Syllabus Data Visualization and Modeling - (BE05043011) Total Credits = TH/30 Assessment Pattern and Marks Total Marks Theory Tutorial / Practical ESE (E) PA (M) PA (I) PBL (I) ESE (V) 04 70 30 20 30 50 200 Unit No. Content 1. Acquiring and Visualizing Data, Application areas, Key factors of Data Visualization. Exploring the Visual Data Spectrum : charting Primitives (Data Points, Line, Bar, Pie, Area, Candlestick, Bubble, Surface, Map Charts, Infographics). Native web rendering mechanics : Introduction to HTML5 Canvas, SVG integration, and GPU-accelerated WebGL rendering for massive datasets. (Chapter - 1) 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. (Chapter - 2) 3. Statistical & Interactive Plotting (Python Ecosystem) : Core plotting principles using Matplotlib and Seaborn for multi-variate statistical graphics (Histograms, Box plots, Heatmaps). Building interactive, web-ready exploratory charts using Plotly within Jupyter Notebooks or Google Colab environments. (Google Charts API removed). (Chapter - 3) 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. (Chapter - 4) 5. Dynamic Data States : D3.js data joins, enters, updates, 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. (Chapter - 5) 6. Visual Analytics for Predictive Modeling & ML : Visualizing Machine Learning model evaluations (Confusion Matrices, ROC/AUC curves, Precision-Recall curves, Residual plots for regression). Visualizing unsupervised learning (K-Means cluster scatter plots, Dendrograms). Dimensionality reduction visualization (PCA, t-SNE). Explainable AI (XAI) graphics : interpreting feature importance using SHAP and LIME plots. (Chapter - 6) 7. Information Dashboard Design : UI/UX layout design, human visual perception, cognitive load reduction, and critical display practices. Micro-charts (Bullet Graphs, Sparklines). Enterprise Business Intelligence (BI) : Data Modeling (Star/Snowflake schema relationships) and dashboard implementation using Microsoft Power BI or Tableau. (Chapter - 7)
