The project provides an opportunity for businesses and students to identify and translate a real business problem into an analytics problem(s). The projects can be short and based on the information provided the students will apply their learnings to address the sponsors business problem. Some examples are:

• Interpret and produce various graphical displays of data and information and learn how to choose the most appropriate technique in a variety of situations
• Interpret and compute confidence intervals and data statistics (mean, median, histograms and significant differences).
• Solve problems with statistical variables that have a binomial, Poisson, normal or other probability distributions
• Use multiple regression to predict a response variable and determine the most significant predictor variables
• Use R and Python to process and analyze data
• Apply all these concepts on a business problem presented by the sponsor

You should submit a high-level proposal/business problem statement including relevant data sets and definitions, a list of acceptable tools (if applicable), and expected deliverables. Business datasets could be provided based on a non-disclosure agreement or in an anonymized/synthetic data format that is relevant to your organization and business problem. The course instructors will review the documents to confirm the scope and timing of the proposed problem and its alignment with the capstone course requirements.

Analytics solution may be applicable for (however they are not limited to) the following topics:

1. Demand for social services (healthcare, emergency services, infrastructure, etc.)

2. Customer acquisition and retention

3. Quantifying Customer Lifetime Value

4. Cross-sell and upsell opportunities

5. Develop high propensity target markets

6. Customer segmentation (behavioral or transactional)

7. New Product/Product line development

8. Market Basket Analysis to understand which items are often purchased together

9. Ranking markets by potential revenue

10. Consumer personification

To ensure students’ learning objectives are achieved, we recommend that the datasets are at least 20,000+ rows in size. Data need to be ‘clean’. If more than one database is provided, which must be conjoined, students will be required to integrate them. This supports the learning experience and minimizes partner data preparation.