Hacker News Comments on
Johns Hopkins University
Hacker News Stories and CommentsAll the comments and stories posted to Hacker News that reference this url.
Originally Coursera only wanted money for the - for the vast majority of people useless - verified completion certificate. You had access to all course content including all the tests and could access course content long after the course had ended. So if you did not see any value in that "verified certificate" there was no reason to pay anything. You got a free certificate either way.
I saved all certificates I ever got from edX and from Coursera as PDFs to remember which courses I took. They actually look quite fancy.
- Example certificate that was free at the time: https://i.imgur.com/XFX05gx.png
- The course was part of a series, which these days is available here: https://www.coursera.org/specializations/jhu-data-science#co...
- Here is an R-Markdown document I created for another of the courses in that series, which used peer assessment where we had to evaluate each others results: https://rpubs.com/Noseshine/74191
At the start everything was free, including all these exercises, all the assessments, and even the certificates. I knew it would not last and used the opportunity, over three years of heavy course taking, over 50 completed courses. I did not have much to spend at the time, I could definitely not have spend the current amounts.
I took over a dozen courses on Coursera alone, medicine and statistics, it was good. I just checked my (long unused) login just now, they only list two courses under completed and "forgot" the other well over a dozen others. Good thing I saved those completion certificates, although there probably is little use in remembering what courses I took - either I remember what I learned or I don't.
Just for fun, this was one of my favorite courses, great professor too, great content: https://www.coursera.org/learn/medical-neuroscience Don't know if it still is as complete, at the time it was almost 25 hours of videos alone, never mind all the reading and all the tests and exercises. It wasn't complicated though, you just had to invest the time but not nearly as much brain as for other "STEM sciency" courses.
I like Johns Hopkins Data Science Specialization on coursera https://www.coursera.org/specializations/jhu-data-science
I've taken the first 3 classes in this specialization and would say it's been invaluable. There's an associated textbook available online for free at:
I've taken the first 6 classes in this specialization as well, and have found them to be pretty valuable. These, so far, have been more about the mechanics of programming in R, and less about the math. The Duke one above is more math, less R. But both are an intermingling of both mathematical concepts and R coding. I find that these two tracks complement each other very well.
A set of videos on Statistics from "Professor Leonard". This is just recordings of all the lectures from a standard college Stats 101 class. But the guy is a good lecturer, explains things well, and has a sense of humor which keeps things interesting.
He also has videos on other topics as well, if you're interested.
I believe Kahn Academy also has a section on Statistics and Probability.
You might also find some of the stuff linked here useful:
Here is the path I'm following:
I'm doing the Data Science Specialization from Johns Hopkins University / Coursera, with verified certificates that I hope will help me create a portfolio to showcase as I look at this type of work.
I feel like having a portfolio to point to, as well as code on a site like GitHub, should be a good basis for a conversation with a potential employer.
https://www.coursera.org/specializations/jhu-data-science If you wish to go deep and learn R as well as data science and machine learning fundamentals, then this is a great specialization course on Coursera.
I've completed a bunch of Coursera courses. Quality really varies. Even within the 9 course Data Science specialization  track some courses were rather poor while the rest were very good. I'm currently taking the #5 rated course . It is excellent. But I'm only taking it because the Statisical Inference course in the Data Science specialization was so weak.
I would also recommend the Cryptography 1 course by Dan Boneh on Coursera . Excellent if you are at all interested in the subject.
I always download the lecture videos, slides, quizzes, labs and exams because, as mentioned, many of the courses don't allow access once the class is completed.
You definitely have to have plenty of self discipline to complete MOOCs. And I don't have any delusions about a Coursera certificate being useful in landing a job; that's not what I'm after. I'm building the skills I want to apply to my own projects.
⬐ sawwithttps://www.coursetalk.com has quite good reviews, especially on the more popular courses.
I think that's a pretty good definition. To define it for myself I took the Data Science Specialization from Coursera https://www.coursera.org/specializations/jhudatascience. There's a fair amount of stats and programming involved.
I personally don't like that Coursera dropped free certificates of accomplishments for many courses. While this might not be such a problem in the US, I (living in Germany) see this as a serious problem in countries as Germany, Austria or Brazil, where it is essential that you have some certificate to prove that you really took the course.
