Ethan’s journey from a mechanical engineering background to a successful data scientist is a powerful example of how dedication, upskilling, and the right training can transform a career—even without prior programming experience. His story highlights the importance of perseverance, hands-on projects, and a growth mindset for anyone looking to break into the field of data science.
Q&A: Ethan’s Data Science Journey
Q1: Can you briefly introduce yourself?
I’m Ethan. I have a degree in Mechanical Engineering and worked in that field for over four years. Wanting more growth, I decided to transition into data science and AI. After researching my options, I enrolled in CCI’s (Center of Computer Intelligence) six-month program to make the switch.
Q2: How did you approach this career change, considering your background?
I had no programming background, so I knew it would be tough. I chose CCI because of its clear learning path and strong support system. The hands-on projects and internship gave me the preparation I needed for the industry.
Q3: Where are you working currently and in what role?
I’m now an Associate Data Scientist at DataFlow Group, working on projects involving AI, OCR, and NLP.
Q4: How was programming for you at the beginning?
I didn’t even know how to write a basic “Hello World” program at first. It was challenging, but I focused on developing analytical thinking and logical reasoning, which are just as important as coding in data science.
Q5: What kinds of projects have you worked on at your current job?
I’ve worked on NLP-based projects, including resume parsing for HR and document forgery detection using OCR and NLP.
Q6: Did you pursue the AI Expert program as well?
No, I completed the Certified Data Scientist (CDS) program. My learning on LLMs and advanced NLP has been self-driven.
Q7: Was statistics a new subject for you?
Mostly, yes. I learned the practical statistical concepts I use today through CCI’s industry-focused curriculum.
Q8: How relevant was the statistics portion of your course to real-world work?
Extremely relevant. The syllabus focused on what’s actually useful in industry scenarios.
Q9: What tools and skills are essential for aspiring data scientists today?
Beyond programming, you need to understand NLP, computer vision, SQL, and BI tools. Logical thinking and conceptual knowledge are crucial for solving business problems.
Q10: How often do you use SQL at work?
Almost daily. SQL is fundamental for working with databases and handling data.
Q11: How valuable was the mock interview experience?
Very valuable. My first few mock exams weren’t great, but constructive feedback helped me improve and prepare for real interviews.
Q12: What was your study pattern like?
My routine wasn’t always consistent, but I never missed a class and always made detailed notes, which I still use before interviews.
Q13: Is daily study necessary to succeed in this field?
Not necessarily. Consistent effort and thorough notes are more important than daily study marathons.
Q14: How important is conceptual thinking in data science?
Extremely important. Programming can be improved over time, but core analytical thinking is essential for real-world problem-solving.
Q15: Have you worked on any white papers or research projects?
Yes, I authored a white paper on generative AI, exploring models that help online teachers gauge student engagement in virtual classes.
Q16: Is publishing a paper beneficial for job seekers?
Absolutely. It adds credibility and demonstrates strong subject knowledge.
Q17: How valuable is an internship after completing the course?
Invaluable. Internships allow you to implement what you’ve learned and give you a real edge in interviews.
Q18: What advice would you give someone from a non-technical background who wants to transition into data science?
It’s possible. Your domain knowledge is valuable. Focus on programming, statistics, and problem-solving. Internships and projects provide essential hands-on experience.
Q19: What kind of projects should learners focus on?
Projects that solve real-world problems—resume parsing, document verification, sentiment analysis. Cover NLP, computer vision, machine learning, and basic AI.
Q20: What are your future plans in data science?
I’m passionate about NLP and plan to focus on projects and learning more about large language models.
Q21: What should I do if I get stuck or fail in an interview?
Reflect on what went wrong, identify gaps, and work on them. Every interview is a learning opportunity.
Q22: Is not getting selected in an interview a sign of failure?
Not at all. Many factors affect interview outcomes. Rejection does not mean you’re not good enough.
Q23: How important is consistency in interview preparation?
Very important. Keep trying and don’t quit—consistent learning and improvement are key.
Q24: What should candidates focus on when preparing for interviews?
Continuously improve, work on weak areas, and learn from feedback.
Q25: Does success in interviews depend only on preparation?
No, success also depends on alignment with the company’s needs, not just preparation.
Q26: Any advice for those currently transitioning into new roles or careers?
Keep learning, stay motivated, and don’t see rejections as failures. Persistence and continuous improvement will eventually lead to the right opportunity.
Insights from Ethan’s Journey
- Transitioned from mechanical engineering with four years of experience to data science.
- Chose CCI for structured learning, hands-on projects, and internship experience.
- Developed programming skills from scratch, focusing on logical and analytical thinking.
- Completed capstone projects and an internship to bridge theory and practice.
- Landed a role as Associate Data Scientist, working on AI, OCR, and NLP projects.
- Uses SQL, Python, and Power BI daily—skills gained directly from CCI’s curriculum.
- Embraced feedback from mock interviews and used setbacks as learning opportunities.
- Authored a white paper on generative AI and pursued self-learning in advanced NLP.
- Maintained detailed notes and a disciplined, if not daily, study approach.
- Encourages non-tech professionals to enter data science with determination and effort.
- Plans to deepen expertise in NLP and generative AI for future career growth.
Ethan’s journey proves that with structured learning, resilience, and strategic effort, anyone can pivot into data science. For aspiring data scientists, especially those from non-coding backgrounds, his story offers inspiration and a clear roadmap for success.
