Latest MOOCs on Data Science

As a programmer stepping into the world of data science, I'm following some Massive Open Online Courses (MOOCs) on various provider websites. Here are some I've shortlisted:

Coursera:

The Data Scientist’s Toolbox (Johns Hopkins)

R Programming (Johns Hopkins)

Regression Models (Johns Hopkins)

Statistical Inference (Johns Hopkins)

Getting and Cleaning Data (Johns Hopkins)

Practical Machine Learning (Johns Hopkins)

Exploratory Data Analysis (Johns Hopkins)

* Machine Learning (Stanford)

Computational Methods for Data Analysis (University of Washington)

Edx:

Introduction to Big Data with Apache Spark (BerkeleyX)

Data, Analytics and Learning (UT Arlington)

Data analysis to the MAX() (DelftX)

Foundations of Data Analysis (UTAustinX)

Big Data and Social Physics (MITx)

Udacity:

Data Analyst Nanodegree

Data Visualization and D3.js

Intro to Hadoop and MapReduce

Real-Time Analytics with Apache Storm

Intro to Data Science

In addition to these courses, you can find many more on these websites or even other MOOC providers.

Hope you enjoy.

8 members like this

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by george sos on January 23, 2015 at 7:43am: I ll grab the opportunity to also thank you for the resources and the blog posts,keeping us informed and updated!

I am currently in coursera data science specialisation path ,hopefully to get equipped with knowledge and practice for the new brave world of big (or bigger) data!!...Of course ,it is a never ending curve learning...

But for me as a late life learner ,finding something as interesting as data science ,was rejuvinating!:):)

Thank you again!

Comment by Sione Palu on January 11, 2015 at 8:11pm: The following paper I mentioned in my previous message of using wavelet in data science would be very useful to some readers here:

"Event Detection in Twitter"

http://www.hpl.hp.com/techreports/2011/HPL-2011-98.pdf

Comment by Sione Palu on January 11, 2015 at 5:03pm: Dave Leonard, given your background in Electronic & Electrical Engineering, I think that the learning curve for you at least is minimal in my opinion. The mathematical techniques/algorithms in your area is similar to or overlapped with ones in data-science.

I still keep my textbooks in signal processing & control systems for use today because they are still relevant in emerging new state of the art techniques being applied today in data-science. I'm sure that you've used the followings, which are standard texts in Physics, Electronic & Electrical Engineering courses.

1) "Feedback Control of Dynamic Systems" by Franklin et al.
2) "Digital Signal Processing Using Matlab and Wavelets" by M. Weeks.

I've seen topics as wavelets that's been used in surprise detection today in data-science (whether its topic detection on twitter or anomaly detection in network intrusion, etc,..) which are covered in Book 2, algorithms the same however Book 2 is targeted for engineering & physics, but concepts applications still the same. I've worked with former electrical engineers where their learning curve to data analytic is minimal. I begin by giving them a specific analytical task to work on, then they expand slowly to other areas. The ones that I've worked with make the transition with less hassle since the math is very similar & overlapped.

Comment by Dave Leonard on January 11, 2015 at 4:17am: I am also new to the Data Science world (electrical engineering and management background) and have started the Coursera Data Science Specialization. I've completed 2 of the first courses and am in the "Getting and Cleaning Data" course now. They are good courses with a lot of information, but sometimes feel short (4 weeks each). However, if that's what you need: rapid-fire comprehensive information on data science from professors in the field, than these courses are a great start!

Comment by Baguinebie Bazongo on January 10, 2015 at 10:21pm: Thank you for these important ressources. I would like to let you know that I successfuly achieved 8 of the 10 data science courses from Johns Hopkins. I plan to achieve specialization in May 2015.

Best