Data Visualization
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Follow on Instagram for UpdatesData Visualization Syllabus — Unit-wise Content
The official GTU curriculum for Data Visualization (subject code BE05049011) as prescribed for CS-Design · Semester 5 divides the course into 6 units, covering 27 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (BE05049011)Unit 1 Acquiring and Visualizing Data, Application areas, Key factors of Data
- Exploring the Visual Data Spectrum: charting Primitives (Data Points
- Line, Bar, Pie, Area
- Map Charts
- Native web rendering mechanics: Introduction to HTML5 Canvas
- SVG integration, and GPU-accelerated WebGL rendering for massive datasets
Unit 2 Reading and parsing modern data formats (CSV, JSON, Parquet). Introduction
- to the Python Data Science Stack: Data manipulation
- structural cleaning, and DataFrame wrangling using Pandas and NumPy
- migrating from manual web- table formatting to automated programmatic data exploration
Unit 3 Statistical & Interactive Plotting (Python Ecosystem)
- Core plotting principles using Matplotlib and Seaborn for multi-variate statistical graphics (Histograms
- Box plots
- Building interactive
- web-ready exploratory charts using Plotly within Jupyter Notebooks or Google Colab environments
- (Google Charts API removed)
Unit 4 Modern D3.js Architecture
- Environment setup
- DOM selections
- data binding
- attribute manipulation, and asynchronous external data loading
- Modern JavaScript Prototyping: Utilizing Observable HQ environments for reactive data binding and rapid D3 visualization engineering
Unit 5 Dynamic Data States
- D3.js data joins
- exits, and animated transitions
- Low-Code Data Application Frameworks: Designing and deploying functional web applications using Streamlit or Dash (Python)
- Streaming Analytics: Visualizing live data feeds via WebSockets or MQTT for real-time tracking
Unit 6 Information Dashboard Design
- UI/UX layout design
- human visual perception
- cognitive load reduction, and critical display practices
- Micro-charts (Bullet Graphs
- Enterprise Business Intelligence (BI): Data Modeling (Star/Snowflake schema relationships) and dashboard implementation using Microsoft Power BI or Tableau
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