[rmb177]

I completed the first two courses in the first four-week block and am currently taking the third course. Mr. Heaton's review provides a really good summary of what to expect.

Overall, I'm also happy with the course. I was expecting a little more degree of difficulty and a little higher workload than what I've run into so far. If you're an experienced developer with a Github account, the first course can be completed in a couple of hours. The R Programming course was more along the lines of what I was expecting. So far, the third class is closer to the first than the second (in terms of difficulty...does require a bit more time).

Going into the course, I wasn't expecting to come out a "data scientist" ready to land a full-time job in the field. My experience so far confirms that expectation. But it's a fun course, a good way to get started in R, and a good way to spring-board your exploration into the field. It's nice to have deadlines as a motivation to keep on track and stay on a track for learning. I'm hoping by the end of the curriculum I feel confident enough to try and land some small free-lance projects.

I'm paying for the "official" certification. I'm not sure if it's really worth it, but at $50/class it's not putting a big dent in my finances.

[galuvian]

Same here. The first three were a good start. It was a little annoying that there is zero feedback on the peer assessments other than the grade. The third class starts off slow but by he last week is at about the same level as the R Programming course. My biggest complaint ( which applies to all of these courses) is that the lectures are just narrated PowerPoint presentations. Are all moocs like that? I'm not expecting polished videos at the same level as Kahn Academy but these lectures are just a small step up from fading the slides myself.

Now I'm taking the next three. They are a good continuation that picks up where the first three left off. I was looking forward to the Statstical Inference class. It has been almost 10 years since I took intro to stats in college. For someone without any stats background this course will really step up the difficulty. I was even more disappointed with the lectures on the stats cours. The yellow highlighting as he reads each line on the slide is extremely distracting. But the content is exactly what I was hoping for.

I've done a little hacking with R for data heavy analysis at work when excel couldn't handle the data. I'm really glad to be taking advantage of this opportunity to get more experience with it in these course. My day job is implementing the 'production' side of this kind of data processing with java and hadoop in the healthcare space. Hopefully this specialization will help me better communicate with our clinical/science teams.

[craigching]

You summed up pretty well how I feel about the first three to four (I've taken the first three and am now taking "Exploratory Data Analysis").

The ease with which I'm going through it is due to my 20+ years of programming in multiple languages, so I've pretty much seen it all. I'm also assuming that these classes are intended for more statistics oriented people who may not have a ton of programming experience. Maybe both of course with the later classes appearing to be a little more stats oriented.

It is easy for me, but I am learning a lot too, not sure I would have dedicated myself to learning R if I didn't have these classes or if I didn't have something in my job that needed it.

The projects are actually fun if you allow yourself enough time to make them into something more than they are. The "Getting And Cleaning Data" class had a little bit of leeway in the project for doing things your way as long as you documented why you did them.

Anyway, I took it signature track as well and am having fun, so it's definitely worth it to me. I'm really looking forward to the "Statistical Inference" and "Practical Machine Learning" courses to help spring me into more AI and Machine Learning topics.

[sten]

I find it amazing how many complaints there are on the official forums form these classes. I'm not sure what people were expecting. But equivalent material is available online for people with 2000-4000 dollar price tags.

These classes will always amount to what people can make of them. My expectation from this isn't to become a data scientist, it's simply to improve from where I current am. After I've taken a few more of these I'll try some Kaggle.

My only issue so far has been the quality of lectures in the Inference Class, but even then it's likely worth the time and money invested.

[sanderjd]

My interest was piqued by this article, but upon initial investigation, I'm having trouble stomaching putting $50 toward learning how to use github... Any reason I shouldn't just skip ahead?

[rmb177]

The $50 is an optional fee for the Signature Track. If you pay for this track, Coursera adds some extra checks to validate your identity and issues you a "Verified" certificate.

You can still take all of the courses for free and get a certificate, but Coursera won't validate that you actually did the coursework.

I'm guessing you could pick and choose to pay for only certain classes, but you have to pay for all of them to earn the overall "Specialization" certificate.

See the following url for details: https://www.coursera.org/specialization/jhudatascience/1?utm...