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Data Science Masters

Data Visualization Examples

On this page you will find five different images shared on previous MSDS capstone presentations. If you rely on alt-text for image interpretability, you may choose to respond based on the text descriptions of the chosen images.

 

MSDS Capstone Image 1

This visualization is from a project entitled 'PIVOT: CNN-labeled image validation'. Chandler Ault, Yamina Katariya, Yash Manne, Aditi Shrivastava looked at whether they could use CNN's to label plankton types. The plot shows the distribution different plankton classes using two different approaches. If you rely on alt-text for image interpretability, you may choose to respond based upon the text descriptions of the images. You may wish the address the combined impact instead of focusing on a singular image.

 

 

MSDS Capstone Image 2

This visualization is from a project entitled 'Whole embryo analysis of nuclear variance during mammalian development identifies left-right somite pair asymmetry'. Madison Sanchez-Forrman looked at processing light sheet fluorescent microscopy image. This plot shows the result of image segmentation. If you rely on alt-text for image interpretability, you may choose to respond based upon the text descriptions of the images. You may wish the address the combined impact instead of focusing on a singular image.

 

 

MSDS Capstone Image 3

This visualization is from a project entitled 'Zap! Analyzing Lightning and Satellite Imagery of Tropical Cyclones for Pattern Recognition of Intensity Changes'. Kyle Bretherton, Edgar Lopez, Alyssa Mell built an app for users to explore lightening activity and hurricane intensity. If you rely on alt-text for image interpretability, you may choose to respond based upon the text descriptions of the images. You may wish the address the combined impact instead of focusing on a singular image.

 

 

MSDS Capstone Image 4

This visualization is from a project entitled 'Modelling the progression of type-I diabetes using machine learning'. Manya Chada, Elizabeth Holden, and Sarah Nguyen looked at whether they could algorithmically predict the onset of T1D. The plot shows the distribution of samples in study datasets. If you rely on alt-text for image interpretability, you may choose to respond based upon the text descriptions of the images. You may wish the address the combined impact instead of focusing on a singular image.

 

 

MSDS Capstone Image 5

This visualization is from a project entitled 'Listening to Puget Sound: Automated Data Pipeline & Visualization of Anthropogenic Sounds'. Clayton Brock, Hua-Hsing Huang, Erin Mee, Srimant Mishra built models to identify noise pollution in hydrophone data. This image shows the broadband signal aligned with ship and whale traffic. If you rely on alt-text for image interpretability, you may choose to respond based upon the text descriptions of the images. You may wish the address the combined impact instead of focusing on a singular image.