Search Results
Your search for courses · during 2023-24 · tagged with STAT Elective · returned 8 results
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CS 314 Data Visualization 6 credits
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.
- Spring 2024
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
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Computer Science 200 or 201
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CS 314.00 Spring 2024
- Faculty:Eric Alexander 🏫 👤
- Size:16
- M, WHulings 316 9:50am-11:00am
- FHulings 316 9:40am-10:40am
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CS 320 Machine Learning 6 credits
What does it mean for a machine to learn? Much of modern machine learning focuses on identifying patterns in large datasets and using these patterns to make predictions about the future. Machine learning has impacted a diverse array of applications and fields, from scientific discovery to healthcare to education. In this artificial intelligence-related course, we’ll both explore a variety of machine learning algorithms in different application areas, taking both theoretical and practical perspectives, and discuss impacts and ethical implications of machine learning more broadly. Topics may vary, but typically focus on regression and classification algorithms, including neural networks.
- Fall 2023, Winter 2024
- Formal or Statistical Reasoning
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Computer Science 200 or 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)
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CS 320.00 Fall 2023
- Faculty:Anna Rafferty 🏫 👤
- Size:34
- M, WLanguage & Dining Center 104 9:50am-11:00am
- FLanguage & Dining Center 104 9:40am-10:40am
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CS 320.00 Winter 2024
- Faculty: Staff
- Size:34
- M, WLanguage & Dining Center 104 9:50am-11:00am
- FLanguage & Dining Center 104 9:40am-10:40am
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CS 362 Computational Biology 6 credits
Recent advances in high-throughput experimental techniques have revolutionized how biologists measure DNA, RNA and protein. The size and complexity of the resulting datasets have led to a new era where computational methods are essential to answering important biological questions. This course focuses on the process of transforming biological problems into well formed computational questions and the algorithms to solve them. Topics include approaches to sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; and specific disease applications including cancer genomics.
- Winter 2024
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
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Computer Science 200 or 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)
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CS 362.00 Winter 2024
- Faculty:Layla Oesper 🏫 👤
- Size:16
- M, WAnderson Hall 223 11:10am-12:20pm
- FAnderson Hall 223 12:00pm-1:00pm
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CS 362.M Winter 2024
- Size:18
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MATH 271 Computational Mathematics 6 credits
An introduction to mathematical ideas from numerical approximation, scientific computing, and/or data analysis. Topics will be selected from numerical linear algebra, numerical analysis, and optimization. Theory, implementation, and application of computational methods will be emphasized.
Not open to students who have already received credit for Mathematics 295 Numerical Analysis
- Winter 2024
- Formal or Statistical Reasoning
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Mathematics 232
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STAT 220 Introduction to Data Science 6 credits
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.
- Fall 2023, Winter 2024, Spring 2024
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
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Statistics 120, Statistics 230 or Statistics 250
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STAT 260 Introduction to Sampling Techniques 6 credits
Covers sampling design issues beyond the basic simple random sample: stratification, clustering, domains, and complex designs like two-phase and multistage designs. Inference and estimation techniques for most of these designs will be covered and the idea of sampling weights for a survey will be introduced. We may also cover topics like graphing complex survey data and exploring relationships in complex survey data using regression and chi-square tests.
- Winter 2024
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
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Statistics 120, Statistics 230, or Statistics 250
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STAT 310 Spatial Statistics 6 credits
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.
- Spring 2024
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
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Statistics 230 and Statistics 250
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STAT 320 Time Series Analysis 6 credits
Models and methods for characterizing dependence in data that are ordered in time. Emphasis on univariate, quantitative data observed over evenly spaced intervals. Topics include perspectives from both the time domain (e.g., autoregressive and moving average models, and their extensions) and the frequency domain (e.g., periodogram smoothing and parametric models for the spectral density).
- Fall 2023
- Formal or Statistical Reasoning Quantitative Reasoning Encounter
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Statistics 230 and 250. Exposure to matrix algebra may be helpful but is not required