Data Science Certificate -- Curriculum 268 (DL) (CS/OR)

Program Officer

MAJ Robert “Rob” Froberg
Glasgow Hall, Room 230

[phone], DSN 756-2277
[e-mail]

Academic Associate

Michael “Mike” Atkinson
Glasgow Hall, Room 249

[phone], DSN 756-2398

[e-mail]

(Joint DL certificate program of the Department of Computer Science and Department of Operations Research.) This curriculum is described in the Department of Computer Science section of this Catalog. The Department of Operations Research supports this curriculum with courses and faculty.

Brief Overview

The Academic Certificate in Data Science provides education in distributed computing infrastructure and the application of statistical and machine learning techniques to appropriately manage and gain insights from data of all sizes and types. Data Science has emerged as an area critical to the mission of the Navy and the Department of Defense because of the central role it plays in intelligence, surveillance, and reconnaissance, talent management, cyber-security, and logistics functional areas. Upon successful completion of the course work, students will be awarded an Academic Certificate in keeping with standard practices of the Naval Postgraduate School.

Background in statistics and some experience with higher level programming language as evidenced by transcripts or work history is required for enrollment. 

Program Length

Four quarters

Outcomes

• Possess the mathematical and computer programming skills required to conduct data science projects and be able to use computers to aid in data analytics.
• The graduate will be well-versed in the fundamentals of statistics and data analysis for applications to machine learning and data mining problems.
• The graduate will be able to identify, evaluate, and apply the concepts associated with managing large data sets, including cloud computing and split-merge distributed processing.
• The graduate will be able to clean, process, and summarize structured and unstructured (text) data.
• The graduate will know when and how to apply common machine learning tools, both supervised and unsupervised, and understand their strengths and weaknesses.
• The graduate will be able to evaluate the results of machine learning algorithms, and propose ways to improve their performance and utility in specific applications.
• The graduate will understand the use of the supervised methods of regression and classification, know when to implement them, and be able to apply basic and advanced techniques in appropriate settings including for large data sets.
• The graduate will be aware of common unsupervised methods and be able to use them particularly for large data sets of high dimension.
• The graduate will gain practical experience working on all aspects of a data science study, including interacting with a variety of different open-source databases, applying real-world applications with the cloud and high-performance computing environments, and demonstrating the ability to conduct independent analytical studies.

Course Requirements

Students are to complete the following four courses to earn the Data Science Certificate:

Course Number	Title	Credits	Lecture Hours	Lab Hours
CS4315	Introduction to Machine Learning and Data Mining		3	1
OS4106	Advanced Data Analysis		3	0
OA3802	Computational Methods for Data Analytics		4	0
OS4118	Statistical and Machine Learning		3	0

CY3650: Students who have enrolled in the Data Science Certificate and completed CY3650 by 31 December 2022 can substitute CY3650 for OA3802.