The project provides an opportunity for businesses and learners to collaborate to

identify and translate a real business problem into an analytics problem.

The projects, which can be short, will allow the student to apply the skills acquired on

the various tools to address the business problem. Some examples are:

• Install and use a Linux distribution on a Virtual Machine
• Discuss the differences between relational databases and NoSQL databases
• Explain the basic components of the Hadoop ecosystem
• Address the business problem using:

-the Cassandra Query Language (CQL) to store and retrieve data to/from Cassandra

-the MongoDB Shell to store and retrieve data to/from MongoDB

-popular tools (like Tableau and Microsoft Power BI) to visualize graphs and charts with data from a NoSQL database

-Python and a graphics library to interface to a database and visualize graphs and charts

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.