Telecommunications, MST

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The Master of Science in Telecommunications (MST) program explores how information flows through networks to support human needs.

Networks, traditionally the focus of phone service and then the Internet, are now present in most aspects of our lives, from smart homes to cyber-physical systems.  On a daily basis, communication and computer networks are utilized to support business operations, educational pursuits, healthcare, transportation networks, and social networks (both physical and virtual) for human well-being.  The MST program is designed to produce Telecommunications professionals who will design, build, secure, and manage networks and infrastructure that accommodate innovative usages of the networks by people, organizations, and businesses.

Graduates will gain a strong foundation in the design of network protocols, wireless networks, network security, and performance enhancement for communication networks. Coursework in data mining, machine learning, and algorithms prepares students for careers that call for maximizing network utilization, supporting information flow in the Cloud, and tailoring networks to user needs and expectations. Graduates will have the skills and knowledge sought after by telecommunications equipment manufacturers, wireless providers, enterprise network users such as financial or pharmaceutical companies, and data center owners. Graduates also have chances to examine general network science and analysis to gain knowledge on emerging communication services from stand-alone streaming media to information flow on social networks.

Admissions Requirements

Applicants for graduate study must have earned a baccalaureate degree from an accredited college or university with a scholastic average of B (3.0 on a 4.0 scale) or better.

Prerequisites for admission to the MST degree program include one college course (3 credits or more) in each of the following (the corresponding Pitt course numbers are indicated):

• Programming: A course on object-oriented programming using Java, C#, or C++. (CMPINF 0401 )
• Probability and Statistics: A course covering data collection, descriptive and inferential statistics is optimal. It should cover measures of central tendency and variability, regression, correlation, non-parametric analysis, probability, and sampling, Bayesian analysis, significance tests, and hypothesis testing. (STAT 0200  or STAT 1000 )
• Mathematics: A college-level mathematics course, in linear algebra, calculus, or discrete mathematics (MATH 0120 , MATH 0220 , or MATH 0400 )