500 Days of Consistent Learning, Alongside an MBA | Scaler Data Science Review:
I didn't expect learning to become such a big part of my life when I first decided to upskill. I was curious about data science and machine learning, but I was also at a point where I had lost some confidence in where my career was going.
What started as curiosity slowly became a habit. I began making small changes to the way I spent my time, kept learning even when my schedule was busy, and gradually became more consistent than I had ever been before.
Today, when I look back at that journey, the number that stands out to me is 500-plus days of consistent learning. But the streak itself isn't really the story. For me, it represents what can happen when small changes become part of your life, and you keep going long enough to see where they take you.
“This is the maximum I could grow”
There was a point in my career when I started feeling like I had reached the maximum I could grow.
I had begun my professional journey as a mechanical engineer in Bhubaneswar, and for a while, I was focused on building my career in that direction. But over time, I became quite demotivated. I wasn't sure what the next step was or how much further I could really take my career.
It wasn't that I had completely stopped wanting to grow. I just couldn't see what that growth was supposed to look like anymore.
Around the same time, I became curious about data science and machine learning. I wanted to understand what was happening in technology and whether learning something completely different could open up opportunities that I wasn't seeing in my current career.
So I decided to upskill. It wasn't because I had a five-year plan mapped out or knew exactly where the decision would take me. It started with curiosity. I wanted to give myself a chance to learn something outside my existing field.
That's when I came across Scaler's Data Science and Machine Learning program. Before the course started, I interacted with Anshuman Singh, who encouraged us to dare to dream big. I started the program with that thought in mind.
I didn't know where the decision would take me at that point. I just knew I wanted to see what was possible.
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Coming from mechanical engineering, moving into data science was a little intimidating. It was a completely different field from what I had studied, so I was initially apprehensive about how I would manage the transition. But I’m glad that the curriculum was nicely structured. It started with the basics and gradually moved towards more advanced topics, so I could build my understanding before moving on to the next level.
I didn't have to worry about knowing everything from the beginning. As I went through the basics and started understanding the concepts, the more advanced topics became easier to follow. So transitioning to data science became much more manageable for me.
500 days of consistent learning
One of the biggest changes for me was developing the habit of learning consistently. I was at a point where I felt completely directionless and hopeless. Reaching 500-plus days of consistent learning felt very different from that point in my life.
It wasn't one big change that got me there. It was a lot of small, incremental changes. I attended classes, worked on data-related problems, read about industry trends, and spent time understanding what I was learning. I also continued learning outside the program, solving data-related problems on my own and researching what was happening in the industry.
You don't always notice the difference while you're making those changes. One class, one problem, or one more hour of learning doesn't seem like much on its own. But when you keep doing it over a long period, those small efforts start becoming a habit.
For me, the 500-day streak became a reminder that I could stay committed to learning. It also showed me how much can change when you keep making small improvements instead of waiting for one big moment to change everything.
Doing this alongside an MBA
I was pursuing my MBA while doing the program, so balancing the two was challenging in the beginning. I already had a busy schedule, but I didn't want to use time as an excuse for not learning.
I knew that lost time would never come back and that commitments in life would only increase. If I wanted to build a successful career in data science, I had to make time for learning rather than waiting for my schedule to become less busy.
The good thing was the program was flexible enough to help me manage that. The evening classes and recorded sessions allowed me to fit the learning around my MBA without compromising my studies.
So all I had to do was make that time count and continue to use it consistently. Over time, learning became a habit I could sustain.
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When I think about what I learnt in college and what I needed later as a professional, the biggest difference for me was the practical side of learning.
I remember studying chemistry in the first year of college. It was part of the curriculum, but I don't use it in my professional life today. Later, when I started learning data science, I was looking for something more focused on skills that I could actually apply.
The projects, case studies, and assessments gave me opportunities to work with what I was learning instead of only studying the concepts. I also found that this kind of skill-focused learning helped when I was applying for jobs because I had a better understanding of what I could actually do.
And the people around me were incredibly helpful. I interacted with mentors, industry experts, and peers throughout the program, and those interactions helped me understand the concepts better and develop what I call a go-getter mindset. We discussed our doubts, helped each other out, and celebrated our progress along the way.
My honest Scaler Data Science review
When I think about how the course was different for me, I think the conventional college curriculum in universities and colleges lacks practical knowledge. I remember studying chemistry in the first year of college, which is of absolutely no use to me now as a professional.
Secondly, I think skill-focused learning did allow me to be better employed. I had better employability while applying for jobs. There is an increasing awareness about the importance of upskilling, and that has created a need for skill-focused learning.
The other thing was the mindset transformation and personal development. My interactions with mentors, industry experts, and peers helped me develop a go-getter mindset. The program also focused on practical learning, which helped me develop my technical knowledge and apply complex concepts effectively.
I also used to solve a lot of data-related problems and research industry trends outside Scaler. Regular assessments, hands-on projects, and mentorship played a major role in my understanding.
Turn Learning into Career Growth
If you think you've missed the bus
I just want to say that things can change. I didn't know what was going to happen when I decided to start learning, but I kept going and slowly got my confidence back.
So, if you're going through a difficult phase in your career, keep trying hard and don't give up. Things may not work out the way you want them to immediately, but that doesn't mean you stop trying.
If you feel that you've missed the bus to a great career, please be assured that nothing is lost. If I can regain my lost confidence in life, so can you.
FAQs
Can you learn data science from a mechanical engineering background?
Yes. Avijit’s experience shows that a non-CS background does not have to be a barrier. He came from mechanical engineering and was initially unsure about moving into data science, but found it easier to follow the program because the concepts were introduced progressively.
Can you do the Scaler Data Science program alongside a full-time degree?
Yes, Avijit was pursuing an MBA during the program and found the schedule challenging at first. Evening classes and access to recorded sessions allowed him to continue learning while managing his MBA.
How much consistency does it actually take?
Avijit’s experience was built around regular learning. He eventually completed 500-plus days of consistent learning, while also working on data problems and keeping up with industry developments outside the program.
What does the program cover that a college degree doesn't?
For Avijit, the distinction was the amount of practical work involved. His college education had been largely theoretical, whereas the program gave him opportunities to work with projects, assessments, and case studies and apply the concepts he was learning.
Ready to build your data science skills? Explore Scaler's Data Science course to learn more about the program.
