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Updated in [August 18th, 2023]

Skills and Knowledge:
This course will provide students with the skills and knowledge to analyze and quantify differences in gene expression using public datasets from the Gene Expression Omnibus. Students will gain a detailed understanding of how gene expression analysis works, including what is fold change. Through examples, students will learn how to use tools like GEO2R, StringDB, PantherDB, and more to analyze publicly available gene expression data. Additionally, students will gain insight into how to write a paper about their project. By the end of the course, students will have the skills to identify potential biomarkers for cancer, analyze changes in gene expression when a sample is treated with a drug or under a condition, and identify differences in gene expression between early and late stage cancer.
Professional Growth:
This course provides a comprehensive introduction to gene expression analysis and its applications in the field of professional growth. It covers the fundamentals of gene expression analysis, such as fold change, and provides examples of how Python can be used to analyze and visualize gene expression data. Additionally, the course provides guidance on how to choose a research topic, find a dataset, process the data, and analyze the data graphically with several tools. Finally, the course provides insight into how to write a paper about the project. By taking this course, professionals will gain a detailed understanding of gene expression analysis and its applications, as well as the skills to apply it to their own research.
Further Education:
This course is suitable for preparing further education as it provides a detailed understanding of how gene expression analysis works, and how to use tools like GEO2R, StringDB, PantherDB, and more to analyze publicly available gene expression data. Additionally, the course provides insight into how to write a paper about a research project, and example topics for research. Finally, the course provides a bonus of how to use machine learning for gene expression analysis, if there is enough interest.

Course Syllabus

Introduction

Finding a Dataset and Using GEO

Using Bioinformatics Tools

Python for Data Analysis and Visualization

Next Steps