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بسم الله الرحمن الرحيم

الحمد لله وحده، والصلاة والسلام على من لا نبي بعد ﷺ

12-Week Course Plan: IS434P - Information Visualization (Updated)

Week Topic Lecture Focus Lab/Project Focus
1 Introduction to Information Visualization Course overview, importance of visualization, historical context Setting up Python (Anaconda), Introduction to visualization libraries (Matplotlib, Seaborn)
2 The Structure and Characteristics of Data Data types, data transformations, data encodings Hands-on: Loading and exploring datasets using Pandas and Matplotlib
3 Visualization Algorithms Common visualization techniques (bar charts, scatter plots, treemaps, heatmaps) Implementing basic visualizations in Python using Matplotlib and Seaborn
4 Exploratory Visual Data Analysis EDA concepts, interactive visualization tools Using Pandas, Seaborn, and Plotly for EDA
5 Focus + Context Techniques Overview of techniques: fisheye views, zooming, distortion techniques Hands-on: Implementing focus+context visualization in Python (e.g., interactive zooming)
6 Dynamic Queries & Interaction Principles of interaction, real-time filtering, brushing & linking Interactive visualizations with Plotly and Dash
7 Graph Databases and Neo4j (NEW) Introduction to Graph Theory, Graph Data Models, Neo4j Basics Installing Neo4j, Writing Cypher Queries, Visualizing Graph Data in Neo4j
8 Graph Visualization Techniques (NEW) Graph layouts, force-directed graphs, node-link diagrams Hands-on: Visualizing network data using Neo4j, NetworkX, and PyVis
9 Document Visualization Visualizing text and structured documents NLP-based visualizations using WordCloud and Topic Modeling
10 Internet and Information Spaces Web-based visualizations, visual analytics for search Scraping and visualizing web-based datasets (e.g., using BeautifulSoup & NetworkX)
11 Empirical Evaluation of Visualizations Evaluation methodologies, usability testing, perceptual effectiveness Conducting user studies and analyzing user feedback
12 Principles for Effective Design Best practices, cognitive principles in visualization, ethical considerations Final project presentations and peer review

Assessment Structure:

  • Labs/Assignments: 20%
  • Midterm Exam: 10%
  • Oral Exam: 10%
  • Final Exam: 60%

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