Among the many questions I get asked regarding my experience of transitioning to the field of analytics and Artificial Intelligence (AI), perhaps the most frequent ones are about why I chose the specific course I did and if it has been worth the money.
Today, I will attempt to answer those questions by sharing how my choice of learning program is having an impact on my life as a data scientist.
It’s been less than six months since I received my certification for completing the Post Graduate Program in Data Science and Machine Learning from the University of Chicago Graham School and Jigsaw Academy and I am here to share things I keep learning, as I apply in practice what I learnt during the course.
How A Good Data Science And Machine Learning Program Helps
There are literally hundreds of data science, machine learning and AI related course on offer for anyone who is interested today.
In fact, it is possible to learn everything one needs to get started and get going for no cost at all, using excellent and completely free online resources. Among the paid options, courses range from those that can be completed in a few weeks, sometimes for less than a hundred dollars, to those that may cost a small fortune.
For someone wanting to acquire the skills but without enough insights into the best way to go about it, the task of making a choice can sometimes be a difficult one.
The question is not just about which route or program offers the best learning experience but also about how you are equipped after the course is completed to succeed as a professional in the field.
The obvious way to assess this is to look at how the job market perceives individuals who have completed the specific program you are considering.
The less talked about aspect is to do with how well the course has prepared you to actually handle the challenges of working in the industry and the impact it has had in giving you some competitive advantages that help you stand out.
Four Ways In Which The PGPDM Program Has Helped Me Be Better At Work
Over the last six months, on the one hand I have been developing my skills and gaining knowledge by doing short internships for small companies in the space and self-conceptualized projects.
On the other, I have also managed to extend certain data science and machine learning services to the portfolio of offerings for my company that helps businesses create great digital experiences for customers.
The list that follows is a summary of how the specific program I chose has helped me perform better at work.
One: Instant Credibility
When I tell someone that the course I enrolled for took 10 months of rigorous work to complete, they are usually impressed straightaway. This holds true, even when I’m speaking with industry veterans!
It is not that they immediately start thinking of me as some machine learning superstar but what they can see for sure is that I am serious and committed enough to have invested time and money to enhance my skills.
There is also a positive assumption about quality that they make right away. The names University of Chicago and Jigsaw Academy both create an expectation that the certification could not have been achieved without achieving a certain level of proficiency.
Two: Readymade Solutions
Almost every topic covered during the course also had an associated project or assignment that needed to be completed.
Beyond the learning, those exercises have also left a rich collection of code that is needed at different points of any project – beginning with reading or importing data to creating and evaluating complex models.
These have regularly come in very handy while working on projects for clients or other purposes.
The work done on assignments completed during the course has helped save an immense amount of time by making available solutions which can be used for current projects with simple and quick adjustments.
For example, it took me just an afternoon’s effort to complete a comparative analysis of India’s performance at the recently concluded Asian Games in Indonesia with previous editions.
Without readily-available code to help me tweak every element of the graphics that I wanted to create using R, the project may have taken significantly longer (perhaps, long enough to discourage me from completing and publishing the outcome).
Three: Friends on call
Recently, a client was discussing a plan to create a recommendation engine for a content platform they have launched and wanted to understand if they could get an effective solution with the rather limited meta data they have about the content.
Now, even though I had an understanding of what the approaches could be, I had not yet done any hands-on work on a problem that could be considered similar in nature.
But that was hardly an issue since one of the groups in the course had done some remarkable work in creating a recommendation engine for their capstone project.
With the client’s consent, I called up a friend who worked on that project, had a 30-minute chat with him and before I left, was able to provide a clear assessment of what could be done and how we could go about getting there.
The network of classmates is turning out to be a great asset after the course has been completed.
Beyond help with specific questions, they are also there for suggesting answers to general questions posted on chat groups. The group is a great source of content relevant to people in our stage of their data science journey.
And very importantly, a network that is helping each other find their feet. As more individuals from the network find jobs related to data science, they are able to recommend others and help them make the career transition as well.
Stories that are great to hear!
Needless to say, many faculty members who I came across also provide similar support. Having access to a whole network of peers and mentors is a great support system to have, especially in these early days.
Four: Learnt How To Learn
What really pleases me these days is that I am able to find solutions to complex problems that I have not encountered before.
The Internet is of course the repository of almost all information that one may need, which often makes learning something new overwhelming. With hundreds of links offering answers to every question you have, it is easy to waste time going through links and content that may not work for you.
Input from faculty, guest speakers and classmates as well as the demands that the course made have helped me understand the best approach to use as well the sources of information that I am able to learn best from.
As a consequence, there is a sense of fearlessness while approaching a new type of data science or machine learning problem. After all, the answer is almost always out there and I feel very confident about my ability to find it, thanks to having already handled such challenges, many times over.
Conclusion: Like A Deep Dive Into Deep Learning
My short experience after completing the course has led me to the belief that success in the field of data science and machine learning depends not just on existing skills and knowledge but many other factors that help in making one a problem solver.
I believe investing in a course that requires intense sustained effort provides access to a group of people and ensures that you get significant experience in problem solving, is definitely worthwhile for those serious about making a career in the field.