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Data Analysis for Biologists

Track :

Computer Science

Lessons no : 50

For Free Certificate After Complete The Course

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What will you learn in this course?
  • Master biological data analysis techniques using R and Python for research and practical applications
  • Apply statistical methods like ANOVA, regression, and hypothesis testing to biological datasets effectively
  • Interpret biological data visualizations such as heatmaps, scatter plots, and boxplots for insightful research conclusions
  • Utilize bioinformatics tools and software to analyze genetic, genomic, and proteomic data accurately
  • Design experiments and analyze biological data to support scientific hypotheses and discoveries
  • Implement data cleaning, preprocessing, and normalization techniques for biological datasets
  • Develop skills to manage large biological datasets using database management and data wrangling tools
  • Apply machine learning algorithms to classify and predict biological phenomena and patterns
  • Evaluate the reliability and validity of biological data analysis results critically
  • Communicate biological data insights clearly through reports, presentations, and visualizations
  • Integrate biological data analysis techniques into research workflows for enhanced scientific productivity
  • Stay updated with emerging data analysis tools and methods in biological research and bioinformatics

How to Get The Certificate

  • You must have an account Register
  • Watch All Lessons
  • Watch at least 50% of Lesson Duration
  • you can follow your course progress From Your Profile
  • You can Register With Any Course For Free
  • The Certificate is free !
Lessons | 50
Data Analysis for Biologists Intro Video
00:04:19

Data Analysis for Biologists Intro Video
Lec 1 Rules of probability
00:14:05

Lec 1 Rules of probability
Lec 2 Discrete probability distribution
00:28:50

Lec 2 Discrete probability distribution
Lec 3 Continuous probability distribution
00:39:27

Lec 3 Continuous probability distribution
Lec 4 Moments mean and variance
00:31:40

Lec 4 Moments mean and variance
Lec 5 Moments variance and covariance
00:43:01

Lec 5 Moments variance and covariance
Lec 6 Bayes theorem and likelihood
00:39:49

Lec 6 Bayes theorem and likelihood
Lec 7 Concept of statistical tests
00:35:47

Lec 7 Concept of statistical tests
Lec 8 Vector and vector operations
00:33:53

Lec 8 Vector and vector operations
Lec 9 Matrix and matrix operations
00:27:48

Lec 9 Matrix and matrix operations
Lec 10 Determinant and Inverse of a matrix
00:22:28

Lec 10 Determinant and Inverse of a matrix
Lec 11 Eigenvalue and eigenvector
00:17:28

Lec 11 Eigenvalue and eigenvector
Lec 12 Linear system of equations
00:28:54

Lec 12 Linear system of equations
Lec 13 Singular value decomposition
00:37:12

Lec 13 Singular value decomposition
Lec 14 Getting ready with R
00:31:24

Lec 14 Getting ready with R
Lec 15 Algebraic and logical operations in R
00:22:09

Lec 15 Algebraic and logical operations in R
Lec 16 Reading and writing data
00:41:54

Lec 16 Reading and writing data
Lec 17 Statistics using R – descriptive statistics
00:24:13

Lec 17 Statistics using R – descriptive statistics
Lec 18 Statistics using R – t test and ANOVA
00:24:43

Lec 18 Statistics using R – t test and ANOVA
Lec 19 Linear algebra using R
00:47:44

Lec 19 Linear algebra using R
Lec 20 Scatter plot Line plot Bar plot
00:33:11

Lec 20 Scatter plot Line plot Bar plot
Lec 21 Histogram Box plot
00:37:38

Lec 21 Histogram Box plot
Lec 22 Heatmap and Volcano plot
00:44:51

Lec 22 Heatmap and Volcano plot
Lec 23 Network visualization
00:37:02

Lec 23 Network visualization
Lec 24 Data visualization using ggplot2 I
00:38:34

Lec 24 Data visualization using ggplot2 I
Lec 25 Data visualization using ggplo2 II
00:26:08

Lec 25 Data visualization using ggplo2 II
Lec 26 Correlations
00:44:19

Lec 26 Correlations
Lec 27 Linear regression I
00:25:01

Lec 27 Linear regression I
Lec 28 Linear regression II
00:29:28

Lec 28 Linear regression II
Lec 29 Linear regression using R
00:25:17

Lec 29 Linear regression using R
Lec 30 Multiple linear regression
00:49:09

Lec 30 Multiple linear regression
Lec 31 Multiple linear regression using R
00:17:58

Lec 31 Multiple linear regression using R
Lec 32 Nonlinear regression
00:27:24

Lec 32 Nonlinear regression
Lec 33 Nonlinear regression using R
00:27:44

Lec 33 Nonlinear regression using R
Lec 34 Clustering and classification
00:30:04

Lec 34 Clustering and classification
Lec 35 Logistic regression
00:35:19

Lec 35 Logistic regression
Lec 36 Logistic regression using R
00:32:08

Lec 36 Logistic regression using R
Lec 37 Distance mesaures for clustering
00:23:45

Lec 37 Distance mesaures for clustering
Lec 38 k means clustering
00:19:58

Lec 38 k means clustering
Lec 39 k means clustering using R
00:26:17

Lec 39 k means clustering using R
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Fatemeh Bayat 

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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.