Data Literacy Foundations
DATA 200 | 3 Credits
Course Desc: 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.
Mathematics for Data Science
DATA 230 | 3 Credits
Course Desc: 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.
Foundations of Data Science
DATA 300 | 3 Credits
Course Desc: 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.
Introduction to Data Analytics
DATA 320 | 3 Credits
Course Desc: 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.
Business Intelligence and Data Management
DATA 330 | 3 Credits
Course Desc: A hands-on, project-based introduction to databases, business intelligence, and data management. The aim is to design secure industry-standard databases and utilize business intelligence and data management techniques and technologies to support decision making. Topics include data and relational databases, SQL queries, business intelligence tools and overall alignment with business strategy. Students may receive credit for only one of the following courses: DATA 330 or IFSM 330.
Data Visualization
DATA 335 | 3 Credits
Course Desc: 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.
Foundations of Machine Learning
DATA 430 | 3 Credits
Course Desc: 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.
Advanced Machine Learning
DATA 440 | 3 Credits
Course Desc: 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.
Advanced Data Science
DATA 445 | 3 Credits
Course Desc: 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.
Data Ethics
DATA 450 | 3 Credits
Course Desc: Prerequisite: DATA 430. A study of ethics within the context of data science, machine learning, and artificial intelligence. Emphasis is on examining data and model bias; building explainable, fair, trustable, and accurate predictive modeling systems; and reporting responsible results. 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.
Artificial Intelligence Solutions
DATA 460 | 3 Credits
Course Desc: (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.
Data Science Capstone
DATA 495 | 3 Credits
Course Desc: 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.
Decision Analytics
DATA 605 | 3 Credits
Course Desc: A project-driven study of the processes and technology designed to enhance data-driven decision-making, integrating artificial intelligence with human decision-making. The goal is to apply creative methods to ask better questions, identify core problems, develop models, interpret results, and convey findings to various audiences. Topics include the use of commercial software to manage, analyze, and report on data and create actionable insights across a range of contexts, including societal, business, political, intelligence, healthcare, and media/entertainment. Discussions explore best practices for the long-term success of an analytics project in terms of project management and communications, with an emphasis on the analytics life cycle.
Decision Management Systems
DATA 610 | 6 Credits
Course Desc: An examination of the process of decision making in large organizations and the technologies that can be used to enhance data-driven decision making. Focus is on the underlying framework of good decision making, featuring operational decisions as reusable assets that can be automated through the creation of business rules. How data can add analytic insight to improve decisions is explored. Discussion covers best practices for long-term success of an analytics project in terms of project management and communications with an emphasis on the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology.
AI Ethics
DATA 615 | 3 Credits
Course Desc: An overview of current ethical issues in artificial intelligence (AI) and data science arising throughout the analytics life cycle. The goal is to create ethically driven and responsible AI solutions that enhance human problem-solving and decision-making, identify the sources of bias and discrimination in machine learning, and build models that promote trust in data. Topics include established and emerging guiding principles for AI ethics, such as explainability, fairness, robustness, transparency, accountability, inclusiveness, and privacy.
Data Management and Visualization
DATA 620 | 6 Credits
Course Desc: Prerequisite: DATA 610. A presentation of the fundamental concepts and techniques in managing and presenting data for effective data-driven decision making. Topics in data management and design include data design approaches for performance and availability, such as data storage and indexing strategies; data warehousing, such as requirement analysis, dimensional modeling, and ETL (extract, transform, load) processing; and metadata management. Topics in data visualization include data types; data dimensionalities, such as time-series and geospatial data; forms of data visualization, including heat maps and infographs; and best practices for usable, consumable, and actionable data/results presentation.
Data Visualization
DATA 625 | 3 Credits
Course Desc: A project-based exploration of the concepts and techniques used in data manipulation, organization, and visualization. The goal is to create informative visualizations depending on the nature of the data and the objectives of analysis. Topics include data types; data dimensionalities, such as time-series and geospatial data; and best practices in scripting and data visualization for formatting and presenting usable, consumable, and actionable data that ensure data integrity standards. Industry standards software tools are followed for project development. Students may receive credit for only one of the following courses: DATA 620 or DATA 625.
Machine Learning
DATA 630 | 6 Credits
Course Desc: Prerequisite: DATA 620. A practical survey of several modern machine learning techniques that can be applied to make informed business decisions. Discussion covers supervised and unsupervised learning techniques, including naïve Bayes, regression, decision trees, neural networks, nearest neighbor, and cluster analysis. How each of these methods learns from past data to find underlying patterns useful for prediction, classification, and exploratory data analysis is examined. Discussion covers significant tasks in real-world applications, including handling of missing data, evaluating classifiers, and measuring precision. Major software tools are used to apply machine learning methods in a wide range of domains such as healthcare, finance, marketing, and government.
Data Management
DATA 635 | 3 Credits
Course Desc: A project-based study of the concepts, principles, and techniques of managing data throughout its life cycle for effective data-driven decision-making. The aim is to apply best practices for data design, data integrity, data quality, and data governance. Topics include SQL and NoSQL; distributed and cloud databases; data lakes and data warehousing; extract, transform, and load (ETL) processing; and metadata management. Students may receive credit for only one of the following courses: DATA 620 or DATA 635.
