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- UIUC Data Mining Specialization
It includes 5 courses:
- Computing for Data Analysis (with R)
YouTube playlists for the videos of the course: week 1; week 2; week 3 and week 4. - Data Analysis (with R)
YouTube playlists for the videos of the course: http://bit.ly/16PPtuI
- Machine Learning, by Andrew Ng, Stanford University
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
- Free Stanford online course on Statistical Learning (with R)
Covers linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines.
- Social Network Analysis
- Introduction to Data Science
The lectures in week 3 give an excellent introduction to MapReduce and Hadoop, and demonstrate with examples how to use MapReduce to do various tasks. - Statistical Aspects of Data Mining with R
Five-hour lecture videos on YouTube
- Natural Language Processing
A course at Stanford University, containing 18 videos, with each of over 1 hour length
- A Sequence of 9 Courses on Data Science on Coursera
The courses, lectured by(Associate/Assistant) Professors of Johns Hopkins University, are designed for students to learn to become Data Scientists and apply their skills in a capstone project. Course 1: The Data Scientist’s Toolbox Course 2: R Programming Course 3: Getting and Cleaning Data Course 4: Exploratory Data Analysis Course 5: Reproducible Research Course 6: Statistical Inference Course 7: Regression Models Course 8: Practical Machine Learning Course 9: Developing Data Products
- Stanford online course: Mining Massive Datasets
Instructors: Jure Leskovec, Anand Rajaraman and Jeff Ullman, Stanford University
- Statistics and R for the Life Sciences
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences
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