Data Sciences Certificate - CISER Consortium for Intelligent Systems Education and Research
Data Science Certificate
Starts in January and July each year
The DSAG coordinates and focuses the unique talent, infrastructure, relationships, and geography of NPS in order to provide:
- an educational platform
- research programs and
- advisory services
to organizations within the Department of Defense that seek to gain insights from data.
Advances in machine learning, artificial intelligence, and data analytics that have produced competitive advantage for commercial enterprises such as Amazon, Google, and Netflix are absolutely imperative if the Department of Defense (DoD) is to turn massive amounts of sensor data into actionable information for commanders.
A four-course Certificate in Data Science for Military Use starts in January and July each year. More information about the certificate can be found here.
View the Data Science Certificate Program PDF for more information.
CY3650 Cyber Data Management and Analytics
This course surveys the use of information technologies and data analytics, with emphasis on case studies relevant to cyber operations and to the DoD. Topics include technologies and trends for Big Data management (e.g., distributed cloud file systems, NoSQL data stores); major themes and technologies in cloud computing (SaaS, PaaS, IaaS), distributed computation frameworks (MapReduce); and case studies focusing on how cloud infrastructure is used to enable services and analytics (e.g., mining, matching filtering and translating data). LEARN MORE
OA4106 Advanced Data Analysis
The course features the blending of sophisticated statistical software and data from recent DoD applications. The manipulation of multivariate data and statistical graphics are emphasized. Methodologies presented can include survival analysis, classification and discrimination, categorical data analysis, and sample survey methods. LEARN MORE
CS4315 Introduction to Machine Learning and Data Mining
A survey of methods by which software and hardware can improve their performance over time. Topics include data manipulation, concept learning, association rules, decision trees, Bayesian models, simple linear models, case-based reasoning, genetic algorithms, and finite-state sequence learning. Students will do projects with software tools. LEARN MORE
OS4118 Statistical and Machine Learning
This course introduces students to the art and science of statistical and machine learning to find patterns in large and "Big" data. The focus is on the strengths and weaknesses of learning techniques and their implementation. Fundamental ideas common to learning methods are covered, and supervised/unsupervised techniques are introduced. These techniques include re-sampling methods, advanced clustering and visualization, tree-based ensembles, stochastic gradient boosting, deep neural networks, auto-encoding, and other dimension reduction techniques, and applications to natural language processing. The software package R and high-performance parallel or distributed computing will be used to demonstrate these techniques. LEARN MORE