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:

statLearnEffort

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.

 

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Which Programming Languages Get Used Most At Hackathons?

For a quick peek into the list:

The Top 10 Languages At Devpost’s Hackathons:

  1. HTML/CSS (see note below)
  2. JavaScript
  3. Python
  4. Java
  5. C/C++
  6. PHP
  7. Objective-C
  8. C#
  9. Swift
  10. JSON (which isn’t … really a programming language, but is on their list for some reason, so I’m including #11 too)
  11. Ruby

Read the full Techcrunch article to know why.

In stark contrast:

The Top 10 Languages according to IEEE Spectrum’s 2015 Rankings:

  1. Java
  2. C
  3. C++
  4. Python
  5. C#
  6. R
  7. PHP
  8. JavaScript
  9. Ruby
  10. Matlab

Note: HTML isn’t quite a “programming” language — it’s a markup language, meaning it’s a means of laying out the elements of a document. But it’s a “language” none the less, and one that pretty much every web developer taps endlessly, so we’ll let the semantic stuff slide

R — The Big Mover in IEEE Spectrum’s 2015 Rankings for Top 10 Programming Languages

The column on the left is the 2015 ranking; the column on the right is the 2014 ranking for comparison:

top-tech-rankings

source: The 2015 Top Ten Programming Languages

The thing to note is that the top 5 languages haven’t budged from their positions. R has pushed past PHP, JavaScirpt and Ruby, which have maintained their relative positions.  So this year’s rankings have been about R moving forward.

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 MarketingDistillery.com

data science

How to become a programmer, or the art of Googling well

This should serve as encouragement to all and sundry. Stop wasting time getting intimidated and learn to code like a pro.

Featured Image: http://xkcd.com/979/

okepi

*Note: Please read all italicized technical words as if they were in a foreign language.

The fall semester of my senior year, I was having some serious self-confidence issues. I had slowly come to realize that I did not, in fact, want to become a researcher. Statistics pained me, and the seemingly endless and fruitless nature of research bored me. I was someone who was driven by results – tangible products with deadlines that, upon completion, had a binary state: success, or failure. Going into my senior year, this revelation was followed by another. All of my skills thus far had been cultivated for research. If I wasn’t going into research, I had… nothing.

At a liberal arts college, being a computer science major does not mean you are a “hacker”. It can mean something as simple as, you were shopping around different departments, saw a command line for the…

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