Big Data and Data Science has many companies scrambling to bolster their in-house expertise and explore avenues for inferring trends and business opportunities from their existing data mounds. They are all searching for "skilled data gurus" - data scientists, data architects, data visualizers, data engineers... you name it

If you've thought about improving your skills or exploring a new career path, you should try the abundance of free online MOOC through Coursera, Udacity, EdX, Saylor etc. In particular, Johns Hopkins University has started a nine-class specialization in data science which to me has been pretty impressive in their approach so far.
The professors for Johns Hopkins’ data science specialization include: Brian Caffo, professor in the department of biostatistics; Roger Peng, associate professor of biostatistics; and Jeff Leek, assistant professor of biostatistics. The specialization includes 9 four-week courses and a capstone project. I assume capstone project will apparently be somewhat competitive, as they will profile the top ten students on the Simply Statistics blog.

My college elective and project had given me a good fundamentals and background in Artificial Intelligence. Though AI and Data Science are both very closely related, I am looking to broaden my knowledge of data science as a whole.
The specialization uses primarily the R Programming Language . Previously I had done some work with Machine Learning for Spoken Language Detection and Network Intrusion Detection back in college. These were mostly project mashed together with some Python and C++ code respectively. I have done some work in R at work, but overall R is somewhat new to me. R is very different than C++ or Python. R is a not a general purpose programming language rather R is a Domain Specific Language (DSL) for statistical computing . I completed the first 3 courses today. Though I took the first two in parallel, I felt could keep up with my work and the course.
I earned certificates for the first three already and awaiting my certificate for the third.
1. The Data Scientist’s Toolbox
2. R Programming
3. Getting and Cleaning Data

These courses are a pretty decent piece of work. So far I am very impressed with the assignments. We are using data from CSV Files, RDS files, web pages and web service API’s. The assignments are challenging, especially if you have not done a good foothold in R programming. I am glad that this specialization is helping me deepen my understanding of R as a programming language as well as understand hands-on the process behind "Data Science"
All the stuff related to the projects can be found in my Projects section . I will continue sharing my experiences as I take up the rest of the course specialization.



NOTE:
Other universities are also offering data-centric courses on Coursera. Here are a few current and upcoming options:
** Stanford is teaching the principles of algorithm design in its upcoming course, “Algorithms: Design and Analysis, Part 1,” which starts in late April.
** In September, Duke begins “Data Analysis and Statistical Inference,” which will help students learn how to use data to make inferences and conclusions.
** Penn State’s “Maps and the Geospatial Revolution” aims to teach students how to make maps and analyze geographic patterns.