#78 overall
Data Science Specialization
Coursera
6 recommenders · 6 threads · recommended in 3 years, 2014–2017 · score 5.8
A 9-course Coursera specialization from Johns Hopkins University covering the data science workflow with R, including data cleaning, exploratory analysis, statistical inference, machine learning, and a capstone project. Commenters describe the first courses as easy and the later statistics course as harder.
Commenters recommend it as a good way to learn R alongside data science and machine learning fundamentals, though one notes it is good rather than great.
Machine Learning & AIbeginnerpaidinteractive MOOC#r#data science#statistics#machine learning#johns hopkins#capstone
Recommendations by year
20062026
By the way, there is an awesome course track on Data Science from Johns Hopkins on Coursera right now, which includes introduction to R.
https://www.coursera.org/specialization/jhudatascience/1?utm...
Great article, the lecture comparing the two from John Hopkins, part of the Data Science Specialization on Coursera also offers a great explanation.
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'm just finishing up Andrew Ng's Machine Learning course and would definitely say it's worth it (depending on how much, if anything, you already know about ML).
I'm also about halfway through the Johns Hopkins Data Science specialization, and I've found all of those courses to be worthwhile. But then again, I'd never programmed in R before and that's something I very pointedly wanted to pick up. So far, this series has been invaluable in terms of learning R (much like I had to learn a fair amount of Octave for Andrew Ng's class).
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:
- https://www.coursera.org/specialization/jhudatascience/1 - https://www.udacity.com/course/nd002
In addition, I would supplement the courses with a good Machine Learning textbook such as [Pattern Recognition and Machine Learning by Bishop].
Also, see https://news.ycombinator.com/item?id=1055042
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.
It sounds like you've got the beginnings of Machine Learning (classification & regression), but I'd still recommend checking out Coursera. The JHU Data Science series is good, but more geared towards DS than AI. There are others on there as well; if you're looking to get into deep learning, I echo the recommendation to check out Ng's course, though this might be good, too: https://www.coursera.org/course/neuralnets
You can also take MOOC courses for example: https://www.coursera.org/specialization/jhudatascience/1?utm...
Coursera has a 'specilization' on Data Science. It's good, not great though. It costs about US$500.
It's not really a boot camp, but a MooC. There are 9 courses and first 3 courses are really easy. For a beginner they might be intimidating, so it depends on your prior experience. The course #6 is on Statistics which is really bad. The classes are not that explanatory and exercises seem very difficult. So, I suggest you to do another Coursera course on statistics by Duke university. This course is usually referred as DASI.
Currently I am at course #6. I have to complete 3 more and a capstone project at the end to complete the data science specialisation