Course Syllabus:

  1. Basic Statistics
    • Probabilities, mean, variance. Basic theorems and notions from probability theory.
    • Common distributions. Normal (Gaussian) distribution. Distributions connected to normal.
    • Sample and empirical distribution
    • Methods of estimation. Moment estimators. Maximum likelihood estimation. Confidence bounds
  2. Basics of Hypotheses Testing
    • First steps (Student t-test and around)
    • Non-parametric tests (Wilcoxon, Mann-Whitney)
    • Goodness-Of-Fit tests: Kolmogorov-Smirnov, Anderson-Darling, chi-square, etc.
  3. Linear models
    • Basics. Linear Regression. Confidence bounds. Model estimation (F-test, informational criteria ala Akaike)
    • Comparisons in groups. One-way, two-way ANOVA
  4. Statistical analysis of microarray data.
  5. P-value / E-value в BLAST
  6. Error correction in NGS data


We use R and Bioconductor.