Computational Skills Development and Mastery for MSTP
Computational Skills Development and Mastery for MSTP Heading link
For MSTP students, learning to code can open numerous opportunities for innovation and enhance students’ research capabilities. The Computational Skills Development and Mastery for MSTP (CSDMM) course is an asynchronous, longitudinal course offering for current MSTP students designed to further students’ computational skills by focusing on data analysis, bioinformatics, and statistical analysis. This course can make students more versatile and valuable in both clinical and research settings, ultimately contributing to the advancement of healthcare.
With increasing access to large data, MSTP students can benefit from proficiency in key programming languages. An understanding of basic programming skills can facilitate statistical analysis for research projects and improve data interpretation and presentation. The educational outcomes of CSDMM are to 1) learn and practice how to analyze and synthesize data, 2) develop an understanding of how data science can be individually integrated into each student’s present or future research, 3) learn and practice coding based on the student’s individual skill level and preferred programming language, and 4) foster a diverse and collaborative culture with students across all skill levels.
This course aims to familiarize students with existing analysis workflows and remove time-consuming barriers to mastering syntax and computational analysis. Through teaching valuable steps in learning to analyze and visualize data, CSDMM will provide introductory and intermediate training in key programming languages’ syntax and data visualization.
Topics are dynamic and adapted to each student’s research goals and programming language. These include but are not limited to the following topics:
- Introduction to programming syntax
- Version control and Git
- Data manipulation
- Data visualization
- Inferential statistics
- Machine learning
- Leveraging large language models
Advanced topics and resources are offered to students to incorporate into their training, along with novel methods and data analytic approaches.
All students are encouraged to assist each other when specific questions arise. Experienced students are encouraged to contribute to future curriculum building with specific topics related to their research.