Course requirements for each specific program can be found at the links below:
Data Science and Analytics Master of Science
Data Science and Analytics Certificate
Undergraduate + Graduate in 5 years (4+1)
Micro-credential in Data Management an Storytelling
“You will never take the same type of class twice with many classes to choose from and with great professors who care about you succeeding.”
- James Gravas '20
Click on the course to jump to the description.
Prerequisite: Instructor permission.
Descriptive statistics, probability concepts, discrete and continuous probability distributions, sampling distributions, interval estimation and hypothesis testing of one and two population means, proportions and variances, non-parametric tests, simple linear regression and correlation, one-way analysis of variance.
Prerequisite: Instructor permission.
Introductory topics in calculus, optimization, linear algebra and discrete mathematics useful for data scientists. Networking concepts relevant to data analytics approached from a mathematical point of view. Mathematical programming to implement a variety of numerical methods.
Prerequisites: Graduate standing.
Introduction to data analysis in Excel, Tableau, and Python; execute queries to extract data from a relational database; Data Science Life Cycle; tools and techniques to perform all the phases of the data science life cycle; Introduction to Machine Learning concepts.
Prerequisites: CIS 512 or DSA 512 or equivalent.
Introduction to Machine Learning Techniques for Data Science; mathematical methods; algorithms; application to academia, industry and business problems. Fundamental concepts and terms; methods, tools, and techniques. Supervised and unsupervised learning; identification of learning problems; data sources; analytical approaches; algorithm implementation; interpretation and reporting.
Prerequisites: Graduate standing.
Current practices in project management as applied to math and science projects. Hands-on experience with the skills, tools, and techniques required in different phases of a project's life cycle, including project selection, project planning, project staffing and organization, task scheduling, project scope management, budgeting and progress reporting, risk management, quality management, project communications, and use of appropriate project management software tools. Techniques for communicating and motivating teams throughout the project life cycle. Emphasis on team building and practicing project management techniques through the use of science-based cases.
Prerequisites: Graduate-level standing.
Intend to develop strategic thinking about communication of quantitative information and improve writing, presentation, and interpersonal communication skills for mathematicians and scientists in a variety of settings (i.e. industrial, managerial, academic, research). Includes a review of “best practices” or guidelines that have been derived from both research and experience. Students will put those guidelines into practice, using a workshop format that will rely heavily on discussion and in-class exercises.
In-depth examination of rapidly and significantly changing disciplinary issues, topics, or practices. Offered occasionally
Prerequisites: Instructor permission.
Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security.
Prerequisites: Graduate standing, instructor permission, and 3.0 minimum GPA
Internship and team project (a.k.a. professional labs). Internships acquaint students with specialized resources in industry. Professional labs allow students to apply skills to real-world challenges.
Prerequisite: Instructor permission.
Practical hands-on introduction to Data Science and Data Analytics tools and acquiring, storing, manipulating, and exploring data - both big and small. Examples from bioinformatics (e.g., genomics), health care informatics, urban and regional planning, astronomy and data journalism. Extensive writing of formal reports.
Prerequisites: MAT 126, MAT 311, CIS 512, or Instructor permission.
Applied introduction to building predictive, machine-learning models for real-world problems; learning Python computing environment, basic data analysis, management; data visualization and reporting using machine learning methods, including k-nearest neighbor, linear models, naïve Bayesian models, decision trees, random forests, and neural networks. Sample data sets from across industry professions.
Prerequisite: Graduate status.
Introduction to a “big picture” understanding of data flow for strategic, data-driven decision making, including data storage, data organization, data gathering and preparation, exploratory data analysis, and meaningful visualizations and communication. Includes hands-on practice.
Prerequisites: Instructor permission.
Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security.
Prerequisites: Undergraduate courses in MAT 126, MAT 311 and CIS 151 or instructor permission.
Introduction to key concepts and applications of time series analysis for bank risk management data-driven decision-making. Analysis, decomposition, segmentation, model selection and estimation, statistical and hypothesis testing, and forecasting and sensitivity testing. Use of actual datasets for applied analysis; revenue forecasting future scenarios; interactive classroom instruction in SAS programming environment. Offered occasionally.
