Course Description
This course will examine and apply analytic methods, data handling, and data cleansing techniques, strategies and the need/use of Information Technology (IT) tools for data collection, data analysis, reporting and knowledge management. This course will apply current theoretical models and research to clinical practices to gain new knowledge from data. Students will be required to use analytic tools for analyzing healthcare data with statistics, data visualization, data mining, big data, data warehousing and report generation.
Prerequisites
- None
Associated Program Learning Outcomes
Upon successful completion of the course, the student will be able to:
- #1. Apply healthcare informatics and technology concepts and skills to case studies and real-world situations
- #4. Improve the various healthcare functions associated with the integration of information technology by implementing technology initiatives
- #6. Compile, conduct and create new information based on the use of technology and datasets through data analytics.
Student Learning Outcomes (SLOs)
Upon successful completion of the course, the student will be able to:
- Leverage modern technology tools to aid and automate data analysis.
- Formulate and discover new insights from data by applying analytics methods.
- Integrate insights and context for improved storytelling.
- Adopt and leverage visual models to effectively communicate results.
- Analyze the use of data analytics in decision making.
Course Activities and Grading
| Assignments | Weight |
|---|---|
Discussions (80 Pts, Weeks 1-8) | 20% |
Paper 1 (50 Pts, Week 2) | 10% |
Project Assignments (100 Pts, Week 3, 6) | 18% |
Paper 2 (50 Pts, Week 2) | 8% |
Paper 3 (50 Pts, Week 4) | 8% |
Paper 4 (50 Pts, Week 5) | 8% |
Final Project (100 Pts, Week 6 and 7) | 20% |
Final Report (50 Pts, Week 8) | 10% |
Total | 100% |
Required Textbook
Available through Charter Oak State College's Book Bundle
- Provost, F., & Fawcett, T. (2013). Data science for business. Sebastopol, CA: OReilly. ISBN-13: 978-1-449-36132-7
Additional Resources
- Other course material, including case study information, will be provided as required reading within the course.
- James, G. (2017). An introduction to statistical learning: With applications in R. Springer. (Download here from website). This book is downloadable free from Stanford University.
Course Schedule
Week | PLOs | SLOs | Readings and Exercises | Assignments |
1 | 4,6 | 1,2 | Topic: Data-Analytic Thinking
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2 | 4,6 | 1,2,5 | Topics: Business Problems and Data Science Solutions
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3 | 1,4,6 | 1,2,5 | Topics: Introduction to Data Analytics and Predictive Modeling
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4 | 1,4,6 | 1,2,5 | Topic: Fitting a model to data
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5 | 1,4,6 | 1,2,4,5 | Topics: Storytelling with Data
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6 | 1,6 | 1,2,3,4,5 | Topic: Data Visualization using Tableau
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7 | 1,6 | 1,4 | Topic: Advanced Data Visualization using Tableau
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8 | 1,4,6 | 1,2,3,4,5 | Topics: Final Project and Virtual Conference
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COSC Accessibility Statement
Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.
COSC Policies, Course Policies, Academic Support Services and Resources
Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.