Master Of Science In Data Science And Analytics - Computational Data Science Concentration

Bradley's 34-hour data science and analytics graduate program teaches you the skills to analyze and process large and complex data. With our computational data science concentration, you'll learn the theory and algorithms needed to collect and understand data properties, build models, identify trends, solve problems and potentially find new insights.

Preparing You For Success

The growing use of electronic media has propelled data mining and knowledge discovery (DM/KD) techniques to the forefront of emerging technologies, increasing the need for experts to analyze and understand the massive amounts of data generated daily.

Bradley's computational data science courses take you through the discovery process and life cycle of a data mining project. You’ll grasp various machine learning algorithms and how to deploy them to analyze data, build forecasting and classification models, or perform unsupervised learning tasks.

By the time you graduate, your experiences may include:

  • Working on real-world, industrial problems assigned as projects in your classes
  • Learning statistical methods for testing and evaluation of models
  • The ability to create helpful and accurate data visualizations
  • Mastering programming languages, such as Python and R, to perform tasks
  • A semester-long capstone research project
  • Writing an optional master's thesis
  • Contributing to research publications

Making Your Mark

Bradley's graduate computational data science program prepares you to develop and apply tools that support the ever-changing data needs of today and tomorrow. You'll graduate with the essential background, knowledge and skills necessary to work as a data scientist or pursue a Ph.D. With applications in virtually every area of engineering, science, medicine, business and education, the techniques you learn are critical to economic management, wealth creation in commerce, and overall improvement of our lives and well-being.

General Admission Requirements

Each of the three concentrations has their own additional requirements. General requirements include:

  1. Completion of at least one semester of statistics
  2. Submission of GRE General Test or GMAT scores taken within the last five years. The applicant may request a GRE or GMAT waiver under certain circumstances.

Core Course Requirements

Common requirements for all three concentrations are as follows (each concentration has additional requirements):

  • At least 34 hours of graduate-level coursework, 16 of which (six courses) are in the common core. At least 24 hours must be labeled CS, CIS, IME, or MIS.
  • Pass a comprehensive written examination based on the core courses.
  • The courses required to satisfy the core requirement are:
    • IME 511: Probability & Statistics for Analytics - 3 hrs.
    • IME 512: Regression and Experimental Design - 3 hrs.
    • CS 560: Fundamentals of Data Science - 3 hrs.
    • CS 571: Database Management Systems OR IME 514: Operations Research - 3 hrs.
    • MIS 570: Introduction to Business Analytics - 3 hrs.
    • BUS 511: Communicating Quantitative Information - 1 hr.

Computational Data Science Concentration

For students with the programming skills for admission, the computational data science concentration teaches you computational skills and tools for data science, machine learning algorithms and the full lifecycle of a data science project, form data cleansing and attribute selection or transformation algorithms, machine learning algorithms and model building, post processing, evaluation, stacking, boosting and more. You will also learn and use popular programming languages for data science projects such as Python and R and their data science libraries, various state-of-the-art data science toolboxes like TensorFlow for deep neural networks and Hadoop for distributed databases, and more. You’ll then apply these skills through your capstone program. You also have the option to write a thesis under the supervision of faculty who are experts in the data science field.

Prerequisites For Admission

The admission prerequisites for the Computational Data Science concentration are:

  • Two semesters of programming classes in any programming language, or CS 541, or CS 502.
  • Two semesters of calculus.
  • Linear Algebra.

Prospective students who do not meet these conditions may still be admitted conditionally. For example, a student lacking two semesters of programming can take CS 502 or CS 541 to make up for it, but the class won’t count as part of the required 34 hours of coursework. Conditional status must be removed prior to graduation.

Course Requirements (12-15 hrs.)

No course taken to satisfy the common core requirement may also count towards the concentration requirement. The Computational Data Science concentration requirement is 12 credit hours, or 15 credit hours if a student chooses to write a thesis.

The remaining credit hours are made up of approved elective courses.

Required courses (4 courses):

  • CS 562: Machine Learning - 3 hrs.
  • CS 563: Knowledge Discovery and Data Mining - 3 hrs.
  • CS 572: Distributed Databases and Big Data - 3 hrs.
  • CS 594: Capstone Project for Data Science - 3 hrs.OR CS 699: Thesis - 6 hrs. (Note: 3 hrs. taken for two consecutive semesters)

Master's Thesis Option

Students pursuing the Computational Data Science concentration have the option to write a master’s thesis. If you select this option, you must choose an advisor and topic as early as possible in order to plan the thesis development and any needed supporting coursework. The following policies apply to theses:

  • A minimum grade point average of 3.5 in computer science and computer information systems graduate courses is required for students enrolling in a thesis course, i.e., CS 699.
  • No student may register for a thesis until 9 hours of graduate courses have been completed in the program.
  • Six credit hours of a thesis course are required and, upon completion, the thesis must be defended in an oral examination.
  • No grade will be given for a thesis course until after the oral defense.
  • A written outline of the thesis project and a tentative schedule must be submitted to and approved by the graduate advisor in data science and the chair prior to the registration for a thesis course.


Beyond the requisite core and concentration courses, many electives are available to push you past your required 34 hours.

Possible electives for the Data Science and Analytics Program include courses required by other concentrations and more:

  • CIS 576: Data Management
  • CIS 580: Digital Society and Computer Law
  • CS 541: Python for Data Science
  • CS 561: Artificial Intelligence
  • ECE 565: Engineering Applications of Machine Learning
  • IME 501: Engineering Cost Analysis
  • IME 526: Reliability Engineering
  • IME 561: Simulation of Manufacturing & Service Systems
  • IME 578: Engineering Analytics II
  • IME 583: Production Planning and Control
  • MIS 613: Advanced Algorithms for Business
  • MTG 624: Marketing Decision Making
  • MTH 510: Numerical Methods I
  • MTH 511: Numerical Methods II