Statistical Learning – 2016

On January 12, 2016, Stanford University professors Trevor Hastie and Rob Tibshirani will offer the 3rd iteration of Statistical Learning, a MOOC which first began in January 2014, and has become quite a popular course among data scientists. It is a great place to learn statistical learning (machine learning) methods using the R programming language. For a quick course on R, check this out – Introduction to R Programming

Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. Slides and video tutorials related to this book by Abass Al Sharif can be downloaded here.

The course covers the following book which is available for free as a PDF copy.

Logistics and Effort:


Rough Outline of Schedule (based on last year’s course offering):

Week 1: Introduction and Overview of Statistical Learning (Chapters 1-2)
Week 2: Linear Regression (Chapter 3)
Week 3: Classification (Chapter 4)
Week 4: Resampling Methods (Chapter 5)
Week 5: Linear Model Selection and Regularization (Chapter 6)
Week 6: Moving Beyond Linearity (Chapter 7)
Week 7: Tree-based Methods (Chapter 8)
Week 8: Support Vector Machines (Chapter 9)
Week 9: Unsupervised Learning (Chapter 10)

Prerequisites: First courses in statistics, linear algebra, and computing.



Skillset Necessary for Data Science

I came across this truly amazing visualization of what it takes to foray into data science by @kzawadz via twitter

data science

Statistics: The Sexiest Job of the Decade

Anyone who’s got a formal education in economics knows who Hal Varian is. He’s most popularly known for his book Intermediate Economics. He’s also the Chief Economist at Google. He is known to have famously stated more or less, that statisticians and data analysts would be the sexiest jobs of the next decade.

That has come true, to a great extent, and we’ll be seeing more.

Great places to learn more about data science and statistical learning:
1] Statistical Learning (Stanford)
2] The Analytics Edge (MIT)

In a paper called ‘Big Data: New Tricks for Econometrics‘, Varian goes on to say that:

In fact, my standard advice to graduate students these days is “go to the computer science department and take a class in machine learning.” There have been very fruitful collaborations between computer scientists and statisticians in the last decade or so, and I expect collaborations between computer scientists and econometricians will also be productive in the future.

See Also: Slides on Machine Learning and Econometrics