Undergraduate Certificates
Data Analytics & Visualization for Social Impact
BS Computer Science
Data Analytics & Visualization for Social Impact
Careers include, but are not limited to, entry-level roles such as data analyst, research assistant, program evaluator, and marketing analyst.
CAREERS
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COURSES
MAT 201 Elementary Statistics (3cr)
This course provides instruction in summarizing data using graphical methods, measures of central tendency, dispersion, position, correlation, regression, data collection, elementary probability, and inferential statistics.
CSC 205 Application Design I (3cr.)
An introduction to C# and the Windows integrated development environment, designing Windows-based applications, control structures, procedures, and functions, arrays, basic graphical user interface controls, properties, events, and methods. Prerequisite: CSC 201 or CIS 210.
DSC 400 Machine Learning (3cr.)
This course uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Prerequisite: Junior or Senior Standing
DSC 420 Data Visualization (3cr.)
Data visualization techniques help managers use their perceptions to better understand the data. The goal of this course is to introduce students to data visualization, including both the principles and techniques. Prerequisite: Junior or Senior Standing. 174
DSC 451 Statistical Inference (3cr.)
This course introduces the ideas and methods of probability and statistical inference to students in mathematics and the sciences. Topics include confidence intervals, tests of significance, chi-square tests of goodness-of-fit and independence, regression analysis, and analysis of variance. Prerequisite: Junior or Senior Standing.
DSC 470 Regression Models (3cr.)
This course covers regression analysis, least squares, and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing. Prerequisite: Junior or Senior Standing.
This course provides instruction in summarizing data using graphical methods, measures of central tendency, dispersion, position, correlation, regression, data collection, elementary probability, and inferential statistics.
CSC 205 Application Design I (3cr.)
An introduction to C# and the Windows integrated development environment, designing Windows-based applications, control structures, procedures, and functions, arrays, basic graphical user interface controls, properties, events, and methods. Prerequisite: CSC 201 or CIS 210.
DSC 400 Machine Learning (3cr.)
This course uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Prerequisite: Junior or Senior Standing
DSC 420 Data Visualization (3cr.)
Data visualization techniques help managers use their perceptions to better understand the data. The goal of this course is to introduce students to data visualization, including both the principles and techniques. Prerequisite: Junior or Senior Standing. 174
DSC 451 Statistical Inference (3cr.)
This course introduces the ideas and methods of probability and statistical inference to students in mathematics and the sciences. Topics include confidence intervals, tests of significance, chi-square tests of goodness-of-fit and independence, regression analysis, and analysis of variance. Prerequisite: Junior or Senior Standing.
DSC 470 Regression Models (3cr.)
This course covers regression analysis, least squares, and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing. Prerequisite: Junior or Senior Standing.