Master of Science (M.S.) in Applied Data Science
Master of Science (M.S.) in Applied Data Science
Statistics is a field that combines rigor, creativity, and practical application. I hope students develop not only a solid foundation in data science and statistics in my classes, but also the ability to think critically, analyze data effectively, and communicate their findings to diverse audiences.
Senior Lecturer
Overview
In today’s data-driven world, the ability to harness the power of data for informed decision-making has become a critical skill. As organizations across industries increasingly rely on data to drive innovation and stay competitive, the demand for professionals with expertise in Data Science has surged. The master’s degree in applied data science is a comprehensive and advanced program designed to equip students, especially working professionals, with the knowledge, skills, and tools necessary to excel in this dynamic and rapidly evolving field.
The Online master’s in applied data science is an interdisciplinary program offered by UGA’s Franklin College Department of Statistics. The program requires completion of 30 credit hours. It is designed to provide students with the skills and knowledge needed to excel in today’s data-driven world. The program begins with an introduction to Python programming and a wide variety of data science techniques, alongside foundational statistical methods for data science using R programming. Students gain hands-on experience with Python and R, learning how to manage, analyze, and visualize data effectively. As students progress, they delve into clustering and classification algorithms, while simultaneously advancing their knowledge of Python and data structures, establishing a strong foundation in computing. The program then provides a solid grounding in statistical modeling for data science, followed by advanced topics such as data management and SQL. Students will also master advanced R programming for data science, advanced machine learning and deep learning, advanced statistical modeling, and natural language processing.
The curriculum equips professionals with applied data science skills through a comprehensive blend of statistics, computer science, data science, linguistics, and management information systems. Emphasizing real-world applications, it prepares graduates to tackle complex challenges in data science across industries by combining theoretical foundations with practical problem-solving. The program’s flexible online delivery enables career advancement while meeting the demands of a data-driven economy. Graduates will master software development, database design and management, distributed data processing, statistical analysis, data mining, machine learning, data visualization, and provide decision-making support.
Accreditations
The University of Georgia is accredited by the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC) to award baccalaureate, master’s, specialist, and doctoral degrees. The University of Georgia also may offer credentials such as certificates and diplomas at approved degree levels. Questions about the accreditation of the University of Georgia may be directed in writing to the Southern Association of Colleges and Schools Commission on Colleges at 1866 Southern Lane, Decatur, GA 30033-4097, by calling (404) 679-4500, or by using information available on SACSCOC’s website (www.sacscoc.org).
Admissions
Admissions Requirements
Undergraduate degree: A bachelor’s degree from a regionally accredited institution in the United States or a comparable degree from a foreign academic institution. While a bachelor’s degree in a quantitative field (e.g., mathematics, statistics, computer science, engineering, economics) can be a plus, applicants from other disciplines who demonstrate quantitative skills are strongly encouraged to apply to the program.
GPA Requirement: A minimum GPA of 3.0 on a 4.0 scale from undergraduate studies.
Flexibility can be provided if the applicant has strong professional experience or other compensatory qualifications.
Quantitative Coursework: Completion of at least one introductory course in probability and statistics and some working knowledge of calculus and linear algebra is preferred.
Programming skills: Basic proficiency in a programming language is preferred. This can be demonstrated through coursework, professional experience, or online courses/certifications.
Professional Experience: Since the program is designed for working professionals, relevant work experience in a related field is preferred. This shows the ability to apply theoretical knowledge in practical settings.
Online Master of Science (M.S.) in Applied Data Science Application Checklist
- Application – Submit the Graduate School Admissions online. Application fee: $75 Domestic/$75 International.
- Select Campus – Online
- Select Degree Level – Masters
- Select Intended Program – MS, Data Science (Statistics) [MS_DSCI_ONL]
- Select Intended Term – Fall
- Résumé or curriculum vita – Submit online to the Graduate School.
- Statement of Purpose – Submit a one-two page statement of purpose online to the Graduate School. The statement of intent should clarify the candidate’s relevant background, interests, and goals in relation to the program.
- Transcripts – Submit unofficial transcripts from all institutions attended as part of the online application. Send official transcripts after you are offered admission.
- Letters of Recommendation – Submit three letters of recommendation online to graduate school. Letters should be from individuals who can evaluate the applicant’s scholarly ability and potential for success in a graduate program
Application Deadlines
Domestic Applicants
- Fall – Priority Deadline: March 15 (priority consideration)
- Fall: July 1 (final deadline)
International Applicants
- Fall: March 15
English Proficiency: For non-native English speakers, proof of English proficiency (e.g., TOEFL, IELTS) may be required to ensure they can succeed in the coursework.
Test Scores (Optional)
You may choose to submit standardized test scores, most commonly GRE or GMAT. However, this is not required for admission into the program.
Cost
The complete cost of attendance can be found at https://osfa.uga.edu/costs/.
Please use the Estimated Cost Calculator on the Bursar’s Office website to calculate one academic (Fall/Spring) year’s tuition.
Fees for those students enrolled in exclusively online programs are $411 per semester.
Potential additional costs include:
- Textbooks
- Exam proctoring fees
- Technology upgrades
The complete cost of attendance can be found at https://osfa.uga.edu/costs/.
Financial Aid
Visit the Office of Student Financial Aid for information about financial assistance.
Corporate Assistance
Consult your employer about the availability of tuition reimbursement or tuition assistance programs.
Military Assistance
Active-duty military, veterans, and military families should visit Veterans Educational Benefits to take full advantage of available financial assistance and educational benefits.
University System of Georgia Tuition Assistance Program (TAP)
The purpose of TAP is to foster the professional growth and development of eligible employees. For more information, see Tuition Assistance (refer to the Distance Learning section).
Curriculum
The Area of Emphasis in Applied Data Science, which will be offered online, requires completion of 30 credit hours.
Core Courses (15 hours):
STAT 6381E, Introduction to Python and Data Science (3 hours)
STAT 6383E, Advanced Python and Data Structures (3 hours)
STAT 6382E, Statistics for Data Science with R programming (3 hours)
STAT 6384E, Statistical Modeling in Data Science (3 hours)
STAT 6385E, Statistical Foundations of Clustering and Classification Methods (3 hours)
Advanced Courses (15 hours):
LING 6750E, Natural Language Processing (3 hours)
MSIT 7510E, Data Management and SQL (3 hours) – NEW
STAT 6386E, Advanced R Programming for Data Science (3 hours)
STAT 6387E, Advanced Machine Learning in Data Science (3 hours)
STAT 6388E, Advanced Statistical Modeling for Data Science (3 hours)
Sample Program of Study
| Year | Summer | Fall | Spring |
|---|---|---|---|
| Year 1 | STAT 6381E: Introduction to Python and Data Science (3 hours) STAT 6382E: Statistics for Data Science with R programming (3 hours) | STAT 6383E: Advanced Python and Data Structures (3 hours) MIST 7510E: Database Management and SQL | |
| Year 2 | STAT 6384E: Statistical Modeling in Data Science (3 hours) STAT 6385E: Statistical Foundations of Clustering and Classification Methods (3 hours) | STAT 6386E: Advanced Programming for Data Science (3 hours) STAT 6387E: Advanced Statistical Machine Learning in Data Science (3 hours) | STAT 6388E: Advanced Statistical Modeling for Data Science (3 hours) LING 6750E: Natural Language Processing (3 hours) |
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