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Data Pattern And Visulaization
Published 5/2026
Created by Sowmya N
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 5 Lectures ( 1h 3m ) | Size: 1 GB [/center]
usage of data types - statistical and probabilistic analysis- data visualization
What you'll learn
⚡ Analyze univariate and multivariate data using probability distributions, correlation, and statistical techniques to extract meaningful insights.
⚡ Handle messy data from SQL, JSON, XML, and online sources using imputation, deletion, and data-cleaning strategies.
⚡ Apply visual encoding techniques and design principles to create clear, accurate, and compelling data visualizations.
⚡ Build and explore 3D geometric models and virtual reality environments to enhance interactive data visualization.
Requirements
❗ Basic knowledge of statistics and mathematics. Familiarity with any programming environment is helpful but not mandatory. No prior visualization experience required.
Description
This course introduces students to the principles and practices of data visualization and pattern analysis. It equips learners with the knowledge to identify various data sources, handle messy data, and apply effective visual encoding techniques to communicate insights clearly and accurately.
The course begins with foundational concepts of data shape analysis, covering univariate and multivariate distributions, probability, correlation, and sampling techniques. Students then explore how to retrieve and manage data from relational databases and formats such as SQL, JSON, and XML, including strategies for handling missing or inconsistent data.
A significant portion of the course focuses on the considerations behind effective data visualization - including contextual relevance, appropriate encoding, redundancy avoidance, and compatibility with real-world scenarios. Students study data layout principles such as positioning, sizing, use of color, typography, and the organization of grouped objects to achieve clarity and simplicity in design.
The final unit introduces geometric modelling and virtual environments for visualization, covering 3D mesh compression, data analysis pipelines, and the application of direct manipulation techniques in virtual reality settings.
By the end of this course, students will be able to analyze univariate and multivariate data, implement methods to handle messy datasets, select appropriate visualization techniques, and develop customized layouts using geometric modelling and virtual environments.
Who this course is for
⭐ ECE/Engineering undergraduate or postgraduate students seeking to master data analysis and visualization. Ideal for anyone looking to present data effectively using modern tools, visual design principles, and virtual environments.
Homepage
Data Pattern And Visulaization
Published 5/2026
Created by Sowmya N
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 5 Lectures ( 1h 3m ) | Size: 1 GB [/center]
usage of data types - statistical and probabilistic analysis- data visualization
What you'll learn
⚡ Analyze univariate and multivariate data using probability distributions, correlation, and statistical techniques to extract meaningful insights.
⚡ Handle messy data from SQL, JSON, XML, and online sources using imputation, deletion, and data-cleaning strategies.
⚡ Apply visual encoding techniques and design principles to create clear, accurate, and compelling data visualizations.
⚡ Build and explore 3D geometric models and virtual reality environments to enhance interactive data visualization.
Requirements
❗ Basic knowledge of statistics and mathematics. Familiarity with any programming environment is helpful but not mandatory. No prior visualization experience required.
Description
This course introduces students to the principles and practices of data visualization and pattern analysis. It equips learners with the knowledge to identify various data sources, handle messy data, and apply effective visual encoding techniques to communicate insights clearly and accurately.
The course begins with foundational concepts of data shape analysis, covering univariate and multivariate distributions, probability, correlation, and sampling techniques. Students then explore how to retrieve and manage data from relational databases and formats such as SQL, JSON, and XML, including strategies for handling missing or inconsistent data.
A significant portion of the course focuses on the considerations behind effective data visualization - including contextual relevance, appropriate encoding, redundancy avoidance, and compatibility with real-world scenarios. Students study data layout principles such as positioning, sizing, use of color, typography, and the organization of grouped objects to achieve clarity and simplicity in design.
The final unit introduces geometric modelling and virtual environments for visualization, covering 3D mesh compression, data analysis pipelines, and the application of direct manipulation techniques in virtual reality settings.
By the end of this course, students will be able to analyze univariate and multivariate data, implement methods to handle messy datasets, select appropriate visualization techniques, and develop customized layouts using geometric modelling and virtual environments.
Who this course is for
⭐ ECE/Engineering undergraduate or postgraduate students seeking to master data analysis and visualization. Ideal for anyone looking to present data effectively using modern tools, visual design principles, and virtual environments.
Homepage
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