Schedule of Classes - UMGC Europe UMGC Europe | Quality academic programs for U.S. military communities in the European and Central Commands
Schedule of Classes
Schedule of Classes
Summer 2024: 8 May - 13 August
 

You are viewing undergraduate classes for students in the Middle East and Africa.

Spring 2021 has a mix of on-site and remote on-site classes. Due to COVID restrictions, some classes will be taught in an interactive, remote format via Zoom. Those locations are listed as "Remote On-Site," and times are shown in Arab Standard Time (AST). Hover over the "ℹ" to confirm the class in your local time.

Summer 2024: 8 May - 13 August

Course Class No. 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.
51161 15 May 2024-09 Jul 2024 Closed Online
Section: 6380 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.
51252 15 May 2024-09 Jul 2024 Open Online
Section: 6381 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.
51308 15 May 2024-09 Jul 2024 Closed Online
Section: 6382 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.
51444 15 May 2024-09 Jul 2024 Closed Online
Section: 6383 Faculty: Moustafa, Rida E 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.
54442 15 May 2024-09 Jul 2024 Open Online
Section: 6384 Faculty: Larson, Deanne M 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.
52347 12 Jun 2024-06 Aug 2024 Closed Online
Section: 6980 Faculty: Kulkarni, Shankar A 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.
52482 12 Jun 2024-06 Aug 2024 Open Online
Section: 6981 Faculty: Ezzati, Parinaz Syllabus Course Materials
DATA 230 Mathematics for Data Science (3)
Prerequisites: STAT 200 and MATH 115 (or MATH 107 and MATH 108) or higher. A practical introduction to the mathematical principles applied within the context of data science. The aim is to understand the mathematical basis of data science and increase awareness of machine learning algorithm assumptions and limitations. Machine learning topics include linear regression, dimensionality reduction, and classification. Projects involve application of linear algebra, probability, vector calculus, and optimization to build data science solutions.
53484 15 May 2024-09 Jul 2024 Closed Online
Section: 6380 Faculty: Martin, Ulyana P Syllabus Course Materials
DATA 230 Mathematics for Data Science (3)
Prerequisites: STAT 200 and MATH 115 (or MATH 107 and MATH 108) or higher. A practical introduction to the mathematical principles applied within the context of data science. The aim is to understand the mathematical basis of data science and increase awareness of machine learning algorithm assumptions and limitations. Machine learning topics include linear regression, dimensionality reduction, and classification. Projects involve application of linear algebra, probability, vector calculus, and optimization to build data science solutions.
54451 15 May 2024-09 Jul 2024 Open Online
Section: 6381 Faculty: Syllabus Course Materials
DATA 230 Mathematics for Data Science (3)
Prerequisites: STAT 200 and MATH 115 (or MATH 107 and MATH 108) or higher. A practical introduction to the mathematical principles applied within the context of data science. The aim is to understand the mathematical basis of data science and increase awareness of machine learning algorithm assumptions and limitations. Machine learning topics include linear regression, dimensionality reduction, and classification. Projects involve application of linear algebra, probability, vector calculus, and optimization to build data science solutions.
53485 12 Jun 2024-06 Aug 2024 Open Online
Section: 6980 Faculty: Huang, Steward H 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.
51162 15 May 2024-09 Jul 2024 Open Online
Section: 6380 Faculty: Wardeh, Mohammed A 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.
51253 15 May 2024-09 Jul 2024 Closed Online
Section: 6381 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.
52483 12 Jun 2024-06 Aug 2024 Open Online
Section: 6980 Faculty: Duan, Chaojie 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.
51229 15 May 2024-09 Jul 2024 Closed Online
Section: 6380 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.
51254 15 May 2024-09 Jul 2024 Open Online
Section: 6381 Faculty: King, Brandon I 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.
52462 12 Jun 2024-06 Aug 2024 Open Online
Section: 6980 Faculty: Chulis, Kimberly 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.
51163 15 May 2024-09 Jul 2024 Closed Online
Section: 6380 Faculty: Heuermann, Lewis Edward 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.
54354 15 May 2024-09 Jul 2024 Open Online
Section: 6381 Faculty: Wrightson, Christopher M 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.
52484 12 Jun 2024-06 Aug 2024 Open Online
Section: 6980 Faculty: Wrightson, Christopher M 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 learning, 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.
51164 15 May 2024-09 Jul 2024 Open Online
Section: 6380 Faculty: Guevara, Yamil E 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 learning, 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.
52485 12 Jun 2024-06 Aug 2024 Open Online
Section: 6980 Faculty: Chakraborty, Sounak Syllabus Course Materials
DATA 440 Advanced Machine Learning (3)
Prerequisites: DATA 230 and DATA 430. 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.
51227 15 May 2024-09 Jul 2024 Open Online
Section: 6380 Faculty: Rai, Paritosh Syllabus Course Materials
DATA 440 Advanced Machine Learning (3)
Prerequisites: DATA 230 and DATA 430. 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.
52459 12 Jun 2024-06 Aug 2024 Open Online
Section: 6980 Faculty: Cook, John A 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.
53831 15 May 2024-09 Jul 2024 Open Online
Section: 6380 Faculty: Schultz, Christopher 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.
52460 12 Jun 2024-06 Aug 2024 Open Online
Section: 6980 Faculty: Burkhardt, Michael H Syllabus Course Materials
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