Coming from HN, this is one of the great things about Coursera courses for me. But it seems to freak out some students when the bar for top of the class is set by people with extensive professional backgrounds. [1] What can make for a corrosive environment is that there is often someone who stokes their ego by talking about how easy the course is in the forums...though this is rarely anyone at Jeff Heaton's level [or an HN'er].
[1] I took an introductory programming course with an HNer who had been on a c++ technical committee. But I am used to looking around the table and not seeing the dimmest bulb in the chandelier.
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.
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.
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.
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.
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...
Whether or not the candidate was part of a MOOC won't directly factor into the technical evaluation though, irrespective of whether the courses completed were relevant technically or not.
1) Whether we like the person (i.e., can we get along with him for months, years, etc.)
2) Is the person honest?
3) Was this person recommended, and do your views align with the recommender's?
(It's an honest question, since that's my impression of "data science" as a statistician)
As an open source maintainer I can say that “question asking etiquette” is NOT common knowledge.
I don't have any problem with having that as a credit earning question. But I hated it when it carried 20-25% of the credit in one of the quizzes. This course could have asked more questions in the quiz to test its students more.
I feel that Data Scienctist Toolkit was a slacker course, it could have covered more topics or tested the students more. Five questions in each quiz and four such quizzes and you are done.
These courses are morale boosters, you can earn a certificate in four weeks and that keeps some motivated.
I like the concept of five late days for the whole course, lets you stay on track despite one's busy schedule.
It depends on a person's background. There are people like Jeff Heaton who have years of professional experience in Data Science taking an introductory data science course, and there are invariably people taking machine learning with no programming experience. But in the sweet spot for any class there will be people who are really stretched. People who will spend twenty or thirty hours on a programming assignment that some people complete in one.
I enrolled in the last iteration of Van Hentenreck's Discreet Optimization. A great course and his enthusiasm is infectious. I learned a lot. Saw where I needed to go. But there was no chance I was going to pass. I just don't have the chops...yet, hopefully.
One of the things that makes Discreet Optimization a great MOOC course is that it can be approached at different levels. A student can attack the problems using dedicated optimization libraries. If that's not enough of a challenge, they can write their own algorithms. And if that's not enough, they can prove optimality for each of their solutions.
And like every Coursera computing course there are people who can do all of it. And most who cannot.