Data Science

Coursera Data Science Specialization (John Hopkins Universitu)
Successfully completing the inaugural capstone for the JHU/Coursera data science track was a thrill for me. The timing for the first track couldn't have been better, as at the time I was using MOOC's to shore up what I was learning in parallel for Master of Science in Predictive Analytics at Northwestern University. The NU degree was rigorous and heavy on statistics and analytical management theory, but I wanted more practical hands on training to reinforce what I was learning. The JHU track was perfect for that. The first course (Data Science Toolbox) in the sequence is a little light, but sets the stage for combining data science learnings with practical state of the art tools that I have come to use on a daily basis. Some of the more advanced courses, like Reproducible Research and Developing Data Products weren't on my radar initially but ended up providing me with new skills that I have turned to again and again. I can't emphasize enough how fantastic both of those courses are. My main criticism (and the loss of the star) is for the Statistical Inference and Regression Models classes -- it's just not realistic to expect students to learn these topics in 4 weeks. I got by, since those were exactly the areas my NU coursework focused on, so Coursera acted as a certification tool, rather than a teaching medium in that case. I expect students with less background in those areas to be really frustrated with the pace, and although they may pass would be dangerously lacking in those very important areas.
The Capstone though was a fantastic experience. The pace was frenetic and a HUGE step up from the other courses, but it was a lot of fun. Ours was based around text analytics and introduced me to a new area. By the time the students had whittled down do less than 500 qualified to take the capstone (form over 2 million!!) I found the cohort to be a really talented group of people that paralleled my NU degree (can't say that for any other MOOC enrollment I've been a part of). I'm not sure if they are still using the same capstone (they did for the second offering of the capstone), which would be a shame, because breaking new ground (versus trolling github for existing code) was part of the glory of that project.
In short, I highly recommend this track, and commend all 3 of the instructors for putting together a brilliant program. I was able to power through from April to December because I was already studying those topics in parallel, but I'd recommend others not in the same situation, or already familiar with the material to take it slowly through the tougher classes to let the learnings sink in.

Great for folks new to computer science, data science, coding!
The courses are great. There's videos and resources for learning, quizzes to make sure you're retaining information, and at least one assignment demonstrating your learning per course. The courses are about a month long with deadlines for respective components throughout. That said, you can work ahead and finish the course as quickly as you like or are able (which is nice!).

I was also continuously impressed by the peer-to-peer support. So many people are willing to help one another and work collaboratively towards a solution when faced with an issue. I was surprised to have more discussion board activity, support, and productivity here than I've had in more formal, expensive, for-credit courses. Shout out to helpful peers and the TAs!

This was a great introduction to the topic and allowed me to quickly develop various skill sets. It was also enough information and resources to make me want to learn, play around, and spend more time on the various topics. That's on me to do, though. One of the other great things this program teaches you is to seek out information from various sources or places, as well as empowering you to make your own resource.

If you're already proficient with coding, this may be slow or extremely easy for you. More than half my battle was making concepts work with R code and/or using command prompt to push, pull, and post data to various places. Again, though, you can work ahead through the courses, so you can work through things you already are familiar with and/or get through the content even faster.

An entry course that opens up your data science career
I strongly recommend this course to anyone who has taken statistics/economics/data analysis courses at college and would like to get some training in big-data analysis. My training background is Economics which contains substantial data-analysis training with economic data. I would say I benefit substantially from the machine learning course in this track. I realize that the econometric models such as linear regression/binary-choice models are only part of the machine learning techniques, while it comes to analyzing big datasets, other methods such as decision tree and SVM models are commonly used as well. This specialization track also helps to brush up my data analyzing skills and applications to a broader, larger dataset. Eventually, it helps me transit my career from an economics researcher to a data scientist in a bay area tech company.

Good intro to data science for the well prepared
Suppose you:
- have programming experience (preferably C, C++, C# or Java )
- have a solid knowledge of the basics of statistics (descriptive and inferential).
then this specialization is a great introduction to data science.

The specialization "teaches" statistics from scratch, but only those with an IQ over 130 and who are very determined have a remote chance of finishing these modules; for the rest of us these modules are impossible to pass -unless you already know the stuff they are trying to teach-.
The programming in R is imho very good, but fast paced and probably hard for those without programming experience.

The really good parts are:
1) the professors are experienced and practicing data scientist, they let you look over their shoulder and show you how the pro's do it.
2) the course covers the complete data science pipeline: from analyzing the problem, getting and cleaning the data to presenting the results.
3) there are engaging assignments and projects.
4) an emphasis on "how to do it" skills (not on theory)

You might want to do one or two modules in stead of the whole specialization.
1) programming in R is very good (for the well prepared)
2) reproducible research is an valuable and unique module
3) the same for developing data product

Great introductory Data Science course
This course covers all the major aspects of the Data Science field. The instructors are reasonably good. However, some of the statistics aspects will be easier if you have some basic knowledge. (I took a basic stats course before this series to refresh my memory.) The exercises and projects are both pretty easy. The grading system makes it easy to achieve high scores. If you really want to learn more, then I would recommend going beyond what are mentioned as minimum requirements for the projects. The Capstone project was also very interesting and relevant to current industry trends.

I'm not sure if this course alone will get you ready for a job in Data Science. You may need some prior background or some further work (in terms of projects) to land a job.