Concentration Selection/Self-Evaluation Form for
The Master of Science Degree in Data Analytics Engineering
Please fill out this self-evaluation form to help us determine your best plan of study. Please make the best match you can between graduate and undergraduate courses you have already taken, and the equivalent GMU courses. When you are admitted to the MS in Data Analytics Engineering degree program, you will be advised as to what foundation courses, if any, you may be required to take.
NAME DATE
PREVIOUS DEGREE(S)
G#
Concentrations:
Select ONLY One Concentration
Applied Analytics Bioengineering Data Mining Digital Forensics Predictive Analytics Statistics for Analytics Individual Plan of Study
• If you have selected the Applied Analytics concentration then no further information is required.
• If you have selected “Individual Plan of Study” then specify a tentative plan of study using the table at the end of this form.
• For all other concentrations, fill in the corresponding table to determine if you have satisfied the necessary prerequisites.
Applied Analytics - For those selecting this concentration indicate in the table below how these
prerequisites are met by matching these GMU equivalents to courses from your previous educational background.
Volgenau School of Engineering
Office of Graduate Admissions & Enrollment Services MS 3D5, 4400 University Drive, Fairfax, Virginia 22030
Foundation Course Equivalent
GMU Course Number, Grade, Course Taken: Institution Check if course not taken Course Summary Calculus w/ Business Applications Math 108 Course #: Grade: Institution:
Statistics I STAT 250 Course #:
Grade: Institution: Computer Programming IT 106 Course #: Grade: Institution:
Data Mining – For those selecting this concentration indicate in the table below how these prerequisites are met by matching these GMU equivalents to courses from your previous educational background.
Foundation Course Equivalent
GMU Course Number, Grade, Course Taken: Institution Check if course not taken Course Summary
Data Structures CS 310 Course #: Grade: Institution: Formal Methods &
Models CS 330 Course #: Grade: Institution: Systems Architecture CS 367 Course #: Grade: Institution: Assembly Programming CS 465 Course #: Grade: Institution:
Calculus II Math 114 Course #:
Grade: Institution: Discrete Mathematics Math 125 Course #: Grade: Institution:
Digital Forensics - For those selecting this concentration indicate in the table below how these prerequisites are me by matching these GMU equivalents to courses from your previous educational background.
Foundation Course Equivalent
GMU Course Number, Grade, Course Taken: Institution Check if course not taken Course Summary Computer Operating Systems IT 342 Course #: Grade: Institution: Computer Programming CS 112 Course #: Grade: Institution: TCP/IP IT 341 or TCOM 535 Course #: Grade: Institution: Routing IT 445 or TCOM 515 Course #: Grade: Institution:
Predictive Analytics - For those selecting this concentration indicate in the table below how these prerequisites are met by matching these GMU equivalents to courses from your previous educational background.
Foundation Course Equivalent
GMU Course Number, Grade, Course Taken: Institution Check if course not taken Course Summary
Calculus I Math 113 Course #:
Grade: Institution: Applied Probability/Statistics STAT 344 Course #: Grade: Institution: Programming Language CS 222 Course #: Grade: Institution:
Statistics for Analytics For those selecting this concentration indicate in the table below how these prerequisites are met by matching these GMU equivalents to courses from your previous educational background.
Foundation Course Equivalent
GMU Course Number, Grade, Course Taken: Institution Check if course not taken Course Summary
Calculus I Math 113 Course #:
Grade: Institution:
Calculus II Math 114 Course #:
Grade: Institution:
Calculus III Math 213 Course #:
Grade: Institution:
Probability Math 351 Course #:
Grade: Institution: Matrix Algebra Math 111 Course #:
Grade: Institution:
Bioengineering for Analytics For those selecting this concentration indicate in the table below how these prerequisites are met by matching these GMU equivalents to courses from your previous educational background.
Foundation Course Equivalent
GMU Course Number, Grade, Course Taken: Institution Check if course not taken Course Summary
Calculus I Math 113 Course #:
Grade: Institution:
Calculus II Math 114 Course #:
Grade: Institution:
Calculus III Math 213 Course #:
Grade: Institution:
Elementary Differential Equations Math 214 Course #: Grade: Institution: Probability for Engineers STAT 346 Course #: Grade: Institution: Signals and Systems BENG 320 Course #:
Grade: Institution:
Individual Plan of Study
For students that wish to craft their own plan of study, choose five possible (non-core) graduate level elective courses (500 level or higher) related to your MS in Data Analytics Engineering interests. Interdisciplinary courses can be selected with the following prefixes: Applied
Information Technology (AIT); Computer Science (CS, INFS, ISA, and SWE); Computer Forensics (CFRS); Statistical Science (STAT); Systems Engineering and Operations Research (SYST, OR); Bioengineering (BENG, ECE) . Please do not select courses entitled Directed Readings or Special Topics.
Electives
Course Number Course Name
1. 2. 3. 4. 5.
