Schedule of Classes




2023 Summer: May 10 - August 15
Stateside hybrid classes for Summer 2023 will be made available the weekend of February 10th, 2023. Registration will open February 13th, 2023.

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.
51786 6380 17 May 2023-11 Jul 2023 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.
52759 6980 14 Jun 2023-08 Aug 2023 Closed Online
Faculty: Trajkovski, Goran 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.
53271 6381 17 May 2023-11 Jul 2023 Open Online
Faculty: Gathoni, Priscilla 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.
53272 6981 14 Jun 2023-08 Aug 2023 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.
54246 6382 17 May 2023-11 Jul 2023 Open Online
Faculty: Trajkovski, Goran 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.
54464 6383 17 May 2023-11 Jul 2023 Open Online
Faculty: Chulis, Kimberly 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.
54679 6982 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Dogan, Tuncay Syllabus Course Materials
DATA 300 Foundations of Data Science (3)
Prerequisite: STAT 200. 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.
51787 6380 17 May 2023-11 Jul 2023 Closed Online
Faculty: Zimmer, Janet M Syllabus Course Materials
DATA 300 Foundations of Data Science (3)
Prerequisite: STAT 200. 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.
53273 6381 17 May 2023-11 Jul 2023 Open Online
Faculty: Genao, Domingo Syllabus Course Materials
DATA 300 Foundations of Data Science (3)
Prerequisite: STAT 200. 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.
53274 6980 14 Jun 2023-08 Aug 2023 Closed Online
Faculty: Zeleke, Abebaw Syllabus Course Materials
DATA 300 Foundations of Data Science (3)
Prerequisite: STAT 200. 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.
54733 6982 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Syllabus Course Materials
DATA 320 Introduction to Data Analytics (3)
Formerly DATA 220. 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 application of foundational statistical concepts to describing datasets 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.
53103 6380 17 May 2023-11 Jul 2023 Open Online
Faculty: Dean, Michael Syllabus Course Materials
DATA 320 Introduction to Data Analytics (3)
Formerly DATA 220. 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 application of foundational statistical concepts to describing datasets 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.
53104 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Tran, Anh L Syllabus Course Materials
DATA 320 Introduction to Data Analytics (3)
Formerly DATA 220. 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 application of foundational statistical concepts to describing datasets 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.
53276 6381 17 May 2023-11 Jul 2023 Open Online
Faculty: Britto, Joseph Solomon Syllabus Course Materials
DATA 335 Data Visualization (3)
Prerequisite: DATA 320. 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.
51788 6380 17 May 2023-11 Jul 2023 Open Online
Faculty: Wrightson, Christopher Michael Syllabus Course Materials
DATA 335 Data Visualization (3)
Prerequisite: DATA 320. 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.
53278 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Perkins, David C 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.
51789 6380 17 May 2023-11 Jul 2023 Open Online
Faculty: Yousefian, Peyman 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.
53279 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Guevara, Yamil E Syllabus Course Materials
DATA 440 Advanced Machine Learning (3)
Prerequisites: DATA 430 and MATH 140. A project-based study of advanced concepts and applications in machine learning (ML) such as neural networks, support vector machines (SVM), ensemble models, deep learning, and reinforced learning. Emphasis is on building predictive models for practical business and social problems, developing complex and explainable predictive models, assessing classifiers and comparing their performance. All stages of the machine learning life cycles are developed, following industry best practices for selecting methods and tools to build ML models, including Auto ML.
Start date has passed. Please register for the next start date.
53083 6380 17 May 2023-11 Jul 2023 Open Online
Faculty: Rai, Paritosh Syllabus Course Materials
DATA 440 Advanced Machine Learning (3)
Prerequisites: DATA 430 and MATH 140. A project-based study of advanced concepts and applications in machine learning (ML) such as neural networks, support vector machines (SVM), ensemble models, deep learning, and reinforced learning. Emphasis is on building predictive models for practical business and social problems, developing complex and explainable predictive models, assessing classifiers and comparing their performance. All stages of the machine learning life cycles are developed, following industry best practices for selecting methods and tools to build ML models, including Auto ML.
