The projects will provide an opportunity for businesses and learners to collaborate to

identify and address real business challenges.

The projects, which can be short, will allow the student to apply the data management

concepts and techniques presented in the classes to address the sponsors business

challenges. Some examples are:

• Identify and use various “big data analytics” tools, algorithms, and terminologies
• Apply text analytics, sentiment analysis and NLP
• Identify and apply machine learning algorithms and how to “scale” those to big
• data: trees with ensemble methods, neural networks
• Asist the sponsor in determining if the organization should invest in “big data
• analytics” technologies

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. Merchandising for trade areas (categories)

4. Quantifying Customer Lifetime Value

5. Determining media consumption (mass vs digital)

6. Cross-sell and upsell opportunities

7. Develop high propensity target markets

8. Customer segmentation (behavioral or transactional)

9. New Product/Product line development

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

11. Ranking markets by potential revenue

12. 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.