The project should provide an opportunity for the students to collaborate with the project sponsor to identify and translate a real business problem into an analytics problem. The projects can be short, where the students can apply their learnings to address the sponsors business problem. Some examples are:

• Describe the Data Analytics Lifecycle, its stages how they complement each other and applicability to the business problem
• Explore, cleanse and extract data to determine the technical and analytical feasibility of presenting viable solutions to address business problem(s)
• Design viable data analytics solution(s) to a set of specific business needs.
• Assess data management tools to perform ETL (Extract Transform and Load) activities 
• Examine and create data visualizations that effectively communicate with audiences at various organizational levels
• Present data management and project management principles related to the Data Analytics Lifecycle in addressing the business problem

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. Customer acquisition and retention
2. Cross-sell and upsell opportunities
3. Develop high propensity target markets
4. Customer segmentation (behavioral or transactional)
5. New Product/Product line development
6. Ranking markets by potential revenue

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.