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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 3-semester program. A minimum of 34 semester hours of graduate coursework must be completed.

Where will I take classes?

Application Deadlines View Admissions Requirements

  • Fall: June 1
  • Spring: Does Not Admit
  • Summer: Does Not Admit

Data Science and Analytics, M.S. — Concentration in Big Data and Machine Learning

Computer Science

BDMLAdmissions@cs.gsu.edu
404-413-5714

The Big Data and Machine Learning (BDML) concentration of the Master of Science in Data Science and Analytics is a three-semester program designed to train professionals in the rapidly growing field of data science. Data science lies at the intersection of computer science, statistical methodologies, and a wide range of application domains. It focuses on the generation, processing, and analyzing of vast amounts of data.

The BDML concentration responds to the growing need for highly qualified professionals who can transform big data into practical and valuable insights. In general terms, data scientists create actionable and valuable insights from data. Students in this program will gain the technical and soft skills that industry increasingly expects from data scientists. The concentration targets employment for students in industries related to the Internet of Things (IoT), robotics, health and remote‐sensing data, as well as top research and development (R&D) labs.

The BDML concentration offers strong preparation in statistical analysis, machine learning, information retrieval, and the management and analysis of massive data sets. The courses in the concentration focus on topics such as reproducible data analysis, collaborative problem solving, data and knowledge visualization and communication, as well as privacy and ethical issues that arise in data science.

VIEW CATALOG INFORMATION

Program Highlights

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

The sprints will be offered through interdisciplinary collaborations between the Computer Science Department and other departments in our university, or with industry. Examples include:

  • Spatiotemporal data mining for students interested in the growing GIS industry, autonomous vehicles, traffic monitoring, and  multiple sensors/IoT devices (e.g., water quality and leakage sensor deployment and analytics of real-time data)
  • Monochromatic computer vision for students interested in robotics, astroinformatics, solar physics, space weather and medical image analysis
  • Sequential data analysis for students wanting to specialize in mining time-series and bioinformatics
  • Polychromatic object recognition for students interested in automated identification of patterns in digital photography and video data (for example, from security cameras)
  • Graph data mining for students interested in social sciences and functional magnetic resonance imaging (FMRI) data analyses.

We believe choosing data formats as central points of our summer activities will help students broaden job opportunities.

Program Details

In addition to the general requirements of the College of Arts and Sciences, the MS degree in Data Science and Analytics with a concentration in Big Data and Machine Learning has the following requirements:

  • A strong record of coursework and/or experience in computer science, engineering, mathematics, statistics, natural and physical sciences, or a related discipline (minimum GPA 3.0)
  • Experience in programming, data structures and algorithms, linear algebra, and probability and statistics
  • Strong performance on the Graduate Record Examination (GRE)
  • Strong performance on the TOEFL or IELTS for international applicants whose native language is not English

Required Materials for Application

  • Complete Online Application for Graduate Studies
  • $50 processing fee
  • Official transcripts (one from each institution attended)
  • Official GRE scores (our institution code is 5251; there is no departmental code), required per College policy
  • Three letters of recommendation
  • International applicants must demonstrate proficiency in English by taking the TOEFL or IELTS tests
  • Statement of purpose (a brief personal essay explaining the applicant’s interest in the program, relevant skills and experiences, and career objectives)

Foundation Course Requirements      

We welcome students from all backgrounds. However, applicants are expected to have completed certain computer science foundation courses with a grade of B or higher in each. Please refer to the current Graduate Catalog for a list of foundation courses. If you did not take all of these courses but the admission committee thinks you are talented, you will be asked to take the missing foundation courses during the first semester of enrollment at Georgia State, before starting your M.S. or Ph.D. coursework. Typically, we recommend that non-CS majors complete the CSC 1000–3000 level courses with a grade of B or higher before applying to our graduate program. You may also consider Georgia State’s post-baccalaureate program.

We also require the GRE (and TOEFL/IELTS for international students). Please be aware that English proficiency is not the only evaluation metric.

How to Apply

To apply for this program, please submit a complete online application. In the “Program Selection” section of the application, select the “Interdisciplinary Studies” College and then select the “Data Science & Analytics” program. Additional application instructions, forms, and links to the online application are available at the College of Arts and Sciences Graduate Admissions website.

