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Your search for courses for 21/WI and with code: STATELEC found 3 courses.

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CS 314.00 Data Visualization 6 credits

Closed: Size: 34, Registered: 30, Waitlist: 0

Location To Be Announced

MTWTHF
2:30pm3:40pm2:30pm3:40pm3:10pm4:10pm
Synonym: 58565

Eric C Alexander

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

Location To Be Announced

MTWTHF
10:00am11:10am10:00am11:10am9:50am10:50am
Synonym: 58603

Katie R St. Clair

(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)

Formerly Mathematics 285

STAT 330.00 Advanced Statistical Modeling 6 credits

Open: Size: 20, Registered: 15, Waitlist: 0

Location To Be Announced

MTWTHF
11:30am12:40pm11:30am12:40pm11:10am12:10pm

Other Tags:

Synonym: 58611

Laura M Chihara

(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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You must take 6 credits of each of these.
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You must take 6 credits of each of these,
except Quantitative Reasoning, which requires 3 courses.
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