10 courses with the subject DATA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DATA 101- Introduction to Data Science3
A hands-on introduction to the field of Data Science and its applications. Covers a wide range of topics to provide an overview of the use of data in different fields. Provides hands-on practice with basic tools and methods of data analysis. Prepares students to use data in their field of study and in their work and to effectively communicate quantitative findings.
Prerequisite: CPSC 225, and grade of C or better in CPSC 240 or DATA 219. Presents basic techniques for the design and implementation of database-driven web applications. Topics include the design of relational and NoSQL databases and scaling techniques such as the use of load balancing and distributed systems. Programming intensive using a dynamic high-level general-purpose language. Cross-listed as CPSC 350.
to data visualization methods as well as essential predictive modeling approaches widely used in analytics practice today. Beginning with a foundation in inferential statistics, the course will cover regression, classification, time series, and clustering models. The use of visualization both to explore data and to create narratives around data will also be emphasized. Cross-listed with DSCI 352.
STAT 180 or equivalent. This course will introduce students
Alias
DSCI 352
DATA 353Analytics II: Optimization Models3
statistics course. This course introduces a variety of Management Science models for use in analysis of “business” problems. A computer software package provides the computational basics for case analysis of problem in linear programming, inventory, waiting lines, PERT/CPM and simulation. Cross-listed with DSCI 353.
Prerequisite: DATA 219 or CPSC 220. Practical knowledge of data mining, machine learning, and information retrieval. Students will examine the theoretical foundations of a variety of techniques, gain experience with these techniques using open source software, and learn how to apply them to solve real-world problems. Topics include decision trees, Naïve Bayes, probabilistic retrieval models, clustering, support vector machines, approaches to web mining, and scalable machine learning applications. Cross-listed as CPSC 419.
to techniques of mathematical modeling and computational simulation applied to practical problems. Topics include system dynamics approaches, discrete-event simulation, and agent-based models. Students complete small projects on topics as diverse as population growth, epidemic transmission, queuing theory, and forest fire outbreaks. Cross-listed as CPSC 420.