34-36 credits

Required Courses (12 hours)
CSC 6710 Database Systems (4)
CSC 6780 Fundamentals of Data Science (4)
CSC 8902 Ethics (1)
STAT 8670 Computational Methods in Statistics (3)

Other Required Courses (15 hours)
CSC 6740 Data Mining (4)
CSC 6760 Big Data Programming (4)
CSC 6850 Machine Learning (4)
One of:
STAT 8310 Bayesian Data Analysis (3) OR
STAT 8561 Linear Statistical Analysis I (3) OR
STAT 8700 Categorical Data Analysis (3)

Two 8000-level Elective Courses (6–8 hours)
CSC 8530 Parallel Algorithms (4)
CSC 8710 Deductive Databases and Logic Programming (4)
CSC 8711 Databases and the Web (4)
CSC 8712 Advanced Database Systems (4)
CSC 8713 Spatial and Scientific Databases (4)
CSC 8740 Advanced Data Mining (4)
CSC 8741 Graph Mining (4)
CSC 8810 Computational Intelligence (4)
CSC 8850 Advanced Machine Learning (4)
CSC 8851 Deep Learning (4)
CSC 8910 Data Dissemination in Online Social Networks (4)
STAT 8090 Applied Multivariate Statistics (3)
STAT 8561 Linear Statistical Analysis I (3)
STAT 8610 Time Series Analysis (3)
STAT 8674 Monte Carlo Methods (3)

BDML Capstone Project (1 hour)
CSC/STAT 8930 MS Project (1)

Tuition and fees for the M.S.A. degree program in Data Science and Analytics with a concentration in Big Data and Machine Learning will be charged at the standard graduate student rate for in-state and out-of-state residents. A complete breakdown of costs can be found here by clicking on the "Graduate" link for the appropriate academic year.

Funding

We consider all accepted students for financial aid at the time of admission; top students will be admitted with funding support. Once we receive your completed application, our graduate admission committee will review your application. Top candidates may receive funding (with a tuition waiver) such as fellowships, teaching assistantships, research assistantships and office assistantships in our department, as well as on-campus jobs at our university. More details will be provided once an admitted student shows up on campus.

Upon completion of the concentration, students will possess the following data science skills and abilities:

  • Understand the principles of Data Science (DS), enhance DS knowledge and be able to apply DS theory for practical tasks.
  • Collect, store, search, mine and visualize big data. Learn how to transform raw data into tangible value and evaluate data in terms of volume, variety, velocity, source, etc.
  • Identify the DS tasks of an organization and design corresponding solutions. Learn how to evaluate DS options and limitations which could possibly influence organizational needs.
  • Analyze and evaluate multiple DS models. Understand the strength and limitation of diverse DS models in terms of various DS tasks and be able to select appropriate ones to solve the given DS task(s).
  • Interpret outcomes from employed DS model(s). Transform the observations from data resources and outcomes from DS models into actionable business strategies, and persuade decision makers about practical benefits coming from these discoveries.
  • Have the leadership skills and knowledge to lead a team of DS professionals, and to provide long-term, high-quality DS services to their employers.

Description

Careers

The Big Data Machine Learning program will allow students to gain the technical skills that industry increasingly expects from Data Scientists. Big Data comes from the Internet of Things (IoT), robotics, autonomous vehicles, and other IT-related fields such as scientific labs working with medical or remote-sensing data, companies specializing in big data processing and analysis, cloud storage and computing services.

These companies aggressively seek graduate-level professionals who can:

  • Collect, clean, manage, analyze and interpret big data,
  • Derive new knowledge from big data,
  • Make sure these discoveries are transferred to the form of actionable items for upper administrators, and
  • Clearly communicate to stakeholders through sophisticated but human-friendly computer visualization techniques.

Several recent reports have highlighted the growing need for workers with these skills and the shortage of highly-qualified people to fill the projected increase in jobs. In the most recent study done by the Education Advisory Board, Data Analytics professionals are referred to as “Masters of the Universe” with 293% job posting growth during the 2013-2016 period and a median base salary of $120,000 reported for regular employees, and $254,000 for data science managers.

Contacts

Rafal Angryk
Founding Director, Big Data & Machine Learning MS Program
Department of Computer Science

Admissions

BDMLAdmissions@cs.gsu.edu

25 Park Pl NE #2500
Atlanta, GA 30303

The information shared provides an overview of Georgia State’s offerings. For details on admissions requirements, tuition, courses and more, refer to the university catalogs.