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Your search for courses for 21/WI and with code: STATELEC found 3 courses.
CS 314.00 Data Visualization 6 credits
Closed: Size: 34, Registered: 30, Waitlist: 0
| M | T | W | TH | F |
|---|---|---|---|---|
| 2:30pm3:40pm | 2:30pm3:40pm | 3:10pm4:10pm |
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Understanding the wealth of data that surrounds us can be challenging. Luckily, we have evolved incredible tools for finding patterns in large amounts of information: our eyes! Data visualization is concerned with taking information and turning it into pictures to better communicate patterns or discover new insights. It combines aspects of computer graphics, human-computer interaction, design, and perceptual psychology. In this course, we will learn the different ways in which data can be expressed visually and which methods work best for which tasks. Using this knowledge, we will critique existing visualizations as well as design and build new ones.
Prerequisite: Computer Science 201
STAT 220.00 Introduction to Data Science 6 credits
Closed: Size: 30, Registered: 27, Waitlist: 0
| M | T | W | TH | F |
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| 10:00am11:10am | 10:00am11:10am | 9:50am10:50am |
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(Formerly Mathematics 285) This course will cover the computational side of data analysis, including data acquisition, management, and visualization tools. Topics may include: data scraping, clean up and manipulation, data visualization using packages such as ggplots, understanding and visualizing spatial and network data, and supervised and unsupervised classification methods. We will use the statistics software R in this course.
Prerequisite: Statistics 120 (formerly Mathematics 215) or Statistics 250 (formerly Mathematics 275)
STAT 330.00 Advanced Statistical Modeling 6 credits
Open: Size: 20, Registered: 15, Waitlist: 0
| M | T | W | TH | F |
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| 11:30am12:40pm | 11:30am12:40pm | 11:10am12:10pm |
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(Formerly MATH 345) Topics include linear mixed effects models for repeated measures, longitudinal or hierarchical data and generalized linear models (of which logistic and Poisson regression are special cases) including zero-inflated Poisson models. Depending on time, additional topics could include survival analysis, generalized additive models or models for spatial data.
Prerequisite: Statistics 230 and 250 (formerly Mathematics 245 and 275) or permission of the instructor
Formerly Mathematics 345
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