Statistical Analysis for Health Data
General
- Continuing Education
- 15 learners; teams of 4
- 40 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 1/1 project matches
- Anywhere
- Academic experience
- Any company type
- Any industries
Categories
Skills
Project timeline
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September 24, 2023Experience start
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November 26, 2023Experience end
Overview
- Details
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This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc.
This course provides a foundation to explore health data through computing and statistical analysis. Focus is placed on the structure and applications of probability, statistics, computer simulation and data analysis as applied to various types of health data. In particular, students will investigate descriptive statistics, inferential statistics, linear regression and probability concepts, hypothesis testing and foundational statistical tools are applicable to data analysis. Common statistical and programming tools will be used. Students should have an introductory/basic understanding of statistics for this course.
- Learner skills
- Adult education, Linear regression, Statistical analysis, Computer simulation, Statistical inference, Programming tools, Statistical hypothesis testing, Computer science, Probability, Descriptive statistics
- Deliverables
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The final project deliverables will include:
- A report on students’ findings and details of the problem presented
- Future collaboration ideas will be identified based on current project outcomes
Project Examples
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:
- Demand for social services (healthcare, emergency services, infrastructure, etc.)
- Quantifying Customer Lifetime Value
- Determining media consumption (mass vs digital)
- Develop high propensity target markets
- New Product/Product line development
- 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.