6 courses with the subject DAT, 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.
DAT 153Database Programming
Instructor Benbow This course provides an introduction to database programming. Using the SQL language, students will learn how to explore and interact with databases, create and manipulate data, and extract knowledge through analytics and visualization. They will also learn fundamental database design principles and methods, and will apply this knowledge to develop databases of their own. While the course will focus predominantly on the applied use of relational databases, we will also introduce neighboring topics like non-relational data, APIs, extract-transform-load (ETL) processes, and business intelligence (BI) systems. Students taking DAT 153 will not be eligible to take CSC 353 because of a significant overlap in their learning objectives. DAT 153 provides a deeper exploration of practical database programming methods than CSC 353 does, but this comes at the expense of lessons on the theoretical basis of relational database systems. While CSC 353 is aimed specifically students majoring in Computer Science, DAT 153 is accessible for students in the Data Science program, or those who have credit or CSC 121. Satisfies Data Science minor requirement. Satisfies Mathematical and Quantitative Thought requirement. Prerequisites & Notes
one of CSC 110, CSC 121, CSC 240, CSC 209, or DIG 120 Not open to students with credit for or enrolled in CSC 353
DAT 205Statistical Linear Models
Instructor Wang This is an applied statistics course with a focus on linear models. Building upon foundations of statistics, students will learn to specify and fit both ordinary linear models (OLS) and more advanced models. Specific topics beyond OLS include generalized linear models (GLM), two-way ANOVA, linear mixed models, and other extensions. Students will also learn model diagnostics as an important pillar of data science . Students will foster a deeper understanding of the flexibility of statistical modeling, which culminates in a course project with applications in real world data to address practical problems. Counts for the Interdisciplinary minor in Data Sciece. Satisfies Mathematical & Quantitative Thought in the Ways of Knowing. Prerequisites & Notes An introductory statistics course equivalent to MAT 104/105 (e.g., BIO 240, ECO 105, POL 182, PSY 300, SOC 201).
Instructor Wang This course comprehensively covers Bayesian statistics from its introduction to advanced computing and model building. Topics covered include distribution theory, conjugate models, hierarchical models, and statistical simulation and computing with Markov Chain Monte Carlo (MCMC). Students will also learn the foundation of statistical model building and diagnostics. With a balance of theoretical rigor and practical applications, the class will culminate in a course project to address real world problems. Satisfies Data Science minor requirement. Prerequisites & Notes A semester of college-level introductory statistics course in Sociology, Economics, Political Science, Psychology, or Mathematics, such as SOC 201, ECO 204, POL 182, or MAT 341. Proficiency in calculus (e.g. Math 113 or MAT140). Strong foundational knowledge in R or CSC 121.
Instructor Benbow Data engineering is the backbone that makes data science possible by providing systems that can extract, transform, and load data at scale. This course focuses on how to build the infrastructure on which the modern data stack operates, including pipelines, warehouses, lakehouses, and semantic models. Students will develop systems that can ingest data from a variety of sources, including files, APIs, and non-relational databases, and they will learn to process this information by streaming it in real-time or loading it in large, asynchronous batches. We will also explore how data engineering overlaps with business intelligence, machine learning, and other related disciplines. Satisfies Data Science minor requirement. Prerequisites & Notes CSC 221, and either one of DAT 153 (Database Programming) or CSC 353 (Database Systems)
Instructor Heyer This course will build on students’ understanding of statistics and basic econometrics to develop further the empirical model building skills they have acquired previously. The course will provide an overview of classical and modern approaches to forecasting in business, economics, and finance. Topics include regression analysis, exponential smoothing and filtering, ARIMA models, modeling and forecasting trend and seasonality, evaluating and combining forecasts. Students will also write forecast reports and deliver oral presentations of their forecasts to classmates as well as learn how to critique constructively others’ reports and presentations. Satisfies Data Science minor requirement. Satisfiest Mathematical and Quantitative Thought requirement. Prerequisites & Notes An introductory statistics course equivalent to MAT 104/105 (e.g., BIO 240, ECO 204, POL 182, PSY 300, SOC 201).