Prerequisites: Instructor permission, programming experience required.
Introduction to Python programming focusing on the development of Python scripts and custom tools for processing and analysis of geospatial data. Automating geoprocessing workflows, creating custom analysis tool, and customizing user interfaces.
Prerequisites: GEG 325 or equivalent introductory GIS course
Introduction to interactive and Web-based mapping. Different approaches to communicating with maps on the Internet; how to create Web-based mapping applications.
Prerequisite: Instructor permission.
Exploration of publicly available data and user-generated data; application of communication theory and research in data analytics; attention to tools and methods in communication data analytics; focus on ethical responsibilities of data scientists; intensive practice in using statistical software and Python.
Prerequisite: Instructor permission.
This course will cover the fundamentals of effective data-driven storytelling. Students will learn how to analyze data, detect stories within datasets and communicate findings in oral, written, and interactive visual delivery modes for various audiences.
Prerequisite: 1 year undergraduate biology and 1 year undergraduate chemistry.
Introduction to bioinformatics concepts and techniques. Bioinformatics applications in academic, biotechnological, clinical and pharmaceutical settings for analyzing individual DSA and protein sequences. No formal computer programming training or high-level mathematical skills are required.
Prerequisite: One course in statistics.
Experimental design and statistical analysis of biological data; applications of computers to biological investigations. Designed for students in the initial stages of planning their research.
DSA 621 Data Science Tools in Energy Engineering
Prerequisites: Instructor permission
Tools and techniques needed to collect, clean, analyze and present data specific to the field of Energy Engineering in large datasets; statistical models to describe data; visualization of data; spreadsheets; databases; data analysis software.
ENT 622 Machine learning for Material Science in Clean Energy
Prerequisite: ENT 621, or instructor permission
Cover broad guidelines and best practices regarding obtaining and treatment of data in materials science and device physics related directly to Clean Energy. Feature engineering, model training, validation, evaluation and comparison. Include interactive Jupyter notebooks with example Python code to demonstrate important concepts, workflows, and best practices in the field.
ENT 581 Renewable Distributed Generation and Storage
Prerequisites: ENT 331 Electric Circuits or equivalent, and ENT 671 Power Systems Analysis I or equivalent, or instructor’s permission.
This course introduces renewable and efficient electric power systems. It encourages self-teaching by providing numerous practical examples requiring quantitative analysis. Topics include historical, regulatory, and utility industry perspectives of the electric system as well as most of the electricity, thermodynamics, and engineering economics background needed to understand new power technologies.
ENT 582 Smart Grid from Systems Perspective
Prerequisites: ENT 331 Electric Circuits or equivalent, and ENT 671 Power Systems Analysis or equivalent, or instructor’s permission.
A comprehensive understanding of smart grid is needed for stakeholders to enable them to develop systems prospective of Smart Grid and its technologies, increase modeling of Smart Grid from multiple perspectives, to increase economic understanding and decision making around current and future technologies, to integrate the role of policy and politics in the advancement of Smart Grid over time, to understand how to educate others in Smart Grid, and to analyze basic subsystems of the Smart Grid.
ENT 591 Operations and Management of Modern Grid
Prerequisites: Instructor’s permission.
This course introduces and explains operations of electric utilities including generation, transmission, distribution, and consumption of electric power, defines system operations and their drivers; discusses impact of deregulation and impact of smart grid technologies on systems operations; introduces concepts of business of system operations, and discusses various management systems used by modern utilities.
Prerequisite: Instructor permission.
Introduction to data-oriented programming and algorithmic problem solving using Python. Hands-on, project-oriented class incorporating problems from many fields.
Prerequisites: MAT 241 or instructor permission.
Introduction to tools and techniques needed to collect, clean, analyze and present data that can be used in any academic discipline. Data scraping from the internet. Visualization of data using appropriate software, spreadsheets, databases, Python.
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