You can find all kinds of courses from the best universities in the world, most of them for free, from culinary to bio-technology. Today, the three most important MOOC sites/platforms are:
Coursera (http://www.coursera.org)
Main Partners: Stanford University, University of Washington, Johns Hopkins University, Ivys, Duke, California Institute of Technology, and much others.
edX (http://www.edx.org)
Main Partners: M.I.T., Harvard; University of California at Berkeley, University of Texas and much others.
Udacity (http://www.udacity.com)
Main Partners: Professional training from companies and also some universities
Here is great list of courses for you to start:
- Stanford - Machine Learning
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. - Johns Hopkins - Data Science Specialization - 9 courses
- Course 1: The Data Scientist’s Toolbox
Get an overview of the data, questions, and tools that data analysts and data scientists work with. Upon completion of this course you will be able to identify and classify data science problems. You will also have created your Github account, created your first repository, and pushed your first markdown file to your account. - Course 2: R Programming
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. Topics in statistical data analysis will provide working examples. - Course 3: Getting and Cleaning Data
Learn how to gather and clean data from a variety of sources. Upon completion of this course you will be able to obtain data from a variety of sources. You will know the principles of tidy data and data sharing. Finally, you will understand and be able to apply the basic tools for data cleaning and manipulation. - Course 4: Exploratory Data Analysis
Learn the essential exploratory techniques for summarizing data. After successfully completing this course you will be able to make visual representations of data using the base, lattice, and ggplot2 plotting systems in R, apply basic principles of data graphics to create rich analytic graphics from different types of datasets, construct exploratory summaries of data in support of a specific question, and create visualizations of multidimensional data using exploratory multivariate statistical techniques. - Course 5: Reproducible Research
Learn the concepts and tools behind reporting modern data analyses in a reproducible manner. In this course you will learn to write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize a data analysis so that it is reproducible and accessible to others. - Course 6: Statistical Inference
Learn how to draw conclusions about populations or scientific truths from data. In this class students will learn the fundamentals of statistical inference. Students will receive a broad overview of the goals, assumptions and modes of performing statistical inference. Students will be able to perform inferential tasks in highly targeted settings and will be able to use the skills developed as a roadmap for more complex inferential challenges. - Course 7: Regression Models
In this course students will learn how to fit regression models, how to interpret coefficients, how to investigate residuals and variability. Students will further learn special cases of regression models including use of dummy variables and multivariable adjustment. Extensions to generalized linear models, especially considering Poisson and logistic regression will be reviewed. - Course 8: Practical Machine Learning
Upon completion of this course you will understand the components of a machine learning algorithm. You will also know how to apply multiple basic machine learning tools. You will also learn to apply these tools to build and evaluate predictors on real data. - Course 9: Developing Data Products
Students will learn how communicate using statistics and statistical products. Emphasis will be paid to communicating uncertainty in statistical results. Students will learn how to create simple Shiny web applications and R packages for their data products. - Lausane - Functional Programming Principles in Scala
Learn about functional programming, and how it can be effectively combined with object-oriented programming. Gain practice in writing clean functional code, using the Scala programming language. - Berkeley - Introduction to Statistics: Descriptive Statistics
An introduction to descriptive statistics, emphasizing critical thinking and clear communication. - Berkeley - Artificial Intelligence
CS188.1x focuses on Behavior from Computation. It will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision–theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in stochastic and in adversarial settings. - NVidia - Intro to Parallel Programming
Using CUDA to Harness the Power of GPUs. Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment by coding a series of image processing algorithms. - University of Washington - Introduction to Data Science
Join the data revolution. Companies are searching for data scientists. This specialized field demands multiple skills not easy to obtain through conventional curricula. Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. - University of Washington - Computational Methods for Data Analysis
Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. - Indian Institute of Technology Delhi - Web Intelligence and Big Data
This course is about building 'web-intelligence' applications exploiting big data sources arising social media, mobile devices and sensors, using new big-data platforms based on the 'map-reduce' parallel programming paradigm. In the past, this course has been offered at the Indian Institute of Technology Delhi as well as the Indraprastha Institute of Information Technology Delhi. - University of Toronto - Statistics: Making Sense of Data
This course is an introduction to the key ideas and principles of the collection, display, and analysis of data to guide you in making valid and appropriate conclusions about the world. - University of Toronto - Neural Networks for Machine Learning
Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. - University of Michigan - Social Network Analysis
This course will use social network analysis, both its theory and computational tools, to make sense of the social and information networks that have been fueled and rendered accessible by the internet. - Johns Hopkins University - Computing for Data Analysis
This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical metho - Johns Hopkins University - Data Analysis
Learn about the most effective data analysis methods to solve problems and achieve insight. - Rice University - An Introduction to Interactive Programming in Python
This course is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids. - Duke University - Data Analysis and Statistical Inference
This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena. - University of Minnesota - Introduction to Recommender Systems
This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!
sources:
http://www.skilledup.com/blog/the-best-mooc-provider-a-review-of-coursera-udacity-and-edx/
http://www.nytimes.com/2012/11/04/education/edlife/the-big-three-mooc-providers.html?_r=0
http://www.r-bloggers.com/a-sequence-of-9-courses-on-data-science-starts-on-coursera-on-2-june-and-7-july-2014/
http://www.skilledup.com/blog/the-best-mooc-provider-a-review-of-coursera-udacity-and-edx/
http://www.nytimes.com/2012/11/04/education/edlife/the-big-three-mooc-providers.html?_r=0
http://www.r-bloggers.com/a-sequence-of-9-courses-on-data-science-starts-on-coursera-on-2-june-and-7-july-2014/
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