While tracking my activities is obviously good, what makes it a great product is the eco-system it provides. Every day, I am competing with my friends to finish those extra steps and shed those extra kilos! We all challenge each other and buck up each other (sometimes in not so polite way as well) to make the group a fitter group!
Well, staying fit without an eco-system and learning new tools by yourself have a few common challenges:
So, in this post, I am sharing some best practices, I have learnt the hard way. I have undergone more than 50 open courses on Data Science across various platforms (including Coursera, edX, Udacity, SAS and several others) in last 18 months or so. I still sign up for most of the new courses which are floated.
One of the things, people get wrong is that they assume that entire course is for everyone to be undergone in same way. This is certainly not true. It is true that the creators of the courses try hard to make the courses as generic for the larger audience, but you still need to decide and define what you are looking for in each of these courses.
For example a course on machine learning would typically start by whetting your appetite about machine learning. If I was undergoing that course, I don’t need that section. So I’ll typically skip that section (P.S. It may be a good idea to do the exercises – more on this later).
Before you start the course, you should have following things defined:
This is where I think Fitbit does a fab job! You need to almost replicate it for MOOCs. Here is how I typically find them.
Mentors: Typically people in your network on Linkedin, experts in the fields, bloggers on the subject. Just reach out to them and ask for help as a mentor. I usually define the expectations from them up front as well – how much time I expect from them? what kind of questions / projects I would be working upon? I also try and offer them some thing in return – it could be research / tutorial for the blogger, a presentation for the expert presenting in a conference – you get the idea!
Buddies: Best is to reach out to people who are asking questions similar to the ones you have. Again, key is to interact with them before finalizing. You would want to have some one who is determined to finish the course, participating in projects and trying out things outside the templates in the MOOC. A good buddy can make or break your learnings from a MOOC. Once found, you should spend time discussing your understanding and doubts with these buddies.
Another practice, which can be of help in some places is to join meet-ups and find people with similar interest.
Discussions are probably one of the best way to learn while experimenting. There is a reason why every coder in this world spends time on stackoverflow to get his answers. We run a similar discussion portal for data science professionals here. All the MOOCs will typically have their own discussion portals as well. If they are active, you should fire your questions there as well.
While on the disucssion portal, offer what ever help you can to fellow participants.
Another way to showcase your learning, is to blog about them. This is how I started my journey! Once you have learnt about a particular technique – apply it to another dataset and come up with a solution. Then see what other people have done. iPython notebooks and Github can help immensely here. You just need a functional blog, where you can note what you are doing, what you are doing and how you are doing.
Projects and assignments are lifeline of all the data science MOOCs. If you miss on them, you miss on a lot of learning. Again, different platforms have different approach. Udacity, particularly is more project focused than others. So, make it a point to do all the assignments diligently – even if you think they are too easy / basic or they are too tough. Just have a go at it.
If you don’t add few numbers of an array or a list thinking that it is too easy, you will struggle with the data cleaning later in a project. If you can afford, you should also participate in a few competitions outside of the MOOC using the same tools you have learnt. So, if you have learn Logistic regression, apply that on Kaggle Titanic survivor competition.
There are 2 commitments you need to make in order to make sure you don’t drop out during the process. The first of them is to stay on the schedule. Finish the content and assignments on every week with in the week itself. Don’t play the catch up game, otherwise you will fall out the next week.
This might sound easier that what is actually is. You will need to put in a lot of efforts to make sure that you are on the top of the schedule.
The second commitment you should make to yourself is the minimum score you would achieve. This is a personal choice and should be in sync with the need of the course. If this is an important course, you should at least commit 70% score.
I hope this post would help numerous people who struggle to complete open courses. They are a fabulous platform to learn new skills. If you have any other tips and tricks to help MOOC learning, please feel free to share
On the lighter side, if you use a Fitbit to track your fitness – let’s compete!
Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.
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Hi Kunaal, I have been a regular follower of all of your posts. This is really a great piece of information written in this article as always. :) Thanks, Mohammed
And avoid what I would like to call MOOC gluttony. As appealing as the prospect is of signing up for a ton of courses, however earnest your interest may be, make sure that your life lets you take in as much of the course as possible. Doing few courses to the fullest is obviously better than doing a few exercises haphazardly in many courses. And even as I type this, I have to remind myself that I may have signed up for one too many. :)
Couldn't agree more
Exactly. I started a D-Sci specialization on Coursera by taking 2 courses at a time first two months. To be honest, first 3 or so were fairly easy. Since 5th module I'm taking only one at a time. All right, Python too, but in my defense, it is easy :) Anyway, I enjoyed some of the projects in 5th module, the time spent on them was more than useful. Usually there is more than enough time to do one module so I'm using it for some extra reading like StatLearning from Stanford. Starting on D-Sci, best move ever.
Hi Kunal I am currently a 5.2 yrs java resource . I want to move into Big data industry . Will dis be a rte career move ? If yes then which is the best place to learn Hadoop. Yr reply will help really. Thanks in advance,