{"product_id":"9789365210781-1","title":"Data Visualization and Modeling  for GTU 24 Course (V - AI\u0026DS - BE05043011)","description":"\u003cp\u003eSyllabus Data Visualization and Modeling - (BE05043011) Total  Credits = TH\/30\tAssessment Pattern and Marks\tTotal Marks \tTheory\tTutorial \/ Practical\t \tESE (E)\tPA (M)\tPA (I)\tPBL (I)\tESE (V)\t 04\t70\t30\t20\t30\t50\t200  Unit No.\tContent 1.\tAcquiring 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.\tReading 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.\tStatistical \u0026amp; 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.\tModern 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.\tDynamic 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.\tVisual Analytics for Predictive Modeling \u0026amp; 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.\tInformation 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)\u003c\/p\u003e","brand":"Technical Publications","offers":[{"title":"Default Title","offer_id":48217988137131,"sku":"12146244013","price":460.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0620\/3355\/9723\/files\/9789365210781_1_7f488f16-5a01-4210-b6b7-34064e26fbad.jpg?v=1791645385","url":"https:\/\/technicalpublications.in\/products\/9789365210781-1","provider":"Technical Publications","version":"1.0","type":"link"}