Schedule of Classes




2022 Summer: May 18 - August 9
Stateside hybrid classes for Winter 2023 and Spring 2023 will be made available the weekend of September 9th, 2022. Registration will open September 12th, 2022.

Course Class No. Section Start & End Date Day Time Status Location
DATA 200 Data Literacy Foundations (3)
An introduction to data and data literacy for students of all majors to enhance their ability to understand and work in today's data-driven world. The aim is to collect, manage, evaluate and apply data in a critical manner and examine the role, significance, and implications of data, including ethical issues within a society, in organizations, or for individuals. Developing skills in data manipulation, analysis, and visualization, students will generate insights from data, build knowledge, and make decisions. Topics include the effective use of cloud-based data storage, collaboration and communication techniques.
Start date has passed. Please register for the next start date.
53096 6380 18 May 2022-12 Jul 2022 Open Online
Faculty: Kinzel, Beate Syllabus Course Materials
DATA 200 Data Literacy Foundations (3)
An introduction to data and data literacy for students of all majors to enhance their ability to understand and work in today's data-driven world. The aim is to collect, manage, evaluate and apply data in a critical manner and examine the role, significance, and implications of data, including ethical issues within a society, in organizations, or for individuals. Developing skills in data manipulation, analysis, and visualization, students will generate insights from data, build knowledge, and make decisions. Topics include the effective use of cloud-based data storage, collaboration and communication techniques.
Start date has passed. Please register for the next start date.
53097 6980 15 Jun 2022-09 Aug 2022 Open Online
Faculty: Dean, Michael Syllabus Course Materials
DATA 220 Introduction to Data Analytics (3)
Prerequisite: STAT 200. A practical introduction to the methodology, practices, and requirements of data science to ensure that data is relevant and properly manipulated to solve problems and address a variety of real-world projects and business scenarios. Focus is on the foundational statistical concepts applied to describing data sets with summary statistics, simple data visualizations, statistical inference, and predictive analytics. The objective is to use data to draw conclusions about the underlying patterns that drive everyday problems through probability, hypothesis testing, and linear model building.
Start date has passed. Please register for the next start date.
53098 6380 18 May 2022-12 Jul 2022 Open Online
Faculty: Perkins, David C Syllabus Course Materials
DATA 220 Introduction to Data Analytics (3)
Prerequisite: STAT 200. A practical introduction to the methodology, practices, and requirements of data science to ensure that data is relevant and properly manipulated to solve problems and address a variety of real-world projects and business scenarios. Focus is on the foundational statistical concepts applied to describing data sets with summary statistics, simple data visualizations, statistical inference, and predictive analytics. The objective is to use data to draw conclusions about the underlying patterns that drive everyday problems through probability, hypothesis testing, and linear model building.
Start date has passed. Please register for the next start date.
53099 6980 15 Jun 2022-09 Aug 2022 Open Online
Faculty: Dave, Linesh Ramesh Syllabus Course Materials
DATA 220 Introduction to Data Analytics (3)
Prerequisite: STAT 200. A practical introduction to the methodology, practices, and requirements of data science to ensure that data is relevant and properly manipulated to solve problems and address a variety of real-world projects and business scenarios. Focus is on the foundational statistical concepts applied to describing data sets with summary statistics, simple data visualizations, statistical inference, and predictive analytics. The objective is to use data to draw conclusions about the underlying patterns that drive everyday problems through probability, hypothesis testing, and linear model building.
Start date has passed. Please register for the next start date.
54162 6381 18 May 2022-12 Jul 2022 Open Online
Faculty: Trajkovski, Goran Syllabus Course Materials
DATA 300 Foundations of Data Science (3)
Prerequisites: CMIS 102 and DATA 220. An examination of the role of data science within business and society. The goal is to identify a problem, collect and analyze data, select the most appropriate analytical methodology based on the context of the business problem, build a model, and understand the feedback after model deployment. Emphasis is on the process of acquiring, cleaning, exploring, analyzing, and communicating data obtained from variety of sources. Assignments require working with data in programming languages such as Python, wrangling data programmatically and preparing data for analysis, using libraries like NumPy and Pandas.
Start date has passed. Please register for the next start date.
53100 6380 18 May 2022-12 Jul 2022 Open Online
Faculty: Cook, John A Syllabus Course Materials
DATA 300 Foundations of Data Science (3)
Prerequisites: CMIS 102 and DATA 220. An examination of the role of data science within business and society. The goal is to identify a problem, collect and analyze data, select the most appropriate analytical methodology based on the context of the business problem, build a model, and understand the feedback after model deployment. Emphasis is on the process of acquiring, cleaning, exploring, analyzing, and communicating data obtained from variety of sources. Assignments require working with data in programming languages such as Python, wrangling data programmatically and preparing data for analysis, using libraries like NumPy and Pandas.
Start date has passed. Please register for the next start date.
53101 6980 15 Jun 2022-09 Aug 2022 Open Online
Faculty: Schultz, Christopher Syllabus Course Materials
DATA 335 Data Visualization (3)
Prerequisites: DATA 220 and IFSM 330. An overview of the fundamentals of data visualization principles in the context of business and data science. Practical focus on data visualization of different data types including time -series, multidimensional data, creating dynamic tables, heatmaps, infographs, and dashboards. Hands on projects will require exploring data visually at multiple levels to find insights to create a compelling story and incorporating visual design best practices to better communicate insights to the intended audience, such as business stakeholders. Projects are selected from a wide range of content areas such as retail, marketing, healthcare, government, basic sciences, and technology.
Start date has passed. Please register for the next start date.
53102 6380 18 May 2022-12 Jul 2022 Open Online
Faculty: Heuermann, Lewis Edward Syllabus Course Materials
DATA 335 Data Visualization (3)
Prerequisites: DATA 220 and IFSM 330. An overview of the fundamentals of data visualization principles in the context of business and data science. Practical focus on data visualization of different data types including time -series, multidimensional data, creating dynamic tables, heatmaps, infographs, and dashboards. Hands on projects will require exploring data visually at multiple levels to find insights to create a compelling story and incorporating visual design best practices to better communicate insights to the intended audience, such as business stakeholders. Projects are selected from a wide range of content areas such as retail, marketing, healthcare, government, basic sciences, and technology.
Start date has passed. Please register for the next start date.
53103 6980 15 Jun 2022-09 Aug 2022 Open Online
Faculty: Wrightson, Christopher Michael Syllabus Course Materials
DATA 430 Foundations of Machine Learning (3)
Prerequisite: DATA 300. A hands-on introduction to machine learning principles and methods that can be applied to solve practical problems. Topics include supervised and unsupervised, especially linear regression, logistic regression, decision tree, naïve Bayes and clustering analysis. Focus is on using data from a wide range of domains, such as healthcare, finance, marketing, and government, to build predictive models for informed decision making. Discussion also covers handling missing data, performing cross-validation to avoid overtraining, evaluating classifiers, and measuring precision.
Start date has passed. Please register for the next start date.
53104 6380 18 May 2022-12 Jul 2022 Open Online
Faculty: Chesney, Steve L Syllabus Course Materials
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