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Online Courses
There are many great online learning platforms that provide various elementary and advanced courses of R/Data science/R-packages. Most of those courses is free, but you can also choose some charged courses for specific needs or pay for a verified certificate of the courses.
edx online courses
edX is a free online course project jointly created by Harvard University and MIT. The purpose is to establish a joint education platform for the world’s top universities, improve the quality of teaching, and promote online education online. At present, it has more than 900,000 registrants. The edX has added 15 world-renowned universities including 6 Asian universities including Tsinghua University, Peking University, Hong Kong University, Hong Kong University of Science and Technology, Kyoto University in Japan and Seoul National University of Korea.
R Programming courses:
Programming in R for Data Science: Learn the fundamentals of programming with R, from reading and writing data to customizing visualizations and performing predictive analysis.
Statistical Analysis in Bioinformatics: Learn basic R programming to analyze biological big data to locate genes, perform simulations, and gauge the effect of specific markers.
Introduction to Linear Models and Matrix Algebra: Learn to use R programming to apply linear models to analyze data in life sciences.
Explore Statistics with R: Learn basic statistics in a practical, experimental way, through statistical programming with R, using examples from the health sciences.
Case Studies in Functional Genomics: Explore data analysis of several experimental protocols, using open source software, including R and Bioconductor.
Data Science related courses:
Statistics and R: An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
Data Science: R Basics: Build a foundation in R and learn how to wrangle, analyze, and visualize data. This course covers common programming commands, how to operate on vectors, and when to use advanced functions such as sorting.
Statistical Inference and Modeling for High-throughput Experiments: A focus on the techniques commonly used to perform statistical inference on high throughput data.
High-Dimensional Data Analysis: A focus on several techniques that are widely used in the analysis of high-dimensional data.
Data Science Essentials: Explore data visualization and exploration concepts with experts from MIT and Microsoft, and get an introduction to machine learning.
Mechine Learning courses:
Principles of Machine Learning: Get hands-on experience building and deriving insights from machine learning models using R, Python, and Azure Machine Learning.
Machine Learning Fundamentals: Understand machine learning’s role in data-driven modeling, prediction, and decision-making.
Machine Learning: Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.
Data Science: Machine Learning: Learn the basics of machine learning, the science behind the most popular and successful data science techniques, to build a movie recommendation system.
Coursera online courses
Coursera was founded in 2012 by Stanford University computer science professors Andrew Ng and Daphne Koller. Ng and Koller were inspired by their experiences offering their Stanford courses online in fall 2011, and soon after left Stanford to launch Coursera. Princeton, Stanford, the University of Michigan and the University of Pennsylvania were the first universities to offer content on the platform. Offerings have since expanded to include Specializations – collections of courses that build skills in a specific subject – as well as degrees and a workforce development product for businesses and government organizations.
Coursera works with universities and other organizations to offer online courses, specializations, and degrees in a variety of subjects, such as engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and others.
R Programming courses:
R Programming: In this course you will learn how to program in R and how to use R for effective data analysis. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
The R Programming Environment: This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting.
Advanced R Programming: This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools.
Building R Packages: Covering R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub.
Data Science related courses:
Statistics for Genomic Data Science: An introduction to the statistics behind the most popular genomic data science projects.
Bioconductor for Genomic Data Science: Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.
Genomic Data Science with Galaxy: Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization.
Mechine Learning courses:
Mathematics for Machine Learning: PCA: This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique.
Mathematics for Machine Learning: Linear Algebra: In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices.
Mathematics for Machine Learning: Multivariate Calculus: This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques.
Bayesian Methods for Machine Learning: Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets.