From Beginner to Expert: Sunil Kumar's Data Science Journey

In the age of digital transformation, data science stands tall as one of the most lucrative and rapidly growing fields. However, diving into this domain isn't always straightforward, especially for those from non-technical backgrounds. Gayam Sunil Kumar's journey is an inspiring tale of persistence, self-belief, and smart learning strategies. From handling messy datasets to cracking interviews, Sunil's story is a masterclass for aspiring data professionals.

In this in-depth interview, facilitated by DataMites, Sunil opens up about the real challenges, strategies, and breakthroughs he experienced while transitioning into a successful data science career. Let us explore this transformative journey through a detailed Q&A session.

Sunil Kumar's Path to Data Science: A Success Story with DataMites

With unwavering determination and the right guidance from DataMites, Sunil Kumar successfully transformed his passion for data into a growing career in Data Science.

Q1: Can you tell us about your background before entering the data science field?

I come from an engineering background. I studied Electronics and Communication Engineering (ECE). Although my initial work was more in the technical core areas, my interest in data started building up when I noticed how much data-driven decision-making was transforming industries.

Q2: So when did you first realize you wanted to transition into Data Science?

I was working and exploring different technologies, and I realized that Data Science had a huge potential. The way data can be used to solve real-world problems fascinated me. I wanted to be a part of that revolution. That's when I seriously started planning my transition.

Q3: What made you choose DataMites as your training partner?

I did a lot of research. What stood out with DataMites institute was their practical curriculum, the hands-on projects, and the mentorship they offered. I wasn't just looking for theoretical knowledge; I wanted to apply what I learned, and DataMites gave me exactly that.

Q4: What was your experience like with your first capstone project?

It was a mix of excitement and struggle. The data was messy and unstructured. I spent over 70% of my time cleaning and preparing the data. This project taught me that data preprocessing is crucial and often the most time-consuming part.

Q5: What tools and technologies did you primarily focus on?

I concentrated on Python for programming, Pandas and NumPy for data manipulation, SQL for querying databases, and Scikit-learn for machine learning. I also became comfortable with Excel and Power BI for dashboarding and reporting.

Q6: How did you balance theory and practical skills?

Initially, I focused more on practical coding through platforms like HackerRank and Kaggle. As I became comfortable, I revisited theoretical concepts, especially in statistics and machine learning, to strengthen my conceptual understanding.

Q7: Did you face any challenges during your learning? How did you overcome them?

Yes, plenty. I struggled with SQL joins and statistical concepts in the beginning. I overcame this by watching videos multiple times, practicing daily, and regularly interacting with mentors at DataMites who clarified my doubts.

Q8: Can you share how your batch progressed during the course?

We started with 40 students. As time went on, only a handful of us remained consistent. Many gave up due to the workload or lack of motivation. I made a promise to myself to stick through it, and that made all the difference.

Q9: What was the most valuable lesson you learned from DataMites?

The value of consistency. Learning a new field requires patience and daily effort. Also, the way DataMites emphasizes real-world projects really prepared me for job responsibilities.

Q10: How important is SQL for aspiring data analysts?

Extremely important. Most of the data lies in relational databases. You should be able to perform joins, aggregate data, and use window functions. Mastering SQL gave me a lot of confidence in interviews.

Q11: What’s your advice for handling real-world datasets?

Don’t expect clean datasets. Real-world data will have missing values, duplicates, and inconsistent formats. Be ready to spend significant time on data cleaning. That’s where your problem-solving skills will shine.

Q12: How did you approach machine learning models during your course?

I first focused on understanding the intuition behind each model, like how KNN uses distance or how decision trees split data. Only after that did I start coding the models using Scikit-learn.

Q13: Were there any particular resources that helped you during your preparation?

Yes, I used W3Resource for SQL, HackerRank for Python, and Krish Naik’s YouTube channel for machine learning concepts. DataMites' LMS was also an excellent support system.

Q14: How did you prepare for job interviews?

I created a study schedule focusing on mock interviews, coding challenges, and revising statistics and ML concepts. I also practiced explaining my capstone projects clearly, focusing on business problems and outcomes.

Q15: What role did DataMites play in your job search and placement?

They played a huge role. From refining my resume to conducting mock interviews and connecting me with opportunities, their support was constant. My relationship manager Jessie was incredibly helpful throughout.

Q16: Any tips for cracking the technical rounds in interviews?

Practice coding daily. Know how to use Pandas to replicate SQL queries. Be confident while explaining your thought process. Most importantly, don’t fake what you don’t know—honesty goes a long way.

