Scaler Review: Why a Submarine Captain Chose Scaler
After more than two decades in the Indian Navy, Vivek Prakash wasn't looking for a career change. If anything, he'd already built the kind of career most people spend decades working towards.
So when he decided to learn Python, statistics, and machine learning, it raised a different question altogether.
Why would someone with years of operational experience choose to become a student again?
If that question crossed your mind too, let's take a look at this Scaler dsml review and see how it all started.
Build an AI-First Career, Master the Complete Skillset
Choose from our industry-leading programs designed for career success
Modern Software and AI Engineering Program
Master full-stack development with AI integration
+1000 moreModern Data Science and ML with specialisation in AI
Advanced data science techniques with AI specialization
+1000 moreAdvanced AIML with Specialisation in Agentic AI
Deep dive into AIML with focus on Agentic systems
+1000 moreDevOps, Cloud & AI Platform Engineering
Build and manage AI-powered cloud infrastructure
+1000 moreAI Engineering Advanced Certification by IIT-Roorkee
Premier AI engineering certification from IIT-Roorkee
AI Forward Deployed Engineer Program
Full-stack engineering, production AI and client-facing consulting
+1000 moreA Question That Stayed With Him
Imagine working in an environment where every day brings large volumes of operational data. Some patterns are obvious, while others take years of experience to recognise. The data is always there, but analyzinga nd understanding it all still depends on judgement built over time.
Over the years, Vivek found himself thinking about that process differently. Could data reveal something more if there was a structured way to analyse it? Could machine learning help understand patterns that experience alone might miss? Those questions stayed with him long after the decisions themselves were made.
His Journey So Far
Vivek Prakash began his journey at Sainik School before graduating from the National Defence Academy and joining the Indian Navy in 2001. Over the next two decades, he specialised in submarine operations and combat systems, serving as a Weapons Officer, Executive Officer, and eventually taking command of submarines.
Throughout those years, his work revolved around understanding complex operational systems, interpreting data, and making decisions in environments where precision mattered. It was a career built on technical expertise, operational discipline, and years of experience working with advanced defence technologies.
Today, Vivek is the Head of Combat Systems at Larsen & Toubro (L&T), where he works on advanced combat management systems for defence applications.
The Realisation
"I realized that data is really, really important. We were never formally trained on data. We always had to conjecture on the data, use our precedents or history."
That thought stayed with Vivek long after his years in the Navy. Much of his work had always involved data, but understanding it and formally analysing it were completely different. He knew how to read patterns, make decisions under pressure, and rely on years of operational experience. What he wanted was to understand the methods behind those decisions and see how data science could strengthen them.
Looking for a more structured way to understand data, Vivek enrolled in a Data Science and Machine Learning program at Scaler. For Vivek, it wasn't really about learning an entirely new field from scratch. It was about building a stronger foundation in Python, statistics, and machine learning so he could approach familiar problems with a different set of tools.
As he progressed through the program, he began to view problems in his own career through a different lens. One of those ideas became a presentation on how machine learning models could be used to analyse sonar data. And the good thing was that this drew the attention of senior defence leaders, who saw him as someone with a new set of skills and much more to contribute.
Just like that, every industry has professionals who have spent years solving problems, making decisions, and building expertise through experience. If you're considering a career switch to machine learning, you may find that your existing knowledge becomes just as valuable as the new skills you're building. It simply gives you another way to explore problems you've been working on for years.
How Scaler Transformed Careers in Different Fields
Scaler learners achieved 2.5x salary growth with average post-Scaler CTC reaching ₹23L.
Google
Amazon
Microsoft
Flipkart
Adobe1200+ moreLearning DSML As A Domain Expert
Vivek had never written code before. Vivek didn’t have a technical background when he was deciding to learn Data Science. Like many learners looking at Scaler from a non-tech background, Vivek had never written code before. Programming was new to him, and he says coding took time to get used to. Before joining Scaler, he had enrolled in another online program. It was there that one of his Python instructors suggested he take a look at Scaler instead. After attending a few sessions and understanding how the program was structured, he decided to join the program.
Now, going through a journey is always a bit different than what you’d expect; similarly, he mentioned that the learning experience was different from what he had expected. Classes were held every alternate day, with mentors and dedicated doubt-solving sessions in between. The curriculum was ongoing and picked up gradually one after another, so there was no confusion, which sometimes happens when lots of topics are introduced at once. He went from Python to statistics, data visualisation, product analytics, and machine learning. That pace gave him enough time to understand one concept before moving to the next.
One detail from those months stayed with him. Although he had spent years in the Navy, he never introduced himself that way to his classmates. "I never revealed that I'm a naval officer," he says. Within the cohort, he was simply another learner. "I always behaved like a student."
His experience is similar to what Scaler's Head of Instructors describes in the DSML cohorts about ML for domain experts. Many learners come from engineering, finance, operations, consulting, and several other industries, so the program begins with mathematical foundations before moving into programming, statistics,s and machine learning. So that learners can build technical concepts progressively, especially for professionals who may be entering data science for the first time.
Different Career Paths into Data Science
Now you know how Vivek entered the classroom after a career in the Navy, but there were also many others who came to the same classroom through very different careers.
Demba Siby had spent 16 years working as a development project consultant before deciding to learn data science. He later went on to found The Fluent Professional Space.
Gokul Lakshmanan's journey began much earlier. He joined the program as a fresher and started his career as a Product Analyst at Curefit.
These scaler success stories, along with Vivek's, are featured in Scaler's audited placement report.
Every career switch starts with learning something new. If you're exploring data science and wondering where to begin, take a look at Scaler's Data Science and Machine Learning Program with AI specialization to see how it's structured for learners from different backgrounds.
Turn Learning into Career Growth
FAQs
Do you think machine learning can be taught to defence or armed-forces professionals at Scaler?
Yes. Vivek Prakash's journey shows that professionals from the defence sector can build a career in data science. After spending more than 20 years in the Indian Navy, he joined Scaler's DSML program without a programming background. During the program, he learnt Python, statistics, product analytics, and machine learning, and later moved into the role of Head of Combat Systems at Larsen & Toubro (L&T). His experience shows how domain expertise can be combined with technical skills through structured learning.
Can Scaler's DSML program be used by non-programmers?
Yes. The program is designed to build technical concepts step by step instead of assuming prior programming knowledge. It begins with mathematical foundations before moving into programming, statistics, and machine learning. As Scaler's Head of Instructors explains, learners come from a wide range of backgrounds, including finance, operations, consulting and other domains. The audited placement report also features professionals who entered the program at different stages of their careers, showing how the curriculum is designed for learners with varied levels of experience.
