Graduate Certificate in Data Analytics

Description: The purpose of this certificate is to prepare students for technical careers in big data analytics and data science. Students will acquire in-depth technical skills that will enable them to understand the underlying technical fundamentals of data analytics, to develop new analytical methods, and to engineer new analytical tools. Students will acquire skills that integrate computational, statistical, and engineering techniques that form the heart of big data analytics. The certificate will provide students with formal recognition of their skills to better support their career prospects.

There is a growing need for technically trained engineers and scientists to lead the rapidly evolving field of big data analytics. The U.S. presidential administration has identified big data analytics as a core area of national need. Data science is one of the fastest growing career paths, and demand for technical expertise is out-pacing supply. Technical expertise is needed to develop new methods, tools, and infrastructures required to support novel big data analytics operations in industry, government, and academia. The technical expertise required involves a combination of computation, statistics, and engineering, such that training in any one of these individual disciplines alone does not suffice. This certificate will serve to train technical students with a broader view across these disciplines to support the data analytics field.

The learning outcomes of this certificate program are as follows:

(1) Students will have technical depth in the fundamentals of data analytics, in terms of understanding the underlying principles and implementations of analytical methods.

(2) Students will have broad understanding of multi-disciplinary perspectives on technical methods in data analytics, including computational, statistical, and engineering perspectives.

Target Audience and Time to Complete:  The target audience of this certificate is technically oriented students in engineering and science. In particular, the certificate is ideally suited to complement the technical training of students enrolled in Virginia Tech’s graduate programs in Computer Science, Statistics, and Electrical and Computer Engineering. Since the certificate requirements fit well with these existing degree program requirements, it is expected that the time to completion of the certificate will not substantially increase their time to completion for their degree program. Per university requirements, all 12 of the required credits for the certificate can be double counted toward a student’s degree program.  Students will need to take at least two additional courses beyond their existing degree requirements. However, students in other graduate programs at VT are not precluded. The estimated time to completion for students in other degree programs and for non-degree seeking participants is one year. 

Graduate Certificate in Data Analytics

Admission:
Admission to the Graduate School and completing a Graduate Certificate Application are required for both degree- and non-degree seeking students.

Degree-seeking applicants:
The Graduate School requires completion of a bachelor’s degree from an accredited institution with a GPA of 3.0 or better for admission to Certificate Status. Applicants with an undergraduate GPA < 3.0 may qualify for Commonwealth Campus admission. Students pursuing a degree and a certificate simultaneously are classified within their degree program. Certificate credits may be used to meet degree requirements if they are appropriate for inclusion on the degree Plan of Study.

Non-degree seeking applicants:
A qualified person who wishes to enter Virginia Tech to obtain a graduate certificate, without being enrolled in a degree program, may apply for graduate admission to Graduate Certificate status. Such applicants submit an Application for Admission and a Graduate Certificate Application https://graduateschool.vt.edu/content/dam/graduateschool_vt_edu/forms/Application_for_Graduate_Certificate_Program.pdf, and must meet the following criteria:

• GPA of 3.0 for admission for the last half of the credits earned for the undergraduate (bachelors) degree*

• official transcripts must be submitted.

• academic background meets the requirements of the admitting academic unit.

• International applicants must submit scores from the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS). A minimum TOEFL score of 550 paper-based (PBT) or 80 internet-based test (iBT) is required for consideration of the application. On the iBT, subscores of at least 20 on each subtest (Listening, Speaking, Reading, and Writing) are required for admission. A minimum IELTS score of 6.5 is required for admission. Some departments have higher TOEFL or IELTS score requirements than those set by the Graduate School. 

Graduate Certificate in Data Analytics

Curriculum Requirements and Descriptions
Number of Credit Hours:Students should complete at least 2 courses from the core list (see below) and 2 courses from the elective list, for a total of 12 credits. For all students, courses taken must span all three departments; Computer Science, Statistics and Electrical and Computer Engineering. All courses must be graded A-F, and students must attain a minimum 3.0 GPA in the designated courses. Transfer credits are not permitted.

Core Courses: (Choose 2)

CS/STAT 5525 Data Analytics I

CS/STAT 5526 Data Analytics II

CS 5824/ECE 5424G: Advanced Machine Learning

Restricted Elective Courses: (Choose 2)

CS 5234 Advanced Parallel Computation

CS 5604 Information Storage and Retrieval

CS 5614 Database Management Systems

CS 5764 Information Visualization

CS 5804 Introduction to Artificial Intelligence

CS 5834 Introduction to Urban Computing

CS/ECE 6524 Deep Learning

CS 6604 Advanced Topics in Data and Information

STAT 5114 Statistical Inference

STAT 5314 Monte Carlo Methods in Statistics

STAT 5414 Time Series Analysis I

STAT 5444 Bayesian Statistics

STAT 5444G Advanced Applied Bayesian Statistics

STAT 5504 Multivariate Statistical Methods

STAT 5544 Spatial Statistics

ECE 5524 Pattern Recognition

ECE 5554 Computer Vision

ECE 5605 Signal Detection and Estimation

ECE 5734 Convex Optimization

ECE/CS 6524 Deep Learning 

ECE 6554 Advanced Computer Vision