The project(s) should provide an opportunity for businesses and learners to collaborate 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:

• Apply core statistics principles—including probability, hypothesis testing, data analysis, and data visualization—to specific health data.
• Apply descriptive statistics, inferential statistics, linear regression, and other foundational statistical tools for health data analysis, and determine when each should be used.
• Apply core statistical concepts—including levels of measurement; mean, standard deviation, range, cross tabulation; probability, tests of significance, binomial and normal distribution, T-tests, ANOVA, and correlation—in the analysis of healthcare data
• Access healthcare datasets through Open Access from CIHI and other sources for the analysis
• Explain trends in healthcare system data with software tools such as Tableau, Microsoft Excel, and SPSS
• Create data visualizations to help provide evidence to inform managerial and healthpolicy decision-making
• Apply statistical concepts, tools, and principles to concrete cases and problems in their own professional context.

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. Quantifying Customer Lifetime Value
3. Determining media consumption (mass vs digital)
4. Develop high propensity target markets
5. New Product/Product line development
6. 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.