DTSA 5304 - Fundamentals of Data Visualization
Primary Instructor
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Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024 / Spring 2025 / Summer 2025
Explores the design, development, and evaluation of information visualizations. Combine aspects of design, computer graphics, HCI, and data science, to gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. Same as CSCA 5702.
INFO 1301 - Statistics for Information Science
Primary Instructor
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Fall 2025
Introduces concepts and techniques for characterizing and quantifying data. Students will learn to summarize, visualize, and interpret data with descriptive statistics and will learn the foundations of statistical inference and modeling. Topics include statistical distributions and the normal distribution, hypothesis testing and statistical significance, and linear regression.
INFO 4602 - Information Visualization
Primary Instructor
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Summer 2024 / Summer 2025
Explores the design, development and evaluation of information visualizations. Covers visual representations of data and provides hands-on experience with using and building exploratory tools and data narratives. Students create visualizations for a variety of domains and applications, working with stakeholders and their data. Covers interactive systems, user-centered and graphic design, perception, data storytelling and analysis, and insight generation. Programming knowledge is strongly encouraged. Same as INFO 5602.
INFO 4604 - Applied Machine Learning
Primary Instructor
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Spring 2023
Introduces algorithms and tools for building intelligent computational systems. Methods will be surveyed for classification, regression and clustering in the context of applications such as document filtering and image recognition. Students will learn the theoretical underpinnings of common algorithms (drawing from mathematical disciplines including statistics and optimization) as well as the skills to apply machine learning in practice. Same as INFO 5604.
INFO 4651 - Fundamental Concepts in Data Science
Primary Instructor
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Fall 2022 / Fall 2023 / Fall 2024 / Fall 2025
This intensive course provides a general understanding of the mathematical concepts required for success in data science. This course will cover a wide range of mathematical tools in data science including an overview of calculus and linear algebra along with selected topics from numerical analysis. The course will also explore computational implementations of these ideas. This course provides a bridge for students without these advanced math concepts to learn to apply them within a data science career or within a graduate program in data science. Same as INFO 5651.
INFO 4652 - Statistical Programming in R
Primary Instructor
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Fall 2023
This intensive course covers foundational data science tools and techniques in the R programming language, including acquiring, cleaning, exploring, and analyzing data, programming, and conducting reproducible research. The course will emphasize the use of data management best practices such as the tidyverse toolkit in R. Same as INFO 5652.
INFO 5602 - Information Visualization
Primary Instructor
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Fall 2022 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024 / Spring 2025 / Summer 2025 / Fall 2025 / Spring 2026
Explores the design, development and evaluation of information visualizations. Covers visual representations of data and provides hands-on experience with using and building exploratory tools and data narratives. Students create visualizations for a variety of domains and applications, working with stakeholders and their data. Covers interactive systems, user-centered and graphic design, perception, data storytelling and analysis, and insight generation. Programming knowledge is strongly encouraged. Same as INFO 4602.
INFO 5604 - Applied Machine Learning
Primary Instructor
-
Spring 2023 / Spring 2024
Introduces algorithms and tools for building intelligent computational systems. Methods will be surveyed for classification, regression and clustering in the context of applications such as document filtering and image recognition. Students will learn the theoretical underpinnings of common algorithms (drawing from mathematical disciplines including statistics and optimization) as well as the skills to apply machine learning in practice. Same as INFO 4604.
INFO 5651 - Fundamental Concepts in Data Science
Primary Instructor
-
Fall 2022 / Fall 2023 / Fall 2024 / Fall 2025
This intensive course provides a general understanding of the mathematical concepts required for success in data science. This course will cover a wide range of mathematical tools in data science including an overview of calculus and linear algebra along with selected topics from numerical analysis. The course will also explore computational implementations of these ideas. This course provides a bridge for students without these advanced math concepts to learn to apply them within a data science career or within a graduate program in data science. Same as INFO 4651.
INFO 5652 - Statistical Programming in R
Primary Instructor
-
Fall 2023
This intensive course covers foundational data science tools and techniques in the R programming language, including acquiring, cleaning, exploring, and analyzing data, programming, and conducting reproducible research. The course will emphasize the use of data management best practices such as the tidyverse toolkit in R. Same as INFO 4652.
STAT 4000 - Statistical Methods and Application I
Primary Instructor
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Fall 2022
Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language. Same as STAT 5000.
STAT 5000 - Statistical Methods and Application I
Primary Instructor
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Fall 2022 / Spring 2023
Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language. Recommended prerequisites of APPM 1360 or MATH 2300 or equivalent. Same as STAT 4000.
