‘Hype Man’ Guides ML Students to Success
Course Report recently spoke to Machine Learning Engineering Career Track mentor Srdjan Santic about his background in self-learning, his career in data science, and his passion for teaching the next generation of technologists. Here’s part of that conversation.
What is your background and how did you get into machine learning and AI?
I first got exposure to statistics and data in school—I have a bachelor’s degree in economics and a master’s degree in econometrics from the University of Belgrade. After graduation, I landed a statistician job with The Nielsen Company, the marketing research consultancy, which taught me everything I know about data analysis, data cleaning, exploring data, and building predictive models—now known as machine learning and data science.
The leadership team at Nielsen emphasized outside learning in both theoretical and methodological views. We got together with statisticians from the other offices in Central Europe for eight hours a day for six days per week—what would now be called a “bootcamp”—and covered a series on statistics or data mining with case studies and a take-home exam or mini capstone project that would go into your performance assessment. Those immersive classes were part of my motivation to mentor at Springboard.
Now, I have five years of experience being a data scientist, a total of 12 years of experience working with data, and three and a half years as a mentor for Springboard.
Why did you want to become a mentor with Springboard?
I’ve always loved teaching and I wanted to do online mentoring to contribute back to the community. Self-study is very hard—I started learning Python through a 12-week course from EDX and took additional courses on machine learning through EDX and Coursera. When I was doing it, I didn’t have any friends who were programmers, so I didn’t have anyone to talk (or complain) to. I felt like a program that provides student mentorship would be a great way to give back to the data science self-study community. It’s been the most fulfilling and rewarding experience of my professional career—hands-down.
Do you also have a full-time job in addition to mentoring at Springboard?
I recently started a consulting company, Logikka, with a friend (also a Springboard mentor)! We provide end-to-end data science and machine learning solutions to clients in order to help transform their business, including in-house training at companies here in Serbia. As the co-founder and principal data scientist, I lead and oversee the more technical parts of our projects (but being very hands-on).
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As a machine learning mentor, how do you work with your mentees at Springboard?
Each student has one 30-minute call with their mentor per week at a designated time and we also stay in touch over email. I have access to the Springboard dashboard to monitor my mentees’ progress throughout the curriculum so I can see how they’re doing in the bootcamp and be prepared for the call.
Springboard students in the Machine Learning Engineering Career Track have a lot of support—mentors can answer questions over email or in the community forum, teaching assistants (TAs) are available, and they have unlimited mentor calls where students can reach any number of mentors. If you’re working on a homework assignment or mini-project and are really stuck, you can easily contact a mentor for help.
How is mentoring for Springboard different from teaching?
Mentoring itself isn’t teaching—mentoring is guiding someone by lighting the way. Yes, we can get more hands-on if needed, but Springboard has other academic resources to help with that portion. There’s also a fine line between mentoring and tutoring—for example, in academia, a Ph.D. student is going to meet with his or her advisor to understand something from an academic paper, not a specific math concept, expecting the advisor to teach them something using chalk on a whiteboard. It’s about guiding students through the curriculum, the mini-projects, and the capstone project, and making sure they’re on track to graduate and aren’t falling behind.
Sometimes, mentoring is also being an ear to listen when my mentees need to vent—six months in a bootcamp is a long time, so if they get discouraged, I’m their “hype man” and am there to help them recognize how much they’ve achieved so far. I’m also able to shed light on the data science career path since I follow the industry in its technical progression, how the data science role is evolving, and the latest job requirements in the market.
Do Springboard students work in addition to taking the bootcamp or is it a full-time commitment?
Springboard’s AI/machine learning bootcamp is primarily a self-study program and most of our career-tracked students are in between jobs and are fully committed to the course. Some do have full-time jobs and families, but some might have a part-time job and the rest of their time is focused on Springboard. Most people will finish in six months if they spend 15-20 hours per week on the course.
What types of students take the AI /machine learning bootcamp?
There are two groups of students that take this bootcamp:
- First, experienced software engineers who want to get into machine learning because lots of software products (from web services to phone apps and even physical products that, of course, run on software) have a machine learning model at their core. If they’re going to be the one integrating these models, they want to know how to build them and add to their full-stack skills.
- The second group are data scientists who have been working for a few years but not building software and want to up their game because more employers are looking for full-stack data scientists—people who can build a model and also package it up properly so the engineering team can just “plug-and-play” the model.
Because your students have professional experience as a software engineer or data scientist, are they getting jobs as senior machine learning engineers when they graduate?
By its very nature, machine learning engineer jobs are “senior.” After graduating, students will be qualified for a senior-level engineering position in machine learning.
For the rest of Srdjan’s conversation, including more advice for people interested in a career in machine learning, head over to Course Report.
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