How Bhavya Made a Successful Leap into Artificial Intelligence

What if the career you studied for wasn't the one you were destined to pursue? For Bhavya, a skilled mechanical engineer by training, that moment of realization sparked not fear but opportunity. In a world rapidly evolving through automation and machine learning, he made the bold decision to trade spanners and schematics for neural networks and data pipelines.

This is not just a story of switching careers, it's a blueprint for anyone dreaming of breaking into the world of artificial intelligence. If you've ever wondered whether it’s too late to start over or doubted your ability to thrive in tech without a computer science degree, Bhavya’s journey will challenge those beliefs and inspire your next move.

Bhavya’s Successful Transition to Data Science with DataMites

Check out how Bhavya made the leap into data science, tackled the ups and downs, and finally landed his dream job all with the help of DataMites training and support.

Q1: Can you introduce yourself and share your professional background before transitioning to data science?

I completed my B.Tech in Mechanical Engineering from Nagpur University and pursued a postgraduate degree in management from Loyola College, Chennai. Subsequently, I worked in the material handling industry within the power sector, focusing on supply chain and project management.​

Q2: What motivated you to shift from mechanical engineering to data science after 14 years in the industry?

While I was performing well in my mechanical engineering role, I observed a paradigm shift towards emerging technologies like blockchain, artificial intelligence (AI), and machine learning (ML). The mechanical field began to feel monotonous, and I sought more challenging opportunities. Recognizing the promising future of AI and ML, I decided to pivot towards Machine Learning data science.​

Q3: How did you choose DataMites for your data science training?

I evaluated several institutions based on their curriculum relevance, training quality, and placement assistance. DataMites met all these criteria, offering comprehensive training aligned with current industry demands and robust placement support.​

Q4: What was your learning experience like at DataMites?

I enrolled in the September 2021 online batch under the mentorship of Deepa Mam. The training was well-structured, with Deepa Mam providing personalized support and addressing individual student needs. The program included practical assignments and projects, enhancing my understanding of data science concepts.​

Q5: How did you find learning Python, given your non-programming background?

Initially, learning Python was challenging due to my limited programming experience. However, with consistent practice and guidance from my mentors, I became proficient enough to develop various machine learning models. It took me approximately two months of dedicated effort to gain confidence in Python.​

Q6: Can you tell us a bit about your background before transitioning into data science?

I come from a mechanical engineering background, where I developed strong problem-solving, analytical, and decision-making skills. Although I was performing well in my field, I saw greater growth opportunities in data science. My curiosity about how companies leverage data for decision-making led me to explore this field as a career.

I completed my data science training in May 2022 and secured a Data Scientist position within two months at Técnicas Reunidas, a Spanish-based company. DataMites’ placement support helped me land interviews quickly, and I successfully cleared my first interview, which primarily focused on AI concepts, machine learning tools, and project experience rather than live coding. While soft skills play a role, I believe that a strong learning attitude and teamwork are even more crucial in securing a data science role

Q7: What advice would you give to professionals considering a career switch to data science?

Transitioning to data science requires a strong foundation in mathematics and statistics, as well as proficiency in programming languages like Python. It's essential to choose a reputable training institute that offers practical learning experiences and placement support. Continuous learning and staying updated with industry trends are also crucial for success in this field.​

Q8: What challenges did you face during your transition, and how did you overcome them?

One of the main challenges was adapting to a new field that required different skill sets. To overcome this, I dedicated extra time to practice coding and worked on real-world projects to build practical experience. The support from my mentors and peers at DataMites also played a significant role in easing the transition.​

Q9: How has your background in mechanical engineering contributed to your role in data science?

My mechanical engineering background provided me with strong analytical and problem-solving skills, which are invaluable in data science. The mathematical and statistical knowledge I gained during my engineering studies has also been beneficial in understanding data science algorithms and models.​

Q10: What inspired you to choose data science? Why did you switch careers?

There were several reasons why I decided to switch:

• Data Science is the Future – Every industry is now using data-driven decision-making, so I wanted to be part of this growing field.
• High Demand & Salary – The demand for data scientists is increasing, and companies offer competitive salaries.
• Problem-Solving Excites Me – I love working with numbers, patterns, and finding insights, and data science allows me to do that.

I realized that a career in data science would give me both job security and intellectual satisfaction.

Q11: How did you start learning data science? What was your learning path?

Initially, I started with self-learning—watching YouTube tutorials, reading blogs, and taking free courses on Coursera. But I quickly realized that self-learning alone wasn’t enough.

To get structured guidance, I joined DataMites’ Data Science Course. This helped me:

• Learn Python, Machine Learning, AI, and Statistics in a structured manner.
• Work on real-world projects to apply my knowledge.
• Get mentorship from industry experts to clear my doubts.
• Receive placement assistance to prepare for job interviews.

This structured approach gave me the confidence and skills to apply for data science roles.

Q12: What were the biggest challenges you faced while switching to data science?

• Bridging the gap between theory and practice – Learning algorithms is one thing, but applying them to solve real-world problems was challenging.
• Competition in the job market – Many professionals are moving into data science, so standing out requires a strong portfolio, certifications, and networking.
• Balancing learning with a full-time job – It was difficult to manage my time between work, learning, and projects, but staying consistent helped.

Despite these challenges, I kept pushing forward, and eventually, I landed my first data science job.

Q13: How did DataMites help in your transition?

