Building Core Visualizations (6 Hours)

• Topics:

  • Creating bar charts, line charts, scatter plots, and pie charts

  • Adding labels, legends, and color scales

  • Managing margins, axes, and gridlines

  • Implementing transitions and animations (d3.transition())

  • Responsive visualizations for different screen sizes

• Outcome:
  Students will be able to create standard and interactive chart types commonly used in analytics dashboards.

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Week 4: Interactivity and Advanced Visuals (6 Hours)

• Topics:

  • Event handling: mouseover, click, hover interactions

  • Tooltips and highlighting data points

  • Creating hierarchical visualizations (tree, treemap, sunburst)

  • Network diagrams and force-directed layouts

  • Integrating D3 visualizations with dashboards or web apps

• Outcome:
  Learners will add interactivity and create advanced visual analytics experiences using D3.js.

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Week 5: Real-world Project & Integration (6 Hours)

• Topics:

  • Case Study 1: Interactive Sales Dashboard Visualization

  • Case Study 2: Time Series & Trend Analysis Visualization

  • Performance optimization and modular code structure

  • Exporting and embedding visualizations in web applications

  • Using D3.js with other frameworks (React, Angular basics)

• Outcome:
  Learners will complete real-world visualization projects and integrate D3.js charts into analytics platforms or websites.

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🎯 Final Deliverables & Outcomes

• Build data-driven, interactive, and dynamic web visualizations from scratch.

• Manipulate DOM elements using D3.js based on real datasets.

• Apply transitions, interactivity, and storytelling techniques in visualization.

• Integrate D3.js visuals within data analytics dashboards or web applications.