Overview: All Tracks

The M.S. Data Science and Business Analytics curriculum is structured to be completed in a single year (fall, spring/summer, and winter) or a two year program for part-time students, with a total of 30 credit-hours required for each student. The program offers three areas of specialization: Advanced "Analytics", Computational "Engineering", or Data-Driven "Business". Trained by world-class faculty under the stewardship of a strong Industrial Advisory Board, students will learn identification and framing of problems; acquisition, management, and utilization of large and fast-moving streams of data; creation, analysis, solution, and interpretation of mathematical models using appropriate methodology; and the integration of these interdisciplinary skills to enable graduates to successfully develop and execute data science and business analytics projects.

The "interdisciplinary core" includes 9 hours of coursework across business, computer science, and industrial engineering. On top of this integrated breadth of study covering the core areas of data science and business analytics, each student has 9 hours of "track specialized" courses to give them depth in an engineering, business, or analytics area of specialization. Each student's 6 credits of elective choices can be personalized to support their individual career goals. The final piece of the curriculum is a 6-credit applied analytics practicum, in which students will work with companies and organizations on real analytics problems.

Base Curriculum - All Tracks

  • Data Science Analytics (3 Credit Hours)
  • Data Science Strategy & Leadership (3 Credit Hours)
  • Computing Platforms for Data Science (3 Credit Hours)
  • 3 Specialization Tracks consisting of: 3 Core Courses (9 Credit Hours in each Track)
  • 2 Elective Courses (6 Credit Hours)
  • Data Science and Business Analytics Practicum (6 Credits)
  • Each student's course choices must satisfy the requirements of at least one of the defined tracks (Advanced Analytics, Computational Engineering, or Data-Driven Business).

M.S. Data Science Track Options:

Advanced "Analytics" Track

The Analytics track provides students with a greater knowledge and understanding of the quantitative methodology of descriptive, predictive, and prescriptive analytics: how to select, build, solve, and analyze models using methodology such as parametric and non-parametric statistics, regression, forecasting, data mining, machine learning, optimization, stochastics, and simulation. For a full list of analytics track courses and objectives, visit: Advanced Analytics.

Data-Driven "Business" Track

The Business Analytics track provides students with a deeper understanding of the practice of using analytics in business and industry: how to understand, frame, and solve problems in marketing, operations, finance, management of information technology, human resources, and accounting in order to develop and execute analytics projects within businesses. For a full list of business track courses and objectives, visit: Data-Driven Business.

Computational "Engineering" Track

The Engineering track provides students with a deeper understanding of the practice of dealing with so-called "big data": how to acquire, preprocess, store, manage, analyze, and visualize data arriving at high volume, velocity, and variety. For a full list of engineering track courses and objectives, visit: Computational Engineering.

Elective Courses

The program offers an extensive list of Elective Courses. For a sample list, visit: Elective Courses.

Capstone Experience: Applied Data Science and Business Analytics Practicum - All Tracks

At the conclusion of the program, in the spring/summer semester, each student will complete a 6-credit-hour applied Data Science & Business Analytics practicum. For the practicum course, students have two choices.  Many students may want to pursue an applied analytics internship as their practicum, working on a significant analytics project at a company or organization site.  Other students may prefer to work in cross-disciplinary teams on a significant data science and business analytics project that companies and organizations bring to campus; those teams will consist of M.S. Data Science students from each track, to bring each of their specializations to bear in an integrated solution. In this way, the interdisciplinary learning will be emphasized in practice as well as in the classroom.

Additional Resources: All Tracks

In addition to learning from world leaders and cutting-edge researchers in data science and business analytics, students in the M.S. Data Science program will have access to a variety of specialized resources, including:

  • Academic and professional advising
  • Job placement support
  • Funding to attend a major analytics conference
  • Exposure at Wayne State's annual Big Data and Business Analytics Symposium
  • Wayne State's state-of-the-art high-performance computing infrastructure for massive-scale analytics
  • Free cloud computing resources
  • Free and discounted analytics, engineering, and productivity software