Senior Data Scientist at RBC joins machine learning instructor team

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Karthik Kuber, Instructor, Certificate in Machine Learning
Karthik Kuber, Instructor, Certificate in Machine Learning

Automation isn’t the future. It’s here now (whether we’re ready or not).

Machine learning data scientists have the power to change the way businesses work and, by extension, the way we interact with the world. With Toronto creating more tech jobs in recent years than Silicon Valley, now is the perfect time to break into this dynamic field.

Since its launch just over 6 months ago, our Certificate in Machine Learning has taken in 2 groups of ambitious data scientists, leading them down a groundbreaking educational path full of career-changing experiences and skill-building opportunities.

Karthik Kuber, PhD is one of our expert instructors and a Senior Data Scientist at RBC. Here’s what he has to say about the state of machine learning, what makes our program vital to the data science industry, and why you should care.

How did you start your career, and what has been the key to your success?

My first foray into live data science projects was through research assistantship work during my PhD at Syracuse University. I enjoyed those applied machine learning and statistics projects and continued to work on such problems at Microsoft in Seattle after graduation. Now, I enjoy the balance of applied machine learning and teaching – trying to look at machine learning problems at RBC with a research mindset, and teaching at York with an industry mindset. I’m doing my best to bridge the academia-industry divide!

What has kept me interested in the field has been curiosity. Every new piece of work is one step closer to “intelligence” (whatever that might be)! It is great to be on board for the ride by observing, learning, and participating.

What makes machine learning such an exciting and rewarding career path?

We live in an interesting time—a great confluence of algorithmic approaches, compute power, and data. Although most algorithms are conceptually decades old, and most new ones are minor improvements over them, the explosion of data and large-scale computation on the cloud has made concepts come to life by making results available in real-time for a practitioner to see, iterate, and improve.

As organizations, especially those with enormous amounts of data, are beginning to see the value that specialized machine learning teams bring, any improvement to an existing process can lead to massive benefits in terms of efficiency, financial impact, and significant insights. As practicing data scientists, we get to see these benefits directly because of models we have built, which is exciting and rewarding.

What trends and changes do you predict for the field over the next few years, and how do they affect someone starting their career in data science?

Now and in the coming years, we should work with the latest approaches in this fast-moving field, and not against them. It is true, we are building algorithms that will take some jobs away, but most of these “stolen” jobs are a result of automation, and that is not new to humans; it has been happening for centuries. With advancement may come a loss of existing roles, but it also introduces newer and better opportunities! This field is no different. It is important to constantly develop and improve skill sets, in order to keep evolving.

If all one does is use an existing algorithm blindly, that job will likely be lost. One example of working with this algorithm will be to complement it through intelligent feature selection and engineering, choosing the best approaches to business problems, translating the problem precisely into a mathematical one, and most importantly, creating actionable outcomes to maximize impact. Think of when the first motor vehicles were first built; humans could not compete by trying to run faster than those vehicles. We had to learn to drive them efficiently.

How does our program prepare students to enter the job market?  What makes our program unique?

The Certificate in Machine Learning strikes a great balance between the theory of machine learning, what the industry wants, and hands-on experience both in class and by partnering with organizations. It also has a comprehensive mix of traditional programming and mathematics material, discussions on select readings and videos from the plethora of online content, making it a sought-after industry-ready program.

It starts off with an introduction to the field and its essentials, condensing the vast amount of machine learning theory, and gets students working on projects to get their fundamentals right. It then goes into issues that arise with scale, just as in the real world, introducing students to cloud computation, and finally gives students a mini-internship of sorts through the Capstone. It is a well-rounded program and lands students exactly where they need to be able to excel in their data science jobs.

What advice would you offer someone considering our program?

Come along for the ride if you enjoy learning and applying the science and mathematics behind machine learning to drive business impact. Do not jump into the field just because of the hype. Hype and buzzwords have a shelf-life. If you are on the fence, trust me— this is fun!

Want to use machine learning solutions to form the business world of the future? Get program information