Data Visualization with ggplot2 in R
ggplot2 is the most popular package for data visualization. It is based on Grammar of Graphics which makes visualization extremely easy.
Linear Regression Machine Learning - Linear Regression in R and Python
This “Linear Regression” Tutorial by Great Learning would help you to comprehensively learn all the underlying concepts of Linear Regression. You will also learn how to implement linear regression in both R language and Python language
CBW's Introduction to R Workshop - June 21-22, 2021
Learn R programming essentials: scripting, statistical tests, loops, functions, linear models, and R Markdown in this comprehensive workshop for beginners.
Introduction to R 2018
Learn the fundamentals of R programming for data analysis and visualization through hands-on sessions covering key concepts and practical applications.
Flow Cytometry Data Analysis Using R 2013
Learn to analyze flow cytometry data using R, covering exploration, processing, quality assurance, static gating, and advanced clustering techniques for comprehensive data interpretation.
Data Visualization
Learn to create volcano plots and heatmaps in R for bioinformatics analysis. Reproduce Galaxy tutorials and master essential data visualization techniques for beginners.
Data Visualization in R
Learn to create impactful volcano plots and heatmaps for bioinformatics using R, with step-by-step guidance on reproducing Galaxy tutorials and utilizing the pheatmap package.
Gene Expression Analysis
Learn essential techniques for analyzing gene expression data, including RNA-Seq pipelines, volcano plots, heatmaps, and RNAseq data analysis using R and Python.
RNA-Seq Analysis
Learn step-by-step RNA-Seq data analysis, including creating Volcano Plots in R. Ideal for bioinformatics beginners, this tutorial reproduces Galaxy workflows and provides hands-on experience.
ANOVA
Comprehensive introduction to Analysis of Variance (ANOVA), covering key concepts, types, and applications in statistical analysis. Includes practical examples and comparisons with other statistical tests.
Multivariate Time Series Forecasting in R - Data Analytics with R - Data Science
Learn multivariate time series forecasting in R, covering fundamentals, modeling techniques, feature engineering, and practical implementation through a hands-on demo.
Data Science for Beginners - Learn Data Science in 9 Hours
This tutorial on "Data Science for Beginners" would help you to comprehensively master all the concepts of Data Science.
R Programming for Beginners - R Programming for Data Science
Comprehensive introduction to R programming, covering basics to advanced topics like data manipulation and visualization. Includes hands-on practice with a Pokemon dataset case study.
Neural Network Tutorial - Artificial Neural Network and Backpropagation in Neural Networks
In this 'Neural Network' Tutorial, you will comprehensively learn about all the concepts of Artificial neural networks and the importance of Back-propagation in Neural Networks.
Linear Regression Full Course
This “Linear Regression” Tutorial by Great Learning would help you to comprehensively learn all the underlying concepts of Linear Regression.
Classification with Decision Trees
Learn to implement classification using decision trees in R, covering key concepts like Gini index, entropy, and impurity measurement. Explore advantages, case studies, and various algorithms for effective machine learning applications.
Using R
Learn advanced statistical techniques and data analysis using R, covering distributions, simulations, estimations, and various statistical tests through practical examples and hands-on coding.
Statistical Theory
Explore advanced statistical concepts, proofs, and applications using R, covering topics from probability distributions to matrix properties and confidence intervals.
Survival Analyses
Learn to generate censored exponential data, derive density and likelihood for censored data, and understand the Kaplan-Meier estimator as a maximum likelihood estimator in R.
Maximum Likelihood Estimation
Comprehensive exploration of Maximum Likelihood Estimation across various probability distributions, including practical applications using R for data generation and parameter estimation.
Approximations
Explore statistical approximations, including binomial, Poisson, and normal distributions. Learn delta method, confidence intervals, and moment estimation techniques for various probability distributions.