Participants typically take two courses per term, with each course meeting one evening per week.
Students must earn at least a 'B' (3.0) grade point average -- both overall and on final projects completed as part of the capstone course, "Master's Capstone: Research, Synthesis, Applications."
(We accept up to 6 of your previously earned graduate level college credits. Schedule an admissions consultation by emailing: email@example.com to have your transcripts evaluated)
Introduction to Data Science and Python
A hands-on introduction to the field of Data Science and its applications. Covers a wide range of topics to provide an overview of the use of data in different fields. Provides hands-on practice with basic tools and methods of data analysis. Prepares students to use data in their field of study and in their work and to effectively communicate quantitative findings. Focus is on the use of Python in data analysis and mastering tools for acquiring, parsing, manipulating, and preparing data for statistical analysis.
Applied Statistics and Data Analysis
Introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. Covers techniques in modern data analysis: regression and econometrics, prediction, design of experiment, randomized control trials (and A/B testing), and data visualization.
Introduction to Machine Learning
Suggested Prerequisite: Applied Statistics and Data Analysis. Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and Bayesian networks.
Applications of Data Analytics and Development
Suggested Prerequisite: Applied Statistics and Data Analysis. This course develops an overview of the challenges of developing and applying analytics for insight and decision making. Examples and cases will come from engineering, social media analytics, business analysis and other data-centered domains. The focus will be on programming and data manipulation techniques for constructing analytics-based applications. Topics include SQL or no-SQL databases, using web service API’s to acquire data, introduction to Hadoop and MapReduce, and use of third-party analytic component API’s.
Data Analysis Electives
- Artificial Intelligence
- Practices for Big Data
- High-Performance Parallel Computing
- Time Series Analysis
- Cloud Computing
- Pattern Recognition
- Predictive Modeling
- Visualization Tools
- Information Retrieval and Analysis
- Internet of Things
- Distributed Computing
- Data Mining
- Data Ethics
- Business Data Analytics
- Healthcare Data Analytics
- Data Analysis for Security
- Government Data and Analysis
- Transportation Informatics
- Climate and Ecosystem Monitoring
Data Science and Analytics Practicum
Catholic University and its industry partners are establishing focus areas that students will address in the practicum. The areas are selected to highlight the power of data and analysis in their solutions. In their final semester, students select one of the issue areas and a method of execution. Students currently working can choose issue areas related to the established set, but which are tailored to their work environment and use data sets supplied by their employer. Students will work throughout the semester in a professional project-like manner. They will submit project proposals, plan of action & milestone charts, and time lines as part of the practicum. Students will have scheduled reviews at various points that will be held in conjunction with the industry partners. At the end of the semester, each student will give a 30-minute presentation on their project to a panel made up of CUA faculty and industry partners. Depending on the scope of the project, teams can be formed to address multiple aspects of the available data.