**The Basics:**

- Histograms, Clearly Explained
- What is a statistical distribution?
- The Normal Distribution
- The Binomial Distribution
- What is a statistical model?
- What does it mean to “sample from a distribution”?
- The Central Limit Theorem (or “How I Learned to Stop Worrying and Love the t-test”).
- The Difference between Technical and Biological Replicates
- The sample size and the effective sample size
- Standard Deviation vs Standard Error
- The Standard Error
- Bar Charts Are Better Than Pie Charts
- Boxplots, Clearly Explained
- Logs (logarithms), clearly explained
- Confidence Intervals
- R-squared explained
- Linear Models Part 0: Fitting a line to data, aka Least Squares, aka Linear Regression
- Fitting a curve to data, aka Lowess, aka Loess
- Linear Models Part 1: Linear Regression
- Linear Models: Linear Regression in R
- Linear Models Part 1.5: Multiple Regression
- Linear Models: Multiple Regression in R
- Linear Models Part 2: t-tests and ANOVA
- Linear Models Part 3: Design Matrices
- Linear Models: Design Matrix Examples in R
- Quantiles and Percentiles
- Quantile-Quantile Plots (QQ Plots)
- Quantile Normalization
- Probability vs Likelihood
- Maximum Likelihood
- Maximum Likelihood: A worked out example for the exponential distribution
- Maximum Likelihood: A worked out example for the binomial distribution
- Maximum Likelihood: A worked out example for the normal distribution
- Odds and Log(Odds)
- Odds Ratios and Log(Odds Ratios)

**Statistical Tests:**

- Enrichment Analysis using Fisher’s Exact Test and the Hypergeometric Distribution
- Which t-test to use
- p-values, clearly explained
- One or Two Tailed p-values
- Thresholds for Significance
- FDR and the Benjamini-Hochberg Method clearly explained
- p-hacking and power calculations

**Machine Learning and Dealing with large datasets that have lots and lots of measurements per sample:**

(NOTE: All of the linear model and curve fitting stuff in the “Basics” section is also considered to be Machine Learning, so make sure you check out those videos too).

- Machine Learning Fundamentals: Bias and Variance
- Regularization Part 1: Ridge Regression
- Regularization Part 2: Lasso Regression
- Regularization Part 3: Elastic-Net Regression
- Linear Discriminant Analysis (LDA) clearly explained
- Principal Component Analysis (PCA) Step-by-Step
- Principal Component Analysis (PCA) explained in less than 5 minutes
- PCA – Practical Tips
- DEPRECATED: Principal Component Analysis (PCA) clearly explained (more details)
- PCA in R
- PCA in Python
- Multi-Dimensional Scaling (MDS) and Principal Coordinate Analysis (PCoA) clearly explained
- MDS and PCoA in R
- t-SNE, clearly explained
- Heatmaps – considerations for drawing and interpreting them
- Hierarchical Clustering
- K-Means Clustering
- K-Nearest Neighbors
- Decision Trees Part 1: Building and Using
- Decision Trees Part 2: Feature Selection and Missing Data
- Random Forests Part 1: Building, using and evaluating
- Random Forests Part 2: Missing data and clustering
- Random Forests in R
- Logistic Regression
- Logistic Regression, Details Part 1: Coefficients
- Logistic Regression, Details Part 2: Maximum Likelihood
- Logistic Regression, Details Part 3: R-squared and its p-value
- Saturated Models and Deviance Statistics
- Deviance Residuals
- Logistic Regression in R

**High-throughput Sequencing Analysis:**

- A gentle introduction to RNA-seq
- edgeR, part1: Library Normalization
- DESeq2, part1: Library Normalization
- edgeR and DESeq2, part2: Independent Filtering (removing genes with low read counts)
- RNA-seq – the problem with technical replicates
- RPKM, FPKM, and TPM

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