DataMites played a huge role in my success. Their course provided:

• Structured Learning Path: Covering Python, SQL, Machine Learning, Deep Learning, and AI.
• Live Projects & Case Studies: These helped me gain practical experience in solving real-world problems.
• Industry Expert Mentors: I received guidance from professionals who are actually working in the field.
• Mock Interviews & Resume Building: DataMites helped me prepare for technical interviews and improve my resume and LinkedIn profile.

Without this structured support, my journey would have been much harder.

Q14: Can you share a project that helped in your career transition?

One of the key projects that helped me land a job was a Sales Prediction Model.

• Project Overview: I analyzed historical sales data and built a machine learning model to predict future sales trends.
• Skills Used: Python, Pandas, Scikit-Learn, Data Visualization
• Outcome: The model helped businesses optimize inventory management and reduce costs.

This project was a game-changer because it showed employers that I could apply data science concepts to real business problems.

Q15: How did you prepare for data science job interviews?

I followed a structured preparation plan:

• Revised Core Concepts – Focused on Python, SQL, Statistics, Machine Learning, and Data Visualization.
• Solved Coding Challenges – Practiced problems on LeetCode and Kaggle to improve my problem-solving skills.
• Worked on Projects – Created multiple real-world projects to showcase in my resume.
• Mock Interviews – DataMites conducted mock interviews to improve my confidence.

By following this strategy, I was well-prepared to answer both technical and business-related questions in my interviews.

Q16: How important is networking in getting a data science job?

Networking is crucial. I actively used:

• LinkedIn – I connected with data scientists, recruiters, and industry professionals.
• Data Science Meetups & Webinars – Attended online and offline events to expand my knowledge.
• GitHub & Kaggle – Shared my projects to showcase my skills.

Because of networking, I got referrals and interview opportunities that I wouldn’t have found otherwise.

Q17: What advice would you give to aspiring data scientists?

My top advice for beginners:

• Start with the basics – Learn Python, SQL, and Statistics before moving to advanced topics.
• Work on real projects – Theory alone won’t help; apply your knowledge to solve real problems.
• Be patient and persistent – The transition takes time, but don’t give up.
• Build a strong online presence – Post your projects on LinkedIn, Kaggle, and GitHub.
• Stay updated – Follow industry trends, read blogs, and participate in competitions.

The key is consistent learning and application.

Q18: What are your future aspirations in the field of data science?

I aim to specialize further in artificial intelligence and machine learning, contributing to innovative projects that have a significant impact. I also plan to mentor aspiring data scientists, sharing my experiences and guiding them through their career transitions.​

Bhavya's story underscores the importance of adaptability and continuous learning in today's ever-evolving technological landscape. His successful transition from mechanical engineering to data science serves as an inspiration for professionals seeking to pivot their careers towards emerging fields.

I believe continuous learning is the key to long-term success in data science.

Refer to this article:

From Engineer to ML Engineer: Rahul Thota’s Inspiring Journey

Final Thoughts: Bhavya’s Key Takeaways for Data Science Aspirants

Structured Learning is Essential – Enroll in a structured course like DataMites to learn efficiently.

• Projects Matter – Work on hands-on projects to build a strong portfolio.
• Network & Engage – Connect with professionals, join meetups, and participate in competitions.
• Never Stop Learning – The field is evolving, so keep updating your skills.
• Strong Motivation for Transition – Bhavya was driven by his interest in data analytics and the growing impact of data-driven decision-making.
• Skill Development Through Courses – He pursued structured learning in statistics, Python, machine learning, and data visualization to build a solid foundation.
• Hands-on Learning is Crucial – Practical projects helped him apply concepts to real-world scenarios, enhancing his expertise.
• Challenges & Overcoming Them – He faced difficulties with complex topics and evolving technologies but overcame them through discipline, mentorship, and community engagement.
• Networking Opened Opportunities – By attending conferences and workshops, he connected with industry professionals, which helped him transition smoothly.
• Continuous Learning is Key – Bhavya advises aspiring data scientists to stay updated with trends, focus on real-world projects, and develop a problem-solving mindset.

Are you ready to start your data science journey? Join DataMites today and take your career to the next level.

Refer these articles:

• Is There a Growing Demand for Artificial Intelligence in Mumbai?
• Is the Demand for Artificial Intelligence Still High in Coimbatore?
• Is the Demand for Artificial Intelligence Still High in Chennai?

Bhavya’s journey proves that anyone can transition into data science with the right training, persistence, and hands-on practice. If you are considering a career shift, start today and take your first step toward becoming a successful AI Engineer.

If you're looking to transition into Artificial Intelligence, now is the perfect time to upskill. According to a Statista report, The market for artificial intelligence grew beyond 184 billion U.S. dollars in 2024, a considerable jump of nearly 50 billion compared to 2023. This staggering growth is expected to continue with the market racing past 826 billion U.S. dollars in 2030. To stay competitive, choose an institute that offers hands-on training, internships, and placement support. Enroll in the best artificial intelligence courses in Bangalore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Ahmedabad, and other major cities to kick-start your career.

DataMites Training Institute is a premier institute offering specialized courses in Data Science, AI, Machine Learning, Python Development, and Data Analytics. Accredited by IABAC and NASSCOM FutureSkills, DataMites provides expert-led training, real-world projects, and comprehensive placement assistance to help professionals successfully transition into data science roles.

With offline artificial intelligence training in Chennai, Hyderabad, Bangalore, Pune, Ahmedabad, Coimbatore, and Mumbai, DataMites equips learners with practical experience and industry-relevant skills. Whether you're a fresh graduate, a working professional, or someone seeking a career change, DataMites offers a structured and effective pathway into the world of Data Science, AI, and Machine Learning.