Online Master of Science in Data Science

This course introduces advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. Students are introduced to descriptive, predictive, prescriptive analytics, and optimization models. The course utilizes Microsoft Excel to engineer and analyze business models. Students identify the proper use of complete regression, optimization, and exponential smoothing models.  

Advanced techniques for data transformation and modeling are the focus of this course. The course covers various methods for cleaning, transforming and integrating large datasets. The course also addresses the application of data science models for the purpose of making strategic, data-driven decisions.

This course focuses on statistical analysis of data for professional applications or research with an emphasis on quantitative methodologies. The course covers populations, sample selection, and descriptive and inferential statistics. Significance, Chi Square, correlations, analysis of variance and simple regression, and concepts of reliability, validity, and levels of measurement are addressed.  

This course follows a logical progression from basic data types into normalization and relational database design. Stages of database design are addressed, as well as techniques for documenting database workflows and schemas. The course explores implementing methods to promote spatial and tabular data integrity and facilitate analytical workflows. Structured query language (SQL) for querying, modifying, and managing data is also covered. The course includes enterprise database topics such as permissions, replication, and performance optimization.

The course utilizes data processing requirements necessary to implement technology-based analytics. The course explores the strengths and limitations of various data formats to make better decisions. The importance of structured and unstructured data formats as well as performing methods of data extraction, transformation, and loading are covered. Data wrangling methodologies explore constructing custom data pipelines to support efficient analysis. These methods include cleaning, filtering, standardizing, and categorizing data. Processes to review data for accuracy, consistency, and completeness are covered, as well as techniques to mitigate error and improve data integrity. The course also investigates legal and ethical considerations of data management.

The course focuses on core programming concepts such as classes and objects, controlling flow, batch processing, and error handling while working in the context of data processing, analysis, and visualization. The course explores a variety of Python packages and integration for project development. Using Python to automate workflows and create custom visualizations is discussed, and students are able to explore tabular data, spatial data, cybersecurity, and data science applications of Python.

This course introduces students to effective use of the internet and technology for sharing spatial and non-spatial data, visualizations, and interactive applications via the web. The course examines cloud data architecture for managing, analyzing, and serving data over the Internet. Students are introduced to web-based programming languages and interfaces (APIs) for presentation, visual analysis/intelligence, and communication/presentation of data via the web.

Prerequisite: GM 630

The course focuses on R programming concepts for analytical and statistical applications involving spatial data and non-spatial data used in various technology fields. The course features scripts and data model applications useful in promoting decision-making, integrating R into technology software platforms, and focusing on effective communication through visual intelligence.

Prerequisite: DIGA 660

This course introduces machine learning concepts and techniques used in data science. Machine learning models and associated computer programming languages are examined, with extensive applications of advanced Python programming. The course introduces supervised and unsupervised machine learning, learning theory, reinforcement learning and adaptive control. Machine learning applications to areas such as data mining and natural language processing are explored. Machine learning computer technology is utilized. A focus of the course is the application of machine learning to data collection and preprocessing, statistical modelling, and predictive analytics.

Prerequisite: BIA 630

This course focuses on the framework for utilizing the Python programming language for artificial intelligence, developing supervised and unsupervised machine learning applications. The course explores performing, creating, and scripting. Students gain experience in programming Python scripts and data science applications and identify how artificial intelligence tools can be used to aid in business decision-making.