Q17: So how did you land your role at Sirpi DataScience?

After completing my course, I applied to multiple companies. Thanks to the placement support from DataMites, I got interview opportunities. During the interview with Sirpi DataScience, I could clearly explain my projects, my approach to solving problems, and my eagerness to continue learning. That made a strong impression.

Q18: What is your current role and what kind of projects do you work on now?

As a Data Scientist at Sirpi DataScience, I work on machine learning projects, building models to solve business problems. It encompasses analyzing data, building models, deploying them into production, and continually refining them for better performance. It’s a very dynamic role, and every project teaches me something new.

Q19: How did you stay motivated during tough times?

I reminded myself of why I started. I also followed success stories from DataMites alumni, which kept me going. Surrounding myself with peers who shared the same goals helped a lot.

Q20: What is your final message to all aspiring data scientists?

Sunil: Don’t give up. Stay curious and keep practicing. Focus on building projects, not just collecting certificates. If I can make it from a non-tech background, so can you.

Refer these articles:

• Harish Kumar's Journey: A Roadmap to Data Science Success
• From Non-Tech to Data Science: Pavan's Career Transition
• Shivaji's Transition from Mechanical Engineering to Data Science

What We Can Learn from Sunil Kumar’s Data Science Breakthrough

Sunil Kumar’s inspiring journey highlights the power of structured learning, relentless practice, and staying adaptable in a constantly evolving field like Data Science.

• Follow Your Passion: Gayam’s journey shows that genuine interest in data fuels success. When you’re passionate, learning becomes engaging and motivating.
• Continuous Learning: Transitioning to data science requires dedication. Combining online courses, practical projects, and structured training (like DataMites) can build a strong skill set.
• Hands-On Projects: Building real-world projects was crucial. They reinforced Gayam’s learning and demonstrated his skills to employers.
• Leverage Your Background: Gayam leveraged his engineering background as a strength. Analytical skills from past experiences gave him a head start in data science.
• Persistence Matters: Consistency and hard work helped Gayam overcome challenges. He balanced work and study, and kept practicing even when learning was tough.
• Seek Mentorship and Community: Engaging with mentors and peers provided guidance and motivation. Having a support network helped him solve difficult problems and stay on track.
• Develop Communication Skills: Explaining data insights clearly is as important as technical skill. Gayam practiced presenting his work effectively, which helped him in interviews and will help in his career.
• Growth Mindset: Gayam remains curious and continually learns new tools and techniques. In the fast-changing world of data science, a commitment to ongoing learning and growth is essential for success

Gayam Sunil Kumar's story is more than just a success tale—it’s a roadmap for thousands of others aspiring to enter the world of data. His journey reminds us that success in data science isn't reserved for the privileged or the inherently tech-savvy; it's for the persistent, the curious, and the hardworking.

Refer these articles:

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

Sunil Kumar's remarkable journey highlights how curiosity, determination, and perseverance can drive a successful transition into the field of data science. With the global data science platform market projected to grow from USD 150.73 billion in 2024 to approximately USD 676.51 billion by 2034 at a CAGR of 16.20%, as per Precedence Research, now is an ideal time to build a career in this growing industry. For aspiring data scientists, enrolling in data science courses in Chennai, Hyderabad, Bangalore, Pune, Mumbai, and Delhi, can offer a strong foundation and a competitive advantage.

Choosing the right institute is crucial to growing in this rapidly evolving sector. DataMites is recognized for offering expert-led training programs across key fields such as Data Science, Machine Learning, Artificial Intelligence, Python Programming, and Data Analytics. Accredited by IABAC and NASSCOM FutureSkills, DataMites delivers an industry-relevant curriculum led by experienced professionals.

With a strong focus on practical learning, the institute offers real-time projects, internship opportunities, and dedicated placement support—ensuring students gain not only theoretical knowledge but also valuable industry exposure. DataMites provides both online and offline data science training in Bangalore, Hyderabad, Chennai, Pune, Ahmedabad, Coimbatore, and Mumbai.

Through expert mentorship, hands-on training, and a strong track record of placements, DataMites empowers freshers, working professionals, and career switchers alike to build a successful future in Data Science, Artificial Intelligence, and Machine Learning.

With structured learning platforms like DataMites and inspiring examples like Sunil Kumar’s journey from a beginner to an industry professional, the dream of a rewarding data science career is more accessible than ever. Let his journey inspire you to take that courageous first step—and stay committed until you turn your aspirations into reality.