NSCI 801 - Quantitative Neuroscience Syllabus

NSCI 801 (Queen’s U) Quantitative Neuroscience course materials

This course is in tutorial format using Python and Google Colab.

Syllabus

Introduction (Gunnar)

Intro Python (Joe)

Advanced Python (Joe)

Data collection / signal processing (Joe)

Statistics and Hypothesis testing - basics (Joe)

  • Descriptors: central tendencies (mean, median, mode), Spread (Range, variance, percentiles), Shape (skew, kurtosis)

  • Correlation / regression

  • The logic of hypothesis testing

  • Statistical significance

  • Multiple comparisons

  • Different test statistics

  • Confidence intervals

    Descriptive Statistic (NSCI801_Descriptive_stats.ipynb)

Statistics and Hypothesis testing - advanced (Joe)

Quantitative wet lab / bench methods (Joe)

Statistics and Hypothesis testing - Bayesian (Gunnar)

Models in Neuroscience (Gunnar)

Data Neuroscience overview (Gunnar)

Correlation vs causality (Gunnar)

Reproducibility, reliability, validity (Gunnar)

Further readings