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INTERDISCIPLINARY GRADUATE PROGRAMS
INTERDISCIPLINARY DATA SCIENCE GROUP
DATA SCIENCE

Detailed course offerings (Time Schedule) are available for

DATA 501 Data Science Visualization Lab (1)
The Data Science Visualization Lab class will provide students additional opportunities to practice and discuss data visualization concepts, with additional emphasis on user-centered design approaches and software development. Students will work in small groups on structured data visualization exercises and UCD methods, and implement simple visualizations.
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DATA 512 Human-Centered Data Science (5)
Fundamental principles of data science and its human implications. Data ethics, data privacy, differential privacy, algorithmic bias, legal frameworks and intellectual property, provenance and reproducibility, data curation and preservation, user experience design and usability testing for big data, ethics of crowdwork, data communication and societal impacts of data science.
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DATA 514 Data Management for Data Science (5)
Introduces database management systems and techniques that use such systems; data models, query languages, database tuning and optimization, data warehousing, and parallel processing. Intended for professional students and non-CSE-majors.
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DATA 515 Software Design for Data Science (5)
Introduces software design and engineering practices and concepts, including version control, testing, and automatic build management. Intended for professional students and non-CSE-majors. Prerequisite: CSE D/DATA 514 or permission of instructor. Offered: jointly with CSE D 515; Sp.
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DATA 516 Scalable Data Systems and Algorithms (5)
Principles and algorithms for data management and analysis at scale. Designs of traditional and modern big data systems and how to use those systems. Basics of cloud computing. Prerequisite: CSE D/DATA 514 and CSE D/DATA 515 or permission of instructor. Offered: jointly with CSE D 516; A.
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DATA 556 Introduction to Statistics and Probability (5)
Overview of probability; conditional probability and independence; Bayes Theorem; discrete and continuous random variables including jointly distributed; key distributions including the normal and its spin offs; properties of expectation and variance; conditional expectation; covariance and correlation; Central Limit Theorem; law of large numbers; Parameter Estimation. Offered: jointly with BIOST 556/STAT 556; A.
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DATA 557 Applied Statistics and Experimental Design (5)
Inferential statistical methods for discrete and continuous random variables including tests for difference in means and proportions; linear and logistic regression; causation versus correlation; confounding; resampling methods; study design. Prerequisite: STAT/BIOST/DATA 556 or instructor's permission. Offered: jointly with BIOST 557/STAT 557; W.
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DATA 558 Statistical Machine Learning for Data Scientists (5)
Bias-variance trade-off; training versus test error; overfitting; cross-validation; subset selection methods; regularized approaches for linear/logistic regression: ridge and lasso; non-parametric regression: trees, bagging, random forests; local regression and splines; generalized additive models; support vector machines; k-means and hierarchical clustering; principal components analysis. Prerequisite: STAT/BIOST/DATA 557, or permission of instructor. Offered: jointly with BIOST 558/STAT 558; Sp.
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DATA 590 Data Science Capstone I- Project Preparation (2)
Part one of a two-course capstone sequence where students organize project teams, select project topics, write a project proposal and begin preparing project data sets.
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DATA 591 Data Science Capstone II- Project Implementation (3)
Part two of a two-course capstone sequence designed to build upon the student driven project from DATA 590. Students will synthesize and apply knowledge and techniques acquired throughout the MSDS program in working with large data sets, deriving insights from data and sharing insights with other people. Prerequisite: DATA 590
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