53084 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Menon Gopalakrishna, Prahlad Syllabus Course Materials
DATA 445 Advanced Data Science (3)
Prerequisites: DATA 335 and DATA 430. A project-based introduction to the concepts, approaches, techniques, and technologies for managing and analyzing large data sets in support of improved decision making. Activities include using technologies such as Spark, Hive, Pig, Kafka, Hadoop, HBase, Flume, Cassandra, cloud analytics, container architectures, and streaming real-time platforms. Discussion covers how to identify the kinds of analyses to use with big data and how to interpret the results.
53086 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Schultz, Christopher Syllabus Course Materials
DATA 450 Data Ethics (3)
Prerequisite: DATA 430. Recommended: CSIA 300. A study of ethics within the context of data science, machine learning and artificial intelligence. The emphasis is on examining data and model bias, building explainable, fair, trustable, and accurate predictive modeling systems, and on reporting responsible results. Additional topics include the technology implications of human-centered machine learning and artificial intelligence on decision-making in organizations and government and the broader impact on society, including multinational and global effects.
Start date has passed. Please register for the next start date.
53087 6380 17 May 2023-11 Jul 2023 Open Online
Faculty: Herranz, Edward Syllabus Course Materials
DATA 450 Data Ethics (3)
Prerequisite: DATA 430. Recommended: CSIA 300. A study of ethics within the context of data science, machine learning and artificial intelligence. The emphasis is on examining data and model bias, building explainable, fair, trustable, and accurate predictive modeling systems, and on reporting responsible results. Additional topics include the technology implications of human-centered machine learning and artificial intelligence on decision-making in organizations and government and the broader impact on society, including multinational and global effects.
53088 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Saripalli, Venkata R Syllabus Course Materials
DATA 460 Artificial Intelligence Solutions (3)
(Designed to help prepare for the AWS Certified Machine Learning or Microsoft Designing and Implementing an Azure AI Solution exam.) Prerequisite: DATA 430. A hands-on, project-based study of artificial intelligence and machine learning solutions to complex problems. Topics include natural language processing, computer vision, and speech recognition.
Start date has passed. Please register for the next start date.
53268 6380 17 May 2023-11 Jul 2023 Open Online
Faculty: Bolton, Jeremy Syllabus Course Materials
DATA 460 Artificial Intelligence Solutions (3)
(Designed to help prepare for the AWS Certified Machine Learning or Microsoft Designing and Implementing an Azure AI Solution exam.) Prerequisite: DATA 430. A hands-on, project-based study of artificial intelligence and machine learning solutions to complex problems. Topics include natural language processing, computer vision, and speech recognition.
53269 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Dave, Linesh Ramesh Syllabus Course Materials
DATA 495 Data Science Capstone (3)
Prerequisites: DATA 440, DATA 445, and DATA 450. A project-based, practical application of the knowledge, technical skills, and critical thinking skills acquired during previous study designed to showcase the student¿s data science expertise. Individually selected projects include all phases of machine learning life cycles and a peer-reviewed final report and presentation. Topics are selected from student-affiliated organizations or employers, special government/private agency requests, or other faculty-approved sources in a wide range of domains, such as healthcare, financial services, marketing, sciences, and government.
53270 6980 14 Jun 2023-08 Aug 2023 Open Online
Faculty: Chesney, Steve L Syllabus Course Materials
DATA 495 Data Science Capstone (3)
Prerequisites: DATA 440, DATA 445, and DATA 450. A project-based, practical application of the knowledge, technical skills, and critical thinking skills acquired during previous study designed to showcase the student¿s data science expertise. Individually selected projects include all phases of machine learning life cycles and a peer-reviewed final report and presentation. Topics are selected from student-affiliated organizations or employers, special government/private agency requests, or other faculty-approved sources in a wide range of domains, such as healthcare, financial services, marketing, sciences, and government.
53645 6981 14 Jun 2023-08 Aug 2023 Open Online
Faculty: George, Ajish D Syllabus Course Materials
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