• DRAW 4110: Drawing II

  3.00 Credits

  University of Utah

  
  This course builds upon components presented in ART 3130 and DRAW 3130 with an investigation of drawing as an organizing tool for thorough and personal image exploration. Prerequisites: "C-" or better in DRAW 3130 AND Minor status in Drawing.
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• DRAW 4120: Life Drawing

  3.00 Credits

  University of Utah

  
  This course builds upon components presented in ART 3120, DRAW 3120, ART 3130 and DRAW 3130 with increasing effectiveness in drawing the human figure and increasing the use of the figure as an expressive metaphor. Relative emphases are adjusted to the needs of the individual student. Prerequisites: "C-" or better in DRAW 3130 AND Minor status in Drawing.
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• DRAW 4140: Drawing III

  3.00 Credits

  University of Utah

  
  This course builds upon components presented in ART 3130 and DRAW 3130 with an investigation of drawing as an organizing tool for thorough and personal image exploration. Prerequisites: "C-" or better in DRAW 3130 AND Minor status in Drawing.
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• DS 2500: Data Wrangling

  3.00 Credits

  University of Utah

  
  The course will introduce students to basic techniques in collecting, scrapping, transforming, and normalizing data, as a first step in a data science pipeline. Students will learn by working with a variety of standard data processing tools, and experimenting on numerous example tasks. Prerequisites: 'C' or better in (CS 1410 OR CS 1420 OR COMP 1020 OR MATH 1070).
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• DS 3190: Found. of Data Analysis

  3.00 Credits

  University of Utah

  
  This class will be an introduction to computational data analysis, focusing on the mathematical foundations. The goal will be to carefully develop and explore several core topics that form the backbone of modern data analysis topics, including Machine Learning, Data Mining, Artificial Intelligence, and Visualization. This will include some background in probability and linear algebra, and then various topics including Bayes' rule and connection to inference, gradient descent, linear regression and its polynomial and high dimensional extensions, principal component analysis and dimensionality reduction, as well as classification and clustering. We will also focus on modern models like PAC (probably approximately correct) and cross-validation for algorithm evaluation. Prerequisites: 'C' or better in CS 2100 AND CS 2420 AND MATH 2270 Corequisites: 'C' or better in MATH 3070 OR CS 3130 OR ECE 3530
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• DS 3390: Ethics in Data Science

  3.00 Credits

  University of Utah

  
  In this course, we will explore the technical, social, and ethical ramifications of the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understanding feedback loops in analysis. Through class discussions, case studies and exercises, students will learn the basics of ethical thinking in science, understand the history of ethical dilemmas in scientific work, and study the distinct challenges associated with ethics in modern data science. Prerequisites: 'C' or better in CS 2420 AND (Full Major or Minor status in Computer Science OR Full Major status in Data Science)
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• DS 3941: Data Science Seminar

  1.00 Credits

  University of Utah

  
  This seminar course exposes students to a wide variety of topics in the vast area of data science. These will range from cutting edge research results and open problems to how these techniques transfer to pressing challenges in industry or research labs dependent on these techniques. Data Science, and seminar talks, will include a wide-array of topics in machine learning, data management, data visualization, mathematics of data, data mining, fairness and trustworthiness of algorithms, and algorithmic challenges in big data. Perspectives will include both theoretical developments to challenges to transferring in practice, and all parts in between. The talks will vary significantly in topic, but should mostly be accessible to a graduate student or junior-level undergrad in a data science related area. Students at all levels will get to engage with experts in data science, discover their interests within this space, and explore potential directions and partners for research collaborations.
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• DS 4140: Data Mining

  3.00 Credits

  University of Utah

  
  Data mining is the study of efficiently finding structures and patterns in large data sets. We will focus on: (1) converting from a messy and noisy raw data set to a structured and abstract one, (2) applying scalable and probabilistic algorithms to these well-structured abstract data sets, and (3) formally modeling and understanding the error and other consequences of parts (1) and (2), including choice of data representation and trade-offs between accuracy and scalability. These steps are essential for training as a data scientist. Topics will include: similarity search, clustering, regression/dimensionality reduction, graph analysis, PageRank, and small space summaries. We will also cover several recent developments and applications. Prerequisites: 'C' or better in CS 3500 AND DS 3190 AND Full Major status in Data Science.
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• DS 4350: Machine Learning

  3.00 Credits

  University of Utah

  
  This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time. Topics include several algorithms for supervised and unsupervised learning, decision trees, online learning, linear classifiers, empirical risk minimization, computational learning theory, ensemble methods, Bayesian methods, clustering and dimensionality reduction. Prerequisites: "C" or better in (CS 3500 AND DS 3190).
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• DS 4530: Database Systems

  3.00 Credits

  University of Utah

  
  Representing information about real world enterprises using important data models including the entity-relationship, relational and object-oriented approaches. Database design criteria, including normalization and integrity constraints. Implementation techniques using commercial database management system software. Selected advanced topics such as distributed, temporal, active, and multi-media databases. Undergraduate students only. Prerequisites: 'C' or better CS 3500.
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