Predictive Modeling
DATA 640 | 6 Credits
Course Desc: Prerequisite: DATA 630. An introduction to advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. Advanced statistical and machine learning algorithms such as support vector machines (SVM), regression, deep learning, and ensemble models are used to develop, assess, compare, and explain complex predictive models. Topics include high-performance modeling, genetic algorithms, and best practices for selecting methods and tools to build predictive models. Major software tools are used to apply predictive modeling in a wide range of domains for improved decision-making in real business situations.
Machine Learning
DATA 645 | 3 Credits
Course Desc: A project-based study of the fundamental concepts and algorithms of machine learning. The aim is to evaluate different algorithms and methods and build models that learn from past data to find underlying patterns useful for prediction, classification, and exploratory data analysis and that can be applied to make informed business decisions. Topics include supervised and unsupervised machine learning techniques, naïve Bayes classifiers, regression, decision trees, and cluster analysis. Discussion explores significant tasks in real-world applications, including handling missing data, evaluating classifiers, and measuring precision. Major software tools are used to apply machine learning methods in a wide range of domains, such as healthcare, finance, marketing, and government.
Big Data Analytics
DATA 650 | 6 Credits
Course Desc: Prerequisite: DATA 640. An introduction to concepts, approaches and techniques in managing and analyzing large data sets for improved decision-making in real business situations. Topics include text analytics, sentiment analysis, stream analytics, AI and cognitive computing. Discussion also covers how to identify the kinds of analyses to use with big data and how to interpret the results. Advanced tools and basic approaches are used to query and explore data using Hadoop Platform and in-memory analytical tools like Spark ML.
Deep Learning and Neural Networks
DATA 655 | 3 Credits
Course Desc: Prerequisite: DATA 645. A practical exploration of the fundamental concepts, architectures, and applications of deep learning in the field of artificial intelligence. The goal is to develop deep learning models and apply them to solve real-world problems in a wide range of domains, such as healthcare, finance, marketing, and cybersecurity. Topics include backpropagation, convolutional networks, recurrent networks, and generative adversarial networks, and their applications.
Advanced Topics in Data Science
DATA 660 | 3 Credits
Course Desc: Prerequisite: DATA 645. A project-based study of advanced concepts in predictive modeling and techniques to discover patterns in data. The aim is to identify variables with the most predictive power and to develop, assess, compare, and explain complex predictive models. Topics include advanced statistical and machine learning algorithms, support vector machines (SVM), ensemble models, and reinforcement learning. Discussion explores high-performance modeling and best practices for selecting methods and tools to explore large data sets using industry-standard software ad cloud applications, such as Apache Spark ML, Amazon Kinesis, and Google BigQuery.
AI Applications
DATA 665 | 3 Credits
Course Desc: Prerequisite or corequisite: DATA 655.A comprehensive overview of artificial intelligence with a specific focus on Natural Language Processing (NLP), Computer Vision, Recommender Systems, and Anomaly Detection. The aim is to develop AI applications relevant to real-world scenarios in multiple disciplines and domains. Topics include text and images classification, sentiment analysis, natural language and image generation, and content-based filtering. Discussions explore fraud detection, network intrusion detection, and system health monitoring.
Data Analytics Capstone
DATA 670 | 6 Credits
Course Desc: Prerequisite: DATA 650. Completion of a major analytics project designed to integrate knowledge and skills gained from previous coursework and provide a complete analytics experience, including problem scoping (framing), data set preparation, comprehensive data analysis and visualization, and predictive model development. Several peer-reviewed presentations are included to enhance the ability to "tell the story" and explain project approach and results. Projects are selected from student organizations, special government agency requests, or other faculty-approved sources. The project culminates in a complete analytics report and presentation.
Specialization Project
DATA 675 | 3 Credits
Course Desc: Prerequisite: DATA 645. An in-depth exploration and application of data science techniques and methodologies to solve complex problems and answer research questions within a specialization. The objective is to conduct a detailed research analysis of the nature and types of data within a selected domain, identify a problem addressing specific challenges or opportunities related to data science, and develop the main stages of a data project. Topics include AutoML, model evaluation, results interpretation, and recommendations. Projects are sourced from various sectors, including finance, marketing, supply chain management, cybersecurity, healthcare, medicine, pharmaceuticals, environmental management, and government, as well as any other domain that aligns with the chosen specialization and research interests.
Workplace Learning in Data Analytics
DATA 686 | 3 Credits
Course Desc: Prerequisites: 12 graduate credits in the program and prior program approval (requirements detailed online at umgc.edu/wkpl). The integration of discipline-specific knowledge with new experiences in the work environment. Tasks include completing a series of academic assignments that parallel work experiences.
Data Analytics Capstone
DATA 690 | 3 Credits
Course Desc: Prerequisite: Completion of 24 credits of program coursework, including all core courses. Completion of a major analytics project designed to integrate knowledge and skills gained from previous coursework and provide a complete analytics experience, including problem scoping (framing), data set preparation, comprehensive data analysis and visualization, and predictive model development. Activities include several peer-reviewed presentations to enhance the ability to "tell the story" and explain project approach and results. Projects are selected from student organizations, special government agency requests, or other faculty-approved sources. The project culminates in a complete analytics report and presentation.