NPTEL : NOC:Data Analysis for Biologists (Biotechnology)

Co-ordinators : Prof. Biplab Bose


Lecture 1 - Rules of probability

Lecture 2 - Discrete probability distribution

Lecture 3 - Continuous probability distribution

Lecture 4 - Moments: mean and variance

Lecture 5 - Moments: variance and covariance

Lecture 6 - Bayes theorem and likelihood

Lecture 7 - Concept of statistical tests

Lecture 8 - Vector and vector operations

Lecture 9 - Matrix and matrix operations

Lecture 10 - Determinant and Inverse of a matrix

Lecture 11 - Eigenvalue and eigenvector

Lecture 12 - Linear system of equations

Lecture 13 - Singular value decomposition

Lecture 14 - Getting ready with R

Lecture 15 - Algebraic and logical operations in R

Lecture 16 - Reading and writing data

Lecture 17 - Statistics using R - descriptive statistics

Lecture 18 - Statistics using R - t-test and ANOVA

Lecture 19 - Linear algebra using R

Lecture 20 - Scatter plot, Line plot and Bar plot

Lecture 21 - Histogram and Box plot

Lecture 22 - Heatmap and Volcano plot

Lecture 23 - Network visualization

Lecture 24 - Data visualization using ggplot2 - I

Lecture 25 - Data visualization using ggplot2 - II

Lecture 26 - Correlations

Lecture 27 - Linear regression - I

Lecture 28 - Linear regression - II

Lecture 29 - Linear regression using R

Lecture 30 - Multiple linear regression

Lecture 31 - Multiple linear regression using R

Lecture 32 - Nonlinear regression

Lecture 33 - Nonlinear regression using R

Lecture 34 - Clustering and classification

Lecture 35 - Logistic regression

Lecture 36 - Logistic regression using R

Lecture 37 - Distance mesaures for clustering

Lecture 38 - k-means clustering

Lecture 39 - k-means clustering using R

Lecture 40 - Hierarchical clustering

Lecture 41 - Hierarchical clustering using R

Lecture 42 - Decision tree classifier

Lecture 43 - Support vector machines

Lecture 44 - Higher-dimensional data in biology

Lecture 45 - Principle component analysis

Lecture 46 - Principle component analysis using R

Lecture 47 - t-SNE

Lecture 48 - t-SNE using R

Lecture 49 - Diffusion maps