Masters In Data Science And Analytics-Computational Data Science

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 30-hour data science and analytics graduate program with concentration in Computational Data Science (DSA-CD) teaches you computational skills and tools for data science to perform the full lifecycle of a data science project. From data cleansing, preprocessing and attribute selection or transformation algorithms to machine learning algorithms and how to deploy them to analyze data, build forecasting and classification models or perform unsupervised learning tasks. Then through the post processing and model evaluation phases, including stacking, boosting and aggregate modeling.

You will 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.

By the time you graduate, your experiences may include:

  • Working on real-world, industrial problems assigned as projects in your classesor the semester-long capstone project.
  • 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 data mining tasks, Machine learning algorithms and toolboxes for model building such as TensorFlow or data warehousing including distributed databases like Hadoop.
  • Writing an optional master's thesis

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.

Program Admission Requirements

  • Completion of at least one semester of calculus.
  • Official GRE or GMAT score sent directly to the Office of Admission by the testing agency. Bradley’s institutional code for score reporting is 1070. Applicants may request a GRE or GMAT waiver under certain circumstances. Consult with university admissions

DSA-CD Graduate Program Requirements

At least 30 hours of graduate-level coursework, 12 of which (4 courses) are in the common core and 18 of which are in the computational data science requirements. Pass a comprehensive written examination.

Courses Required to Satisfy the Common Core - 12 hrs
  • IME 511: Probability & Statistics for Analytics - 3 hrs.
  • CS 541 Python for Data Science - 3 hrs OR CS 560: Fundamentals of Data Science - 3 hrs.
  • CS 571: Database Management Systems or IME 568: Engineering Analytics I - 3 hrs.
  • MIS 573: Data Visualization for Business Analytics - 3 hrs.
  • No course taken to satisfy the common core requirement may also count towards the concentration requirement.

Courses required to satisfy the Computational Data Science Concentration (DSA-CD) - 18 hrs

    Required courses (4 courses) 12 hrs:

    CS 594 Capstone Project for Data Science - 3 hrs. OR CS 699 Thesis – 6 hrs.

    Three courses, that you have not taken to fulfill the common core, out of the following four courses.

    CS 560 Fundamentals of Data Science - 3 hrs.

    CS 562 Machine Learning - 3 hrs.

    CS 563 Knowledge Discovery and Data Mining - 3 hrs.

    CS 572 Distributed Databases and Big Data - 3 hrs.

    Note: If you fulfilled the core by taking CS 541, then one of the 3 courses taken from the list above must be CS 560.

    Electives (2 courses - 6 hrs) which must be approved by the student’s graduate advisor.

Master's Thesis Option

Interested and qualified students pursuing the Computational Data Science concentration have the option to write a master’s thesis. Students selecting this option are encouraged to choose a thesis advisor and topic as early as possible to plan the thesis development and any needed supporting coursework.

The following policies apply to thesis:

  • A minimum grade point average of 3.5 in graduate courses taken at Bradley 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. The six hours must be in consecutive semester or terms (3+3).
  • No grade will be given for a thesis course until after the oral defense. The thesis oral defense substitutes the comprehensive exam that the non-thesis students have to take.
  • A written outline of the thesis project and a tentative schedule must be submitted to and approved by the graduate coordinator and the chair prior to the registration for a thesis course.


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 560: Fundamentals of Data Science
  • CS 561: Artificial Intelligence
  • CS 562: Machine Learning
  • CS 563: Knowledge Discovery and Data Mining
  • CS 571: Database Management Systems
  • CS 572: Distributed Databases and Big Data
  • CS 531: Web Development Technologies
  • CS 532: Advanced Java Computing
  • ECE 565: Engineering Applications of Machine Learning
  • ECO 519: Econometrics
  • IME 501: Engineering Cost Analysis
  • IME 512: Regression and Experimental Design
  • IME 514: Introduction to Operations Research
  • IME 526: Reliability Engineering
  • IME 561: Simulation of Manufacturing & Service Systems
  • IME 568: Engineering Analytics I
  • IME 578: Engineering Analytics II
  • IME 583: Production Planning and Control
  • MIS 613: Advanced Algorithms for Business
  • IB 502: Global Trade Management and Analysis
  • MTG 502: Logistics Tools and Techniques
  • MTG 506: Markeeting Analytics
  • MTG 507: Customer Analytics
  • MTG 624: Marketing Decision Making
  • MTG 640: Obtaining, Analyzing and Applying Marketing Information
  • MTH 510: Numerical Methods I
  • MTH 511: Numerical Methods II
  • QM 526: Business Forecasting
  • QM 564: Decision Support Systems