What I'm particularly angry about is that in former days you could get a free certificate of accomplishment for the courses from the Data Science specification
A few weeks ago Coursera changed the policy even for these existing courses. That's why I completely lost any trust that I had in Coursera and will actively avoid taking courses from Coursera (and instead look what edX has to offer).
⬐ gavazzyDo employers look at certificates at all?⬐ alistairSHAs I review resumes, it's just another data point, along with degree, previous jobs, etc. They aren't required, or even preferred. But, they do display an interest in learning and progressing professionally, which is always good.⬐ kagenekoMy current employer is requiring everyone to get Security+ certifications because our clients are starting to require that.
edit: oh, certificate vs certification. I don't know of anyone who cares about the Coursera certificates, but it seems certifications are still in vogue.
Some certs mean nothing, but some of the MOOCs do apparently earn you some credence.
I know the NSA, for instance, regards completing Coursera's Data Science specialization  as at least noteworthy enough to mention it in their Data Science job listings.
⬐ RockyMcNutsStrikes me as an R data wrangler certification, not a hard core data science quant / codebreaker, not that there's anything wrong with that.
I've started taking a couple of Coursera specialization tracts:
Data Science - Johns Hopkins https://www.coursera.org/specialization/jhudatascience/1
Data Mining - UIUC (edit: was Johns Hopkins - bad copy/paste) https://www.coursera.org/specialization/datamining/20
There are more specializations that you can get here: https://www.coursera.org/specializations
It's kind of a layer on top of the free courses. I've been pleased so far. They'll also look nice in the education section of your resume, if you care about that.
⬐ ojbyrneThe Data Mining one is from UIUC.⬐ strictnein⬐ acconradYou're correct. Bad copy/paste on my part.Will they actually? Are employers starting to value Coursera courses as valuable educational experience?⬐ strictneinNot sure about the individual courses, but I've seen the certified tracts mentioned in job descriptions, most recently in some data science positions.
Hi, I was in a similar situation to you last year. I was working as backend developer and took the online course on Machine Learning on Coursera and realised that I want to work on Machine Learning in the future.
One of the myths is that you can learn to use toolkits and programming languages (R, Python) and become eligible for Machine Learning jobs (I certainly couldn't). It's only when you begin to understand the underlying maths behind the algorithms, you can be successful in interviews.
I would say getting another degree is the best way to go about it since it is very much an academic field. However, if that is not an option, I'd recommend looking at some online courses such as:
In addition, I would supplement the courses with a good Machine Learning textbook such as [Pattern Recognition and Machine Learning by Bishop].
By the way, there is an awesome course track on Data Science from Johns Hopkins on Coursera right now, which includes introduction to R.
Coursera offers a data science specialization:
It's cheap [if you want the certificate, free if you don't] and provided through John's Hopkins.
As other people have pointed out that even though its an online degree it has the same price as an offline one. Though it doesn't have the same credential as Berkeley (or maybe the rigorousness), Coursera's Data Science specialization  from John Hopkins costs $500. Recently they announced that they have partnered with SwiftKey  for the final capstone project.
I think coursera actually started tackling the issue by offering "specializations", i.e. the "Data Science" specialization is a set of 9 small classes which should in the end provide the student with the tools needed to be a "junior number hacker" or something like that.
It's not a full CS curriculum, but I'd think vertical narrow "mini curricula" may be good enough.
Interesting. I wonder how this compares to Coursera's Data Science specialization, from what it looks like they both have very similar curriculum.
⬐ gautambayWe got asked this question before, and here's our analysis of the differences.
1. Coursera focuses solely on R for Data Science. SlideRule covers additional tools (e.g. Python, SQL) which a practicing data analyst will find handy. It seems there's a bit of an R vs Python debate in the data world, so we think it's useful for people to know both.
2. SlideRule's path has an (optional) "intro to programming" section for beginners. Coursera assumes some prior programming experience.
3. Most of the courses in the SlideRule path are "self-paced", so in theory someone studying this full-time could cover it in 4-6 weeks. Coursera has fixed start and end dates, so the fastest one could complete the track (accounting for interdependencies of courses) is ~24 weeks.⬐ krrishdThanks for the response, that definitely makes sense. I guess it really depends on the specific technology you want to learn and the type of learner you are.
