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Your search for courses for 23/SP and with code: STATELEC found 3 courses.

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CS 314.00 Data Visualization 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

Weitz Center 235

MTWTHF
1:50pm3:00pm1:50pm3:00pm2:20pm3:20pm
Synonym: 64318

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

Open: Size: 30, Registered: 0, Waitlist: 0

CMC 102

MTWTHF
12:30pm1:40pm12:30pm1:40pm1:10pm2:10pm
Synonym: 65284

Deepak Bastola

(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, data wrangling, data visualization using packages such as ggplots, interactive graphics using tools such as Shiny, supervised and unsupervised classification methods, and understanding and visualizing spatial data. We will use the statistics software R in this course.

Prerequisite: Statistics 120 (formerly Mathematics 215), Statistics 230 (formerly Mathematics 245) or Statistics 250 (formerly Mathematics 275)

Formerly Mathematics 285

STAT 310.00 Spatial Statistics 6 credits

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

CMC 306

MTWTHF
1:50pm3:00pm1:50pm3:00pm2:20pm3:20pm

Other Tags:

Synonym: 63898

Claire E Kelling

Spatial data is becoming increasingly available in a wide range of disciplines, including social sciences such as political science and criminology, as well as natural sciences such as geosciences and ecology. This course will introduce methods for exploring and analyzing spatial data. Methods will be covered to describe and analyze three main types of spatial data: areal, point process, and point-referenced (geostatistical) data. The course will also extensively cover tools for working with spatial data in R. The goals are that by the end of the course, students will be able to read, explore, plot, and describe spatial data in R, determine appropriate methods for analyzing a given spatial dataset, and work with their own spatial dataset(s) in R and derive conclusions about an application through statistical inference.

Prerequisite: Statistics 230 (formerly MATH 245) and Statistics 250 (formerly MATH 275)

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