Course offerings listed below may vary from year to year based on course availability. For the most up-to-date courses, course requirements and descriptions, always refer to the current University Catalog. View the current Catalog here.
DAT 111 Statistics for Data Science I
An introduction to descriptive statistics as well as an introduction to tools and technologies used in data science. Statistics topics include data types, graphical and numerical representations and summaries of data, distributions, and linear regression. Additional topics include data cleaning and sorting, joins, unions, VLookup and pivot tables. Students will work with real-life data sets and use MS Excel and R Studio throughout the course to manipulate, analyze and visualize data. Prerequisite: MTH 102 or placement. Not open to students with credit for DAT 110.
DAT 112 Statistics for Data Science II
An introduction to inferential statistics as well as an introduction to tools and technologies used in data science. Topics include distributions, probability density functions, sampling distributions, Central Limit Theorem, confidence intervals, hypothesis testing, linear and multiple linear regression. Students will work with real-life data sets and use MS Excel and R Studio throughout the course to help with analysis and visualization. Prerequisite: DAT 111. Not open to students with credit for DAT 110.
DAT 210 Visualization with Tableau Software
Introduction to data visualization with Tableau software. Topics include connecting to data files, data preparation, dashboards and story points. In addition to Tableau-specific topics, best practices for creating effective visualizations and history of visual data storytelling will be discussed. Students will work with real-life data sets and complete a project. Prerequisites: CIS 107 or CIS 150 or MTH 140.
DAT 341 Foundations of Data Analytics I**
In an increasingly data-driven world, everyone should be able to understand the numbers that govern our lives. Whether or not you want to work as a data analyst, being “data literate” will help you in your chosen field. In this course, you’ll learn the core concepts of inference and data analysis by working with real data. By the end of the term, you’ll be able to analyze large datasets and present your results. This online class has optional live sessions. Topics include actionable data analytics, Python basics, A/B testing, Bayes’ Theorem. Prerequisite: DAT 112.
DAT 342 Foundations of Data Analytics II**
This course is intended as a continuation of Foundations of Data Analytics I. In this course, you’ll learn how Data Analytics are applied within the workforce. Particular attention will be paid to the role of the Data Scientist or Analyst, machine learning and the applications of Big Data. By the end of the term, you will be able to design and execute a range of data-driven experiments. This online class has optional live sessions. Topics include experiment design, machine learning, big data analytics. Prerequisite: DAT 341.
DAT 441 Principles and Techniques of Data Analytics I**
This course is based heavily on UC Berkeley’s Data 100 class. Data Analytics combines data, computation and inferential thinking to solve challenging problems and understand their intricacies. This class explores key principles and techniques of data science, and teaches students how to create informative data visualizations. It also explores particular concepts of Linear Algebra which are central to Data Science. Topics include linear algebra, data sampling and cleaning, data visualization, regression analysis and modeling. Prerequisite: DAT 342.
DAT 442 Principles and Techniques of Data Analytics II**
This course builds on Principles and Techniques of Data Analytics I to provide students with a more robust understanding of the tools of a Data Scientist. Data Analytics combines data, computation and inferential thinking to solve challenging problems to thereby better understand the world. This class explores key principles and techniques of data science, including quantitative critical thinking and algorithms for machine learning methods. It will also introduce students to the ways in which data analytics is deployed in healthcare, marketing, political science, criminal justice, and other fields. Topics include data analytics in the real world, feature engineering, regression analysis and machine learning, data processing. Prerequisite: DAT 441.
DAT 470 Capstone Project in Data Science
Students will work in small teams to explore a real-life problem. Questions and data for the projects will come from industry partners. Prerequisite: DAT 330
DAT 471 Data Science Practicum**
This course is a capstone project in which students are asked to work through a full data science workflow on a set of real data drawn from sports, politics, business or public health. This course exists to prepare students for the kind of work they will do on Data Science or Analytics teams, and as such, also features an emphasis on interviewing for jobs in the space and communicating results to stakeholders. Prerequisite: DAT 442.
**Offered online via ODU's partnership with Rize, an education company seeking to prepare college students for careers in the fastest-growing fields. Rize courses are designed by top academics and industry leaders, vetted by ODU, and taught by faculty who are experts in the field.