3 credits
Fall 2025 Lecture Upper Division
Statistical methods are important for data analysis and understanding the trends in the dataset. This course will provide an introduction to the analysis of biological data in a statistical framework using standard computational methods. The course will have a lecture and hands-on component to introduce students to topics and then utilize them to solve typical problems in Health Sciences. In addition to learning the statistical concepts, the students will also be introduced to computational approaches for data analysis. The combination of statistical and computational concepts along with the hands-on experience will help students in their research projects. The topics covered include data representation, sample statistics, probability, common discrete and continuous distribution, confidence interval estimation, experimental design, analysis of variance, statistical methods for hypothesis testing, linear and logistic regression, correlation, power analysis, graph theory, network analysis, omics-based analysis, and data visualization. The course is ideal for Health Sciences students who perform data analysis and are interested in implementing these approaches in their research. Permission of department required.
Learning Outcomes1Describe the fundamentals of statistics and computational concepts useful for research.
2Think critically and analyze methods of data collection, statistical tools for data analysis, and presentation of results.
3Evaluate systematically statistical procedures and summaries.
4Learn methods for computational analysis and data visualization.
5Participate actively in developing projects to apply statistical and computational approaches.
Restrictions
made by Purdue students.