Building Your Data Science Career

Building Your Data Science Career

We recently sat down with Rishi Sharma to discuss how to get into machine learning and start your data science career. Rishi is a professional with a focus on strategy and product management in the investment, trading and financial technology space. He has 10 years of experience where he has worked with investment management and software development firms on new platform strategy, tech stack build-out, deploying analytics and algorithmic trading platforms, and creating services and strategy for new markets. Rishi enjoys working in areas where multiple disciplines intersect and there are opportunities to solve new problems with the use of technology.

 

Rishi holds an MBA from the University of Chicago, Booth School of Business, a Masters in Finance from Illinois Institute of Technology, Chicago and a B.S. in Computer Engineering and Math from the University at Buffalo.

 

EmergingEd: Rishi, thanks for taking the time to chat with us today! A lot of our readers come from non-technical backgrounds and are not sure about where to begin their machine learning career. Can you tell us about how you first got started?

 

Rishi: I think I got an informal start. It was not that I applied for a specific job where knowledge of machine learning was a requirement. I had a personal interest in this field and had done a little bit of work in school building statistical models and with some data analysis. So, I used that to start and then picked up most skills on the job. As I worked with these concepts, I also built up knowledge by doing personal projects in my spare time. I would come back home and try to find datasets and different types of problems, and then work through them at my own pace and I think that is what helped me learn the most.

EmergingEd: Self-development is hard for a lot of people to do. Thinking back on all these years, if you had to give your younger self some advice, what would it be?

Rishi: I would have told my younger self to spend more time on structured learning experiences and invest more time in getting a broader perspective while learning something new. Sometimes when you are trying to learn something new for work, you tend to focus on a particular problem or project but it’s important to understand that the subject matter can be far broader. When it came to machine learning and applying the techniques more broadly, I had to take myself out of the niche that I had built with particular skillset, and take a broader view of where I was going with my learning and application. This meant I had to do self-study in statistics, math, and programming. My professional roles and work helped me learn and develop a lot, but I do wish I had taken advantage of some courses available on these topics, similar to what EmergingEd is offering.  

 

EmergingEd: That is a great segue into my next question. Generally, our audience is comprised of non-technical managers and executives within an organization. If they want to develop these machine learning and data science skills, what advice can you give to them?


Rishi: I think its important to first ask yourself, “What is it about machine learning, or what is it about this field, that really interests you? Do you know what you want to do?”, What problems are you interested in solving? Whether you want to do this as a full-time job or just as a hobby, the appropriate steps will vary. You don't have to switch jobs just to learn machine learning. You can continue to do that on the side with courses like EmergingEd to build a foundational understanding, However, if you wanted to pivot to a machine learning career, then you should understand what type of work you want to do and lay out steps for how you’ll build that skillset.

EmergingEd: That is helpful advice. What types of careers can somebody pursue in machine learning and data science? Can you describe some of those roles for us?

 

Rishi: There are a lot of roles you’ll find on job websites. Titles like machine learning engineer, data engineer, data scientist and data analyst are common. The engineering roles are responsible for building the data infrastructure and processes that allow for the data to be processed and organized. The data scientists and analysts utilize models and applications to understand what the data is telling them in a particular space. But, at its core, there are common themes across these roles. You must understand statistics, probability theory, mathematics and programming. You must know how to write code at some level, model data and then interpret the results of that model. The goal is to be able to generate tangible outcomes. Most of the roles in machine learning and data science focus on this goal.

EmergingEd: How do you envision machine learning roles changing over the next decade?

Rishi: I believe the core requirements for these roles won’t change much. You’ll still need to understand statistics, data modeling, mathematics and programming in order to be able to practice this effectively. However, I think there will be much more of an emphasis placed on having context-specific knowledge. For example, if you are a data analyst working in medicine or biological field, you might need to acquire more context specific knowledge and may have to undergo training similar to that of a biologist. In my opinion, that type of expertise will become key

EmergingEd: Let’s talk a little bit about EmergingEd’s machine learning courses. In your opinion, what value would that bring to professionals who are new to machine learning? 


Rishi: What stands out to me about EmergingEd’s courses compared to other online courses I’ve taken is the way it’s structured -- from beginning to end. The courses build up gradually and does not throw a lot of information at you at once. The EmergingEd machine learning courses have the right blend of theory and practice and push you to think about how machine learning can be applied in the practical context. I think that’s the best way to learn something—by applying theory to practice. Also, the fact that these courses are self-paced which means that you can take the time you need to make sure you’re absorbing all of the material. EmergingEd also provides access to industry experts and practitioners. It’s helpful to have this when you want to have a more practical conversation about a new field of study. I think that’s a unique feature that you don’t see from other online providers.

EmergingEd: I definitely agree with those points about the courses. That wraps up our interview for today. Thanks so much for taking the time to chat with us and give us your insights, Rishi. It’s been very informative and helpful.

 

Rishi: My pleasure, thanks for setting this up!


For those interested in learning more and taking our machine learning courses, please visit our site.


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