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Transform Your Future with Data Science and Analytics

What does it cost?

Check out our cost calculator or visit student financial services for information on estimated costs.

How long will it take?

This is designed as a three-semester program. A minimum of 34 semester hours of graduate coursework must be completed.

Where will I take classes?
Atlanta Campus

Application Deadlines

Fall: June 1
Spring: October 30
Summer: Does Not Admit

View Admissions Requirements

College of Arts & Sciences

Data Science and Analytics MSA, concentration in Big Data and Machine Learning

Computer Science

[email protected]
404-413-5714

The Master of Science Analytics (M.S.A.) in Data Science and Analytics with a Concentration in Big Data and Machine Learning at Georgia State University blends advanced coursework in data science and machine learning with practical applications designed to meet the demands of today's data-driven world.

The field of data science lies at the intersection of computer science, statistical methodologies, and a wide range of application domains, focusing on the generation, processing, and analysis of vast amounts of data. This program is specifically designed to train professionals in the rapidly growing field of data science, equipping them with the skills needed to transform big data into actionable insights.

The field of data science and analytics, particularly in the context of big data and machine learning, is profoundly influenced by technological advancements and the increasing importance of data-driven decision-making. The MSA Data Science and Analytics program at Georgia State University focuses on these critical aspects, including:

The role of big data in contemporary analytics.
The impact of machine learning and data science on various industries.
Strategies for effective data management and analysis.
Understanding the ethical implications and responsibilities in data science.

The MSA in Data Science and Analytics prepares you to:

Lead and manage data-driven projects in diverse sectors.
Apply critical thinking to solve complex problems and make informed decisions.
Adhere to ethical standards in data handling and analysis.
Utilize advanced tools and techniques for data analysis and interpretation.
Communicate effectively with a diverse team and stakeholders in data-centric environments.

Master the Skill of Data Intelligence

Your Gateway to an Innovative
and Impactful Career

This master’s degree is your gateway to an innovative and impactful career in a variety of industries. This program helps you learn how to understand complex data, find useful insights, and make decisions using data in different industries.

Discover more about the opportunities that Georgia State University's program offers in the realms of technology, business, healthcare, and beyond. If you're ready to take the next step in your professional journey, use the START YOUR APPLICATION button on the right for comprehensive application guidance or to request more details.

Learning Objectives

The MSA program at Georgia State University aims to prepare graduates for careers in data-driven fields by focusing on these key learning objectives:

Advanced Data Science Understanding: Learn about cutting-edge data science topics like big data technologies, machine learning, and analytical methods.
Leadership in Data-Driven Decision Making: Develop skills to lead data-focused projects and teams effectively, promoting innovative solutions across various industries.
Ethical Data Management: Understand and apply ethical standards in handling data, emphasizing integrity and responsibility in the data science profession.

By achieving these objectives, graduates of the MSA program are equipped to excel in their careers and contribute positively to the advancement of data science and analytics.

Careers in Data Science and Analytics

The demand for skilled data professionals continues to grow, offering graduates numerous, diverse, and rewarding career opportunities. According to the U.S. Bureau of Labor Statistics, data scientists have a median annual wage of $103,500 as of May 2022. The field is experiencing rapid growth, with employment projected to grow by 35 percent from 2022 to 2032, much faster than the average for all occupations. Approximately 17,700 job openings for data scientists are projected each year over the decade. This growth is indicative of the increasing importance of data analysis and machine learning in various sectors.

Graduates find employment at the following:

Technology firms and startups
Healthcare and pharmaceutical companies
Government agencies and research institutions
Payment processing sector
Consulting and analytics service providers
E-commerce and online retail

Program Details

A complete application to the MSA in Data Science and Analytics program includes:

Submitted Online Application - Online applications must be submitted by the deadline. Paper applications are not available.
Application fee - A non-refundable fee of $50 is required for each application. This fee must be paid online by credit card.
Statement of Purpose - Provide a statement of your educational background, experience, and preparation relevant to a graduate program in computer science, and specify your research and career goals.
English Language test scores (International applicants only) - International applicants are required to submit official TOEFL (Test of English as a Foreign Language) or IELTS (International English Language Testing Systems) academic scores.
Three letters of recommendation - Letters should be submitted through the online application.
Upload Transcripts from each post-secondary college/university attended: Upload each transcript to the Academic History section of the online application before submitting your application or upload the transcript to your application status page under the Upload Materials section if your application has been submitted. Review the requirements for submitting transcripts before sending your submission. Failure to follow these requirements will delay your application processing.

For more information on submitting your transcripts, visit the graduate transcript requirements page.

The Master of Science in Analytics (MSA) program at Georgia State University, with a concentration in Data Science and Analytics, offers a robust curriculum designed to equip students with the necessary skills and knowledge in this rapidly evolving field. The program encompasses a total of 30 credit hours, which includes both core courses and a capstone project.

Key components of the curriculum include:

Data Analysis and Visualization
Advanced Statistical Methods
Machine Learning and Predictive Analytics
Big Data Technologies
Ethical and Legal Aspects of Data Science
Capstone Project

This curriculum is designed to provide a comprehensive education in data science and analytics, preparing graduates for successful careers in various industries where big data plays a pivotal role.

For a complete list of courses and up-to-date curriculum requirements, please visit the course catalog.

At Georgia State University, we understand that students come to our institution from a range of financial backgrounds. We value the investment our students make in their education, and we aim to provide access to scholarships and aid opportunities that can support students through their academic journey.

Georgia State University offers various scholarships and aid opportunities for students in the data science and analytics field.

Learn more about tuition rates or use Georgia State’s Cost Calculator to determine the investment required for your course of study.

The MSA in Data Science and Analytics program equips students with strong preparation in statistical analysis, machine learning, information retrieval, and the management and analysis of massive data sets. Courses focus on reproducible data analysis, collaborative problem-solving, data and knowledge visualization and communication, as well as privacy and ethical issues that arise in data science.

Over the summer, students will either participate in data science internships, work under faculty supervision on their own big data-driven machine learning projects or participate in data science sprints through interdisciplinary collaborations.