Data Analysis for Biologists (English)

   Sunday, April 07, 2024   

Starting a new Lecture Notes Series on Data Analysis for Biologists

Youtube Lecture Playlist Credits 

Channel Name: NPTEL IIT Guwahati

So Let Us Start to This Journey of Learning 

Data Analysis for Biologists By Lecture Notes together!

Lecture 1: Data Analysis for Biologists

Lecture 2: Lec 1: Rules of probability

Lecture 3: Lec 2: Discrete probability distribution

Lecture 4: Lec 3: Continuous probability distribution

Lecture 5: Lec 4: Moments: mean and variance

Lecture 6: Lec 5: Moments: variance and covariance

Lecture 7: Lec 6: Bayes theorem and likelihood

Lecture 8: Lec 7: Concept of statistical tests

Lecture 9: Lec 8: Vector and vector operations

Lecture 10: Lec 9: Matrix and matrix operations

Lecture 11: Lec 10: Determinant and Inverse of a matrix

Lecture 12: Lec 11: Eigenvalue and eigenvector

Lecture 13: Lec 12: Linear system of equations

Lecture 14: Lec 13: Singular value decomposition

Lecture 15: Lec 14: Getting ready with R

Lecture 16: Lec 15: Algebraic and logical operations in R

Lecture 17: Lec 16: Reading and writing data

Lecture 18: Lec 17: Statistics using R – descriptive statistics

Lecture 19: Lec 18: Statistics using R – t-test and ANOVA

Lecture 20: Lec 19: Linear algebra using R

Lecture 21: Lec 20: Scatter plot, Line plot & Bar plot

Lecture 22: Lec 21: Histogram & Box plot

Lecture 23: Lec 22: Heatmap and Volcano plot

Lecture 24: Lec 23: Network visualization

Lecture 25: Lec 24: Data visualization using ggplot2 - I

Lecture 26: Lec 25: Data visualization using ggplo2 - II

Lecture 27: Lec 26: Correlations

Lecture 28: Lec 27: Linear regression - I

Lecture 29: Lec 28: Linear regression - II

Lecture 30: Lec 29: Linear regression using R

Lecture 31: Lec 30: Multiple linear regression

Lecture 32: Lec 31: Multiple linear regression using R

Lecture 33: Lec 32: Nonlinear regression

Lecture 34: Lec 33: Nonlinear regression using R

Lecture 35: Lec 34: Clustering and classification

Lecture 36: Lec 35: Logistic regression

Lecture 37: Lec 36: Logistic regression using R

Lecture 38: Lec 37: Distance mesaures for clustering

Lecture 39: Lec 38: k-means clustering

Lecture 40: Lec 39: k-means clustering using R

Lecture 41: Lec 40: Hierarchical clustering

Lecture 42: Lec 41: Hierarchical clustering using R

Lecture 43: Lec 42: Decision tree classifier

Lecture 44: Lec 43: Support vector machines

Lecture 45: Lec 44: Higher-dimensional data in biology

Lecture 46: Lec 45: Principle component analysis

Lecture 47: Lec 46: Principle component analysis using R

Lecture 48: Lec 47: t-SNE

Lecture 49: Lec 48: t-SNE using R

Lecture 50: Lec 49: Diffusion maps