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1 Introduction and Course Logistics Understand the motivation for learning data visualization including its increasing Key soft skills Communication visual storytelling and the technical tools required for relevance in job markets data visualization 2 Visual Analytics Applications Recognize different fields where visual analytics apply e g healthcare epidemiology Real world examples like Dr John Snow s cholera map demonstrate how visualization data mining impacts decision making 3 Basic Visualization Techniques Study non spatial data visualization basics including charts and graphical representations Learn techniques for data cleaning transformation and reduction to ensure manageable 4 Data Preparation and Reduction and analyzable datasets 5 Perception and Cognition in Design Explore human cognition and perception basics to design effective and engaging visualizations Concepts of data to ink ratio and visualization aesthetics 6 Fundamentals of Statistics Grasp the statistical underpinnings essential for visual analytics including probability distributions confidence intervals and correlation Familiarize yourself with D3 js and Vega Lite libraries for creating interactive and 7 Technical Skills D3 and Vega Lite sophisticated visualizations 8 Data Types and Similarity Learn about different data types similarities and distance measures that aid in data categorization and clustering 9 10 Data Mining Techniques Study clustering text analysis and pattern recognition techniques in data mining 11 High dimensional Data and Dimensionality Reduction Techniques like PCA Principal Component Analysis and t SNE t Distributed Stochastic Neighbor Embedding for reducing data dimensionality 12 Computer Graphics and Volume Rendering Introduction to rendering methods for 3D and spatial data visualization 13 15 Scientific and Medical Visualization Focus on techniques specific to visualizing scientific and medical data including CT MRI Learn how to emphasize data features without photorealistic quality enhancing clarity in data 16 Non Photorealistic Rendering scientific data visualization 17 Principles of Interaction 18 19 Visual Sense Making Process data through visualization 20 Correlation and Causal Modeling Study interaction design principles that make visualizations more intuitive and user friendly Understand the stages of the visual analytics process and how to derive meaning from Delve into methods to distinguish correlation from causation within visualized data 21 Big Data and Data Summarization Approaches to handle and summarize large datasets for visualization 22 24 Advanced Visualization Techniques Graphs hierarchies text data visualization and time series data analysis 25 27 Narrative Visualization and Storytelling Techniques for crafting visualizations that communicate compelling narratives critical for data journalism 28 Evaluation and User Studies Projects and Assignments Methods to evaluate visualization effectiveness and conduct user studies Practical applications of topics via projects dashboard creation data analytics implementation and interactive visualization projects


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SBU CSE 332 - Midterm Guide 1

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