Teaching
BIOL 5648 - Coding and Statistical Thinking in the Neurosciences

BIOL 5648 - Coding and Statistical Thinking in the Neurosciences

Course Information

Course: BIOL 5648 - Coding and Statistical Thinking in the Neurosciences
Semester: Spring 2020
Institution: Washington University in St. Louis
Course Directors: Timothy Holy, Larry Snyder, Edward Han, Alexxai Kravitz
Teaching Assistant: Binxu Wang
Status: Required course for first-year Neuroscience graduate students

Course Description

This course provides essential computational and statistical skills for neuroscience research. Students learn programming fundamentals, statistical methods, and data analysis techniques specifically tailored for neuroscience applications.

Course Development

This was a first-time course offering developed collaboratively by faculty and graduate students to address the growing need for computational skills in modern neuroscience research. As a teaching assistant, Binxu Wang contributed to course development and curriculum design.

Topics Covered

Statistical Methods

  • Bootstrapping Techniques
    • Theoretical foundations of bootstrap methods
    • Implementation in neuroscience contexts
    • Confidence interval estimation
    • Hypothesis testing applications

Programming Skills

  • Statistical programming fundamentals
  • Data manipulation and visualization
  • Reproducible research practices
  • Version control and collaboration tools

Neuroscience Applications

  • Neural data analysis workflows
  • Statistical inference in neuroscience
  • Handling experimental variability
  • Multiple comparisons and corrections

Course Materials

Tutorial Content

  • Bootstrapping Module - Comprehensive tutorial on bootstrap methods
    • Theoretical background
    • Practical implementation
    • Neuroscience-specific examples
    • Hands-on coding exercises