Data Analysis for Biologists

Computer Science

2024-01-18

Lessons List

Data Analysis for Biologists Intro Video

Lec 1 Rules of probability

Lec 2 Discrete probability distribution

Lec 3 Continuous probability distribution

Lec 4 Moments mean and variance

Lec 5 Moments variance and covariance

Lec 6 Bayes theorem and likelihood

Lec 7 Concept of statistical tests

Lec 8 Vector and vector operations

Lec 9 Matrix and matrix operations

Lec 10 Determinant and Inverse of a matrix

Lec 11 Eigenvalue and eigenvector

Lec 12 Linear system of equations

Lec 13 Singular value decomposition

Lec 14 Getting ready with R

Lec 15 Algebraic and logical operations in R

Lec 16 Reading and writing data

Lec 17 Statistics using R – descriptive statistics

Lec 18 Statistics using R – t test and ANOVA

Lec 19 Linear algebra using R

Lec 20 Scatter plot Line plot Bar plot

Lec 21 Histogram Box plot

Lec 22 Heatmap and Volcano plot

Lec 23 Network visualization

Lec 24 Data visualization using ggplot2 I

Lec 25 Data visualization using ggplo2 II

Lec 26 Correlations

Lec 27 Linear regression I

Lec 28 Linear regression II

Lec 29 Linear regression using R

Lec 30 Multiple linear regression

Lec 31 Multiple linear regression using R

Lec 32 Nonlinear regression

Lec 33 Nonlinear regression using R

Lec 34 Clustering and classification

Lec 35 Logistic regression

Lec 36 Logistic regression using R

Lec 37 Distance mesaures for clustering

Lec 38 k means clustering

Lec 39 k means clustering using R

Lec 40 Hierarchical clustering

Lec 41 Hierarchical clustering using R

Lec 42 Decision tree classifier

Lec 43 Support vector machines

Lec 44 Higher dimensional data in biology

Lec 45 Principle component analysis

Lec 46 Principle component analysis using R

Lec 47 t SNE

Lec 48 t SNE using R

Lec 49 Diffusion maps

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Fatemeh Bayat

good 2025-01-09

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Data Analysis for Biologists course, In this course,\ we will learn about essential data analysis techniques tailored to biological research and applications.

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Data Analysis for Biologists

Computer Science

Data Analysis for Biologists Intro Video

Lec 1 Rules of probability

Lec 2 Discrete probability distribution

Lec 3 Continuous probability distribution

Lec 4 Moments mean and variance

Lec 5 Moments variance and covariance

Lec 6 Bayes theorem and likelihood

Lec 7 Concept of statistical tests

Lec 8 Vector and vector operations

Lec 9 Matrix and matrix operations

Lec 10 Determinant and Inverse of a matrix

Lec 11 Eigenvalue and eigenvector

Lec 12 Linear system of equations

Lec 13 Singular value decomposition

Lec 14 Getting ready with R

Lec 15 Algebraic and logical operations in R

Lec 16 Reading and writing data

Lec 17 Statistics using R – descriptive statistics

Lec 18 Statistics using R – t test and ANOVA

Lec 19 Linear algebra using R

Lec 20 Scatter plot Line plot Bar plot

Lec 21 Histogram Box plot

Lec 22 Heatmap and Volcano plot

Lec 23 Network visualization

Lec 24 Data visualization using ggplot2 I

Lec 25 Data visualization using ggplo2 II

Lec 26 Correlations

Lec 27 Linear regression I

Lec 28 Linear regression II

Lec 29 Linear regression using R

Lec 30 Multiple linear regression

Lec 31 Multiple linear regression using R

Lec 32 Nonlinear regression

Lec 33 Nonlinear regression using R

Lec 34 Clustering and classification

Lec 35 Logistic regression

Lec 36 Logistic regression using R

Lec 37 Distance mesaures for clustering

Lec 38 k means clustering

Lec 39 k means clustering using R

Lec 40 Hierarchical clustering

Lec 41 Hierarchical clustering using R

Lec 42 Decision tree classifier

Lec 43 Support vector machines

Lec 44 Higher dimensional data in biology

Lec 45 Principle component analysis

Lec 46 Principle component analysis using R

Lec 47 t SNE

Lec 48 t SNE using R

Lec 49 Diffusion maps

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Data Analysis for Biologists

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