Subject: DSCI

Term: Summer 2021

DSCI 354: Data Visualization and Analytics (800/3334)

TWR 10:30-12:00 PM Jun 01-Jul 27

Bruckman, L

Data Visualization and Analytics students will learn data visualization and analytics techniques focused on different types of data such as time-series, spectral, or image data science problems. This class will focus on increasing analysis of complex data sets through visualization by enhancing exploratory data analysis and data cleaning. This class will focus on creating effective data visualizations to communicate data analytics results to different audiences. Different datasets will be provided to develop different types of visualizations and analytics. Types of data visualizations include in interactive plots (e.g., bar graphs change over time), applications that allow users to adjust the visualizations based on their decisions (e.g., shiny applications), interactive maps, 3-D plots of data, etc. Discussing how an audience understands information and brings in data as well as the ethics of making data visualizations will be discussed. The class will also include ways to increase modeling and analysis with effective visualizations for credible, data-driven decision making. This will include a git repository for other students to use these codes as open source resources and the preparation of reproducible data science analyses for different types of problems. Offered as DSCI 354, DSCI 354M, and DSCI 454.

DSCI 354M: Data Visualization and Analytics (800/3335)

TWR 10:30-12:00 PM Jun 01-Jul 27

Bruckman, L

Data Visualization and Analytics students will learn data visualization and analytics techniques focused on different types of data such as time-series, spectral, or image data science problems. This class will focus on increasing analysis of complex data sets through visualization by enhancing exploratory data analysis and data cleaning. This class will focus on creating effective data visualizations to communicate data analytics results to different audiences. Different datasets will be provided to develop different types of visualizations and analytics. Types of data visualizations include in interactive plots (e.g., bar graphs change over time), applications that allow users to adjust the visualizations based on their decisions (e.g., shiny applications), interactive maps, 3-D plots of data, etc. Discussing how an audience understands information and brings in data as well as the ethics of making data visualizations will be discussed. The class will also include ways to increase modeling and analysis with effective visualizations for credible, data-driven decision making. This will include a git repository for other students to use these codes as open source resources and the preparation of reproducible data science analyses for different types of problems. Offered as DSCI 354, DSCI 354M, and DSCI 454.

DSCI 454: Data Visualization and Analytics (800/3336)

TWR 10:30-12:00 PM Jun 01-Jul 27

Bruckman, L

Data Visualization and Analytics students will learn data visualization and analytics techniques focused on different types of data such as time-series, spectral, or image data science problems. This class will focus on increasing analysis of complex data sets through visualization by enhancing exploratory data analysis and data cleaning. This class will focus on creating effective data visualizations to communicate data analytics results to different audiences. Different datasets will be provided to develop different types of visualizations and analytics. Types of data visualizations include in interactive plots (e.g., bar graphs change over time), applications that allow users to adjust the visualizations based on their decisions (e.g., shiny applications), interactive maps, 3-D plots of data, etc. Discussing how an audience understands information and brings in data as well as the ethics of making data visualizations will be discussed. The class will also include ways to increase modeling and analysis with effective visualizations for credible, data-driven decision making. This will include a git repository for other students to use these codes as open source resources and the preparation of reproducible data science analyses for different types of problems. Offered as DSCI 354, DSCI 354M, and DSCI 454.