Manohar’s Journey: From Mechanical Engineer to Data Scientist

Data science is one of the most in-demand fields today, attracting professionals from diverse backgrounds like engineering, marketing, and finance. One such inspiring story is that of Manohara, a Mechanical Engineer who successfully made the transition into the world of data science.

In this blog, we take a deep dive into Manohar's unique journey—from his initial experiences in mechanical engineering to his eventual career shift into data science. Manohar’s story is a testament to the power of determination, continuous learning, and hands-on practice. He recently transitioned from his data science training to securing a full-time role at Trinity Mobility. In this blog, he shares his struggles, the challenges he faced during mock interviews, and the steps he took to land his dream job in data science.

From Mechanical Engineer to Data Scientist: Manohar's Story with DataMites

Discover how Manohara leveraged his engineering background and DataMites training to successfully transition into the world of data science.

Q1: Can you tell us about your background? What were you doing before this placement?

I come from a Mechanical Engineering background. I started working as a mechanical engineer, and while I enjoyed problem-solving, I found myself becoming more interested in the idea of working with data. Eventually, I realized that data science was the perfect field for me to combine my technical skills with my interest in solving complex problems using data.

Q2: What sparked your interest in data science?

I think it was when I started seeing how powerful data could be in solving real-world problems. I’ve always been interested in working with numbers and solving problems, and data science seemed like a natural fit. I started looking into how machine learning models work and how data could drive business decisions. That’s when I decided to make the leap into data science.

Q3: When you decided to switch careers, what was the first thing you did to start your learning process?

The first step was to get familiar with Python programming, as it's one of the most widely used languages in data science. I also began learning machine learning algorithms, statistics, and data visualization. I started with some online courses, and platforms like Kaggle were incredibly useful for applying what I learned in real-life projects.

Q4: How did you manage to improve your skills and build a solid foundation?

Consistency was key. I practiced coding every day, even if it was just for an hour or two. I worked on different datasets, participated in Kaggle competitions, and slowly started to understand how to apply machine learning techniques to solve problems. One of the biggest takeaways was to never stop learning. There’s always something new to learn in data science, and you have to keep adapting.

Q5: How did you approach your capstone projects during the course?

Working on hands-on projects was key to developing my practical skills, especially in data cleaning, feature engineering, and model deployment. Each project taught me valuable lessons and deepened my understanding of the real-world application of data science. I completed three capstone projects, spending around 15-20 days on each. My focus was not just on completing them, but on understanding the reasoning behind the models, ensuring I approached problems methodically and not just applied code blindly.

Q6: Did your projects help in interviews?

Absolutely. In fact, the projects I worked on were related to what I’m doing now. I could explain how I used machine learning in real applications, and that’s what they were interested in—my thought process behind the algorithms.

Q7: What kinds of ML models or tools did you use in your projects?

I worked with regression, classification, and even some ensemble models. I used Python, of course, and some visualization tools. I didn’t just stick to what was in the curriculum—I explored extra techniques when needed.

Q8: When it came to preparing for interviews, did you face any challenges?

Yes, the interview process was definitely challenging, but it was a great way to test my skills. I remember the first technical interview I attended—it was a bit overwhelming. I was asked a lot about data science concepts, machine learning, and even SQL. It was tough because I wasn’t fully prepared for all the technical questions, especially related to deployment. But I learned from it and took it as a learning experience to improve my knowledge in those areas.

Q9: What steps did you take to prepare for the interviews after that first experience?

After that first interview, I realized I needed to focus more on deployment and cloud platforms, which I hadn’t initially paid much attention to. I also made sure to brush up on SQL—it’s a critical skill for working with large datasets. I practiced answering questions on machine learning algorithms, data manipulation, and model evaluation metrics like accuracy, precision, and recall. I also made sure to practice coding challenges on platforms like LeetCode and HackerRank to improve my problem-solving skills.

Q10: What were some of the technical questions asked?

One round had a question where I had to calculate accuracy manually from a problem statement. I also got a logic-based Python question where I had to write code on the spot. It wasn’t just about getting the right answer—they were looking for clarity of thought.

Q11: Was there any SQL or deep learning involved in the interview?

Yes, SQL did come up. Some basic queries, joins, filtering data… that kind of stuff. Deep learning wasn’t the main focus, but they touched on it a little. They were more focused on how well I understood the fundamentals of ML and if I could explain my reasoning.

Q12: How long did it take to get this job after finishing your coursework?

After my interviews, it took about two to three weeks to receive the offer. It was an exciting moment for me, as I had worked so hard for this opportunity. I was offered a role as a Data Scientist at Trinity Mobility, where I now apply my skills to real-world projects. It’s been a great experience so far, and I continue learning every day.

Q13: Did mock interviews help in your preparation?

Definitely. My first interview was a failure—I didn’t know deployment well enough at the time. But after each mock or real interview, I sat down and asked myself, “What went wrong? What didn’t I know?” That helped me fill gaps.