The $50 is an optional fee for the Signature Track. If you pay for this track, Coursera adds some extra checks to validate your identity and issues you a "Verified" certificate.
You can still take all of the courses for free and get a certificate, but Coursera won't validate that you actually did the coursework.
I'm guessing you could pick and choose to pay for only certain classes, but you have to pay for all of them to earn the overall "Specialization" certificate.
See the following url for details: https://www.coursera.org/specialization/jhudatascience/1?utm...
Think I'll do this while I wait for  to start. Might be a good introduction for the class as well!
Don't want to be "that guy" but compare these offerings:
GA Data Science (4000$ and taught by MBAs): https://generalassemb.ly/education/data-science/new-york-cit...
Johns Hopkins Data Science via Coursera (490$, taught by Professors of Biostatistics, and granting a cert with Hopkins' name on it): https://www.coursera.org/specialization/jhudatascience/1?utm...
Now I'm not taking either class, but going to be very hard for GA to compete with things like this (and not to mention the outrageous quality codeschool.com pumps out). Let alone the rent they must pay for their awesome spaces.
Spanish, Chemistry, EE, Woodworking, Gross Anatomy and other classes with a serious lab component need an offline element for sure. But they are teaching things that most people usually pick up better from blogs, coursera, etc. (IMHO).
⬐ JPKabWow, the Data Science course in NYC is taught by MBAs?? That's bullshit. I'm attending (on my company's dime, since they actively encourage any and all training) the DS course in DC. I had met both the instructors in the past and seen their work. They are real deal industry stat/ML experts with full time jobs in the field. Had they been MBAs, I would NEVER have signed up.
Regarding the online stuff like Johns Hopkins, I'm a self-learner. I love doing the online courses, but there is something that is fun, engaging, and advantageous to meeting classmates in person. Unlike University of Phoenix, this is a class where people are actively building shit. You can clearly and objectively see who is and is not competent among your classmates. This makes identifying people you would want to work with a lot easier. This won't happen in an online class.
But let me add: 4000 is a lot. I probably couldn't have swung it without my company picking up the tab.⬐ jamiequint⬐ jamiequintWhat happens if a statistician gets an MBA? Are they all of a sudden disqualified to talk about statistics? In what way is a Data Science class taught by an MBA bullshit?An MBA with a focus in Statistics. Irrationally hating on MBAs for no reason doesn't make your point stronger.⬐ subpixelTo play devil's advocate, it's not all about the quality. It's about the product, and to whom they are selling it.
GA is something like the University of Phoenix meets the Apple Store. In-demand course offerings, aspirational customers, sexy downtown location.
Good luck earning all that money back though.⬐ toomuchtodo> GA is something like the University of Phoenix
Show me one person who takes a University of Phoenix degree seriously.⬐ subpixelI don't know them personally, but there are many thousands of them: the people who pay to pay to take University of Phoenix classes, and who forked over about $1B dollars in each of the last two quarters.⬐ markkanofI think the key to this is what University of Phoenix directly illustrates in their commercials. They claim to have a huge alumni network which will help you get a job. So basically if they are able to get enough people to go through their program early on, they can turn around and leverage that to recruit new students. All of the previous graduates have to take the degree seriously when looking to hire. Otherwise they would be admitting that their own degree is a joke, which they are unlikely to do.
You can also take MOOC courses for example: https://www.coursera.org/specialization/jhudatascience/1?utm...
⬐ pkinskyInteresting, but I'm not sure about investing $150 for the first round of three classes.⬐ jtleek⬐ pellaJeff here - one of the creators of the classes. The great thing we like about this is that you can take the classes for free if you want. You only have to pay if you want the specialization. More info here:⬐ sudontI can vouch for the value of the material. I took yours last year and it definitely helped with both work as well as my personal projects. In particular the components on functional knowledge are things that other courses tend to ignore.⬐ stenJeff, I'm signed up for the entire set at the moment. It's just a matter of deciding if I want to pay for it and focus on it or take other classes (MIT is doing a data course as well this spring over with edx). If I can get my employer to pay that'd be better, not because it's expensive but because I perceive that it would be better material to add to my resume if I can say it was work sponsored.
I'd like to know your thoughts on the value of the final capstone project respective to career development?:)
Open source lecture material
"These are the course materials for the Johns Hopkins Data Science Specialization on Coursera"