Riipen Riipen
Log in Sign up

Business Analytics Applied Capstone

MSBA 795
Open Closing on March 22, 2025
George Mason University
George Mason University
Fairfax, Virginia, United States
Suneel Grover
Suneel Grover
Educator
2
Experience
1/15 project matches
Dates set by teams
Preferred companies
United States
Any company type
Any industries

Experience scope

Categories
Artificial intelligence Data visualization Data analysis Data modelling Data science
Skills
python (programming language) r (programming language) predictive modeling statistical analysis sas (software) segmentation analysis time series analysis and forecasting machine learning
Learner goals and capabilities

George Mason University’s Business Analytics Applied Capstone provides MS in Business Analytics students with the opportunity to engage in hands-on, real-world projects. Learners are final-year students with advanced analytical skills, trained in breaking down complex business problems and providing actionable solutions using data analytics. Throughout the course, students will build on their ability to work with varying forms of data, communicate insights to technical and non-technical stakeholders, and use advanced analytics tools. These learners are skilled in data analysis, using tools like Python, R, SAS, Tableau & Power BI to collect experiences in solving real-world business problems.


The practicum and capstone are designed to apply analytics knowledge gained in the classroom to real-world problems. In this 8-week course, students will work in teams on a sponsor’s project to receive academic credits. External project sponsors propose a project and work closely with the students over the semester.

Learners

Learners
Graduate
Intermediate, Advanced levels
80 learners
Project
50 hours per learner
Educators assign learners to projects
Teams of 5
Expected outcomes and deliverables

The George Mason University’s MS in Business Analytics students are conducting a pro-bono project, not an official internship.


  • Industry sponsors propose a project, which is often based on mock data, publicly available data or owned data assets which have sensitive data removed. To be clear, students do not need access to proprietary information.
  • Teams of several students (4-5) are matched to accepted projects.
  • Industry sponsors meet with the team to provide guidance and feedback for the duration of the project.
  • With the help of their industry and faculty mentors, students embark on a full cycle data analytics process using tools provided by GMU and/or the sponsor.


At the conclusion of this collaboration, employers can expect:


  • A comprehensive presentation & research summary detailing the analysis, key findings, and actionable recommendations.
  • A final presentation is delivered on-campus on the 8th week of the course where students will present their methodology, findings, and proposed solutions, with a Q&A session for further clarification. Sponsoring organizations are welcome to attend, but this is optional based on availability, as well as geography.
  • Requesting a virtual presentation from student teams is another option for sponsors to consider in receiving the final presentations.


Project Examples

Requirements

Submitted projects are assessed for feasibility and quality. 


Positive considerations for feasibility include:


  • Use of public data, or data that is stored and controlled by the sponsor with access provided to students. 
  • Use of standard computing technologies, or specialized computing technology (SAS/Python/R/Tableau/Power BI, etc.) that are available to students on-campus. 
  • Medium complexity and difficulty levels that challenge a student group but are realistically achievable in the 8-week course. 
  • Exposure to novel technologies and approaches. 


Positive considerations for quality include:


  • Polished proposals. 
  • Proposals that align with industry standards or best practices. 
  • Proposals that align with ethical and responsible analytical practices. 


For Summer 2025, proposals are due by March 1st, 2025. Acceptance decisions will be

announced in late March 2025.


Examples include:


  1. Customer Segmentation Analysis: Analyze customer data to identify key segments and provide recommendations for targeted marketing strategies.
  2. Supply Chain Optimization: Use historical data to model and optimize the supply chain process, reducing costs and improving efficiency.
  3. Sales Forecasting Model: Develop a time-series model using past sales data to forecast future sales and help inform inventory management.
  4. Employee Attrition Prediction: Use HR data to identify key factors contributing to employee turnover and propose solutions to increase retention.
  5. Customer Predictive Model: Use customer-oriented data to create a predictive model and propensity scores for a customer experience or marketing-centric use case.


Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

  • Q1 - Text long
    Please share your project title.  *
  • Q2 - Text long
    Any relevant information on the sponsor’s business, the business problem, prior analyses, and links to known business and/or academic literature.  *
  • Q3 - Text long
    Project overview and definition. The problem definition should merit the use of data visualization, advanced analytical or machine learning techniques. At the same time, the scope of the problem has to be constrained in a way that allows it to be well addressed by the assigned student team during the eight-week course.  *
  • Q4 - Text short
    Project goals. Clearly identify project goals and critical factors to indicate project success.  *
  • Q5 - Text short
    Potential Roadblocks and Barriers to Success. Based on prior attempts at this project, what were known barriers? These could include access to data, inadequate analytical capabilities, cloud compute capacity, API access, or others.  *
  • Q6 - Text long
    Preferred Methodology. Based on the sponsor’s understanding of the problem, is there a vision for the data or analytics methodology?  *
  • Q7 - Text long
    Will the project sponsor be providing some or all of the data or will the team need to acquire data?  *
  • Q8 - Text long
    The project sponsor’s vision regarding the type of analytics to solve the problem. For example, exploratory data analysis, data mining, customer/marketing/retail/pricing analysis, forecasting, etc.  *
  • Q9 - Text long
    The team will be providing the project sponsor with all data, visualizations, presentation summary and program code (if applicable) at the end of the 8-week experience. Does the project sponsor have any additional considerations?  *
  • Q10 - Text long
    Will the students need to sign an NDA? Would you prefer not to be mentioned in public communications? Other concerns related to confidentiality? These are common requests and do not affect our consideration of your proposal.  *
  • Q11 - Text long
    Will the project sponsor be providing a budget for data acquisition, if necessary?  *
  • Q12 - Text short
    If your project is not selected for this semester, are you interested in participating in future semesters?  *