Q14: Can you give examples of concepts you improved after interviews?

After one interview, I realized I didn’t know dictionary comprehensions well. I had only used list comprehensions. So I practiced those.In another interview, I was tasked with working on strings without utilizing any built-in functions.That was tough, but I learned to write logic from scratch after that.

Every rejection gave me a specific concept to improve. I used that to build my skills, not lose confidence.

Q15: How did you handle logic-based questions?

The key is to understand the problem step-by-step. Don’t panic. Think out loud if needed. And definitely practice questions from previous mock interviews. They prepare you well.

Q16: What advice would you give to someone looking to transition into data science?

My biggest advice is to stay committed and keep learning.Data science is a dynamic field that continuously evolves, offering endless opportunities to learn and grow. Don’t get discouraged by initial setbacks, as they are part of the learning process. Work on real projects, engage with the community, and practice coding and problem-solving every day. Most importantly, believe in yourself and your ability to succeed in this field.

Refer these articles:

• Partha’s Data Science Journey: From Beginner to Data Scientist
• Neelam Jaswanth’s Journey to Success in Data Science 
• From BPO to Data Scientist: Babul Kumar’s Inspirational Journey

Essential Insights from Manohar's Transition to Data Science

Manohar's journey is a testament to the power of determination, hands-on practice, and leveraging the right resources to successfully pivot into the world of data science.

• Background in Mechanical Engineering: Manohara initially pursued a career in mechanical engineering but later shifted his focus to data science due to his growing interest in the field.
• Decision to Transition: The decision to transition to data science was driven by Manohar’s interest in technology and analytics, as well as the potential of data science in solving real-world problems.
• Importance of Continuous Learning: Manohara emphasizes the importance of continuous learning. He took up various online courses and resources to build his skills in data science.
• Hands-On Practice: To solidify his learning, Manohara focused on practical application, working on several data science projects to gain hands-on experience.
• Key Focus Areas in Preparation: During his preparation, Manohara concentrated on crucial topics like Python programming, machine learning algorithms, data structures, and SQL. He also practiced coding challenges and problem-solving techniques.
• Mock Interviews and Challenges: Manohara faced several mock interviews and challenges, which helped him understand industry expectations and improve his skills. These mock interviews exposed him to real-world questions and helped him prepare for difficult scenarios.
• Learning from Mistakes: Manohara stresses that learning from mistakes was a key part of his journey. He acknowledges that each interview and project taught him valuable lessons that helped him improve over time.
• Securing a Full-Time Role: Manohar's perseverance paid off when he secured a full-time role at Trinity Mobility, after completing his data science training. This marked the successful culmination of his transition into data science.
• Importance of Problem-Solving: A key takeaway from Manohar’s journey is the emphasis on problem-solving. He not only focused on mastering theory but also concentrated on solving real-world data challenges to build a strong portfolio.
• The Role of Persistence and Self-Reflection: Manohar's story highlights how persistence, self-reflection, and actively working on improving after each failure can lead to eventual success in data science.

Manohar’s journey from Mechanical Engineer to Data Scientist highlights the power of dedication, hard work, and a commitment to continuous learning. By making strategic choices in his education, working on real-world projects, and preparing meticulously for interviews, he was able to successfully transition into the data science field.

Refer these articles:

• Why Data Scientist Career in Pune
• Data Science Careers in Ahmedabad
• Data Science Careers in Coimbatore

If you’re thinking about transitioning into data science, now is an ideal time to sharpen your skills. According to a report by Grand View Research, the global data science platform market was valued at USD 96.25 billion in 2023 and is expected to grow at a CAGR of 26.0% from 2024 to 2030. The rapid expansion of data generated through digital activities is fueling this growth. To stay ahead in this dynamic field, it’s essential to select a training institute that provides practical learning, internships, and strong placement assistance. Explore best data science institute in Pune, Bangalore, Hyderabad, Chennai, Ahmedabad, Coimbatore, Mumbai, and Delhi, to give your career a strong start.

DataMites Institute stands out as a premier provider of training in Data Science, AI, Machine Learning, Python Development, and Data Analytics. With accreditation from IABAC and NASSCOM FutureSkills, DataMites provides expert-led training, real-world projects, and dedicated placement assistance, ensuring smooth transitions into data science careers.

DataMites provides offline Data Science courses in Bangalore, Hyderabad, Chennai, Pune, Ahmedabad, Coimbatore, Mumbai, and other top cities, along with online learning options to cater to your preferences. With a strong emphasis on practical experience and industry-relevant skills, DataMites empowers fresh graduates, working professionals, and career changers with the expertise needed to excel in the fields of Data Science, AI, and Machine Learning.

Manohar’s story is a testament to the fact that with the right mindset and approach, career transitions can be both achievable and rewarding. If you’re thinking about a career in data science, take inspiration from Manohara, embrace learning, build a strong portfolio, and continue striving to improve.