From Civil Engineering to Data Science: Prasad’s Inspiring Journey

Transitioning from a non-technical background into data science is no easy feat—but with the right mindset, guidance, and dedication, it’s entirely possible. Prasad Har’s journey is a testament to this. Originally a civil engineer from a small town near Goa, Prasad is now a junior data scientist at Findability Sciences in Mumbai. His path was paved with challenges, perseverance, and a transformational learning experience at DataMites.

We delve into Prasad’s story to understand how he broke into the world of data science. Through a candid conversation with the DataMites team, Prasad opens up about his learning experience, interview preparation, and the continuous effort that went into shaping a successful data science career.

How Prasad Dharne Made the Leap into Data Science with DataMites

With a passion for analytics and a drive to upskill, Prasad transformed his career journey through DataMites training institute comprehensive data science program.

Q1: What was your background before venturing into data science?

Before venturing into data science, I held a degree in Civil Engineering. However, my strong interest in data, problem-solving, and analytics eventually led me to explore the field of data science.

Q2: You were with DataMites in June 2022. What’s life been like since then?

It’s been an exciting journey. I’m currently working as a Junior Data Scientist at Findability Sciences in Mumbai. My work primarily involves time series forecasting for demand planning. I joined as a Data Analyst Intern, and after six months, I was converted into a full-time junior data scientist.

Q3: Can you tell us more about your current role?

I work with a team of 14 people. Our main tech stack is Python, and we use libraries like pandas extensively. We also implement CI/CD pipelines using Jenkins, and there’s a bit of MLOps work as well.

One of our key clients is an electronics manufacturing company. My job involves using data to predict future demand, helping these companies plan better. It's deeply rewarding.

Q4: Can you walk us through your interview process at Findability?

I found the company by researching job openings. I checked their website, understood their focus on demand forecasting, and brushed up on time series analysis basics. However, the actual interview wasn’t too technical on time series.

They mainly asked about:

• Python: List functions, data structures
• Logical questions
• SQL and Statistics
• My projects, especially one involving healthcare data

Interestingly, they didn’t ask me to code live, but the interviewer was keen on understanding how I approached problem-solving in my projects.

Q5: Was there only one interview round?

Yes, since it was for an intern position initially. After joining in May, I worked for a few months and then was confirmed as a full-time employee by November 2023.

Q5: What was your learning journey like with us?

I come from a civil engineering background, so Python was completely new to me. Initially, it was difficult, but the structured learning at DataMites helped a lot.

Nikhil Sir, our mentor, played a big role. His classes were not only conceptual but hands-on. I used to:

• Study basic Python and SQL from books and YouTube
• Attend the live classes for reinforcement
• Solve DataMites-provided practice problems

I also spent a lot of time practicing outside the classroom, which helped strengthen my foundation.

Q6: How did you approach job applications?

I was very methodical. I applied to 10–20 jobs a day and tweaked my resume every single time to match the job description.

I used tools to extract keywords from job listings and aligned my resume to be ATS-friendly. It made a huge difference.

Q7: What other resources did you use beyond our curriculum?

A lot! Here are some of my go-to resources:

• HackerRank – For Python and SQL practice
• W3Resources – SQL exercises (different from W3Schools)
• GitHub Repositories – Especially for pandas mastery
• CampusX YouTube Channel – For machine learning and deep learning
• Papers with Code – To stay up to date with state-of-the-art models in NLP, CV, ML
• Medium – For practical insights and tutorials

These platforms complemented what I learned at DataMites and helped me bridge the gap between theory and industry applications.

Q8: For someone from Pharmacy or another non-tech background, what advice would you give?

Firstly, don’t be intimidated by the tech jargon. Start slow.

• Learn Python deeply—it's foundational.
• Focus on SQL, Statistics, and Data Manipulation (Pandas) early on.
• Make sure to do projects—they speak louder than certificates in interviews.
• Practice daily and use mock interviews (like those at DataMites) to identify your weak points.
• Internships are a great way to gain entry; don’t be afraid to start there.

And lastly, be consistent. Even if you're starting from scratch, you can do it with daily effort.

Q9: How did DataMites help you in your career?

DataMites played a significant role in my journey. The training, particularly the hands-on projects and real-world applications, were invaluable. The mock interviews and profile-building sessions were crucial in preparing me for the job market. Nikhil sir, our mentor, taught us Python and machine learning concepts in great detail, which helped me build the confidence I needed for interviews and real-life projects.

Q10: What were some challenges you faced during the DataMites course, and how did you overcome them?

The main challenge was initially grasping the basics of Python, as I had no prior experience. However, through constant practice and engaging with the DataMites community, I was able to overcome these hurdles. The course provided a strong foundation, and the additional resources and projects helped me gain practical experience.

Q11: How did you manage your time while studying and preparing for interviews?

I dedicated time each day to learning and practicing. I ensured I completed all course assignments, followed up on projects, and also applied for jobs regularly. I used tools like resume optimization software to tailor my applications and increase my chances of landing interviews.

Q12: What role did mock interviews play in your preparation?

Mock interviews were extremely helpful in building my confidence and preparing for real interviews. They gave me a sense of the types of questions I would face, both technical and behavioral, and helped me refine my responses. The feedback from mock interviews allowed me to focus on areas where I needed improvement.

Q13: Can you briefly describe some of the data science projects you’ve worked on?

Certainly. I’ve worked on four key projects:

• Liver Patient Identification – A binary classification problem using clinical data to predict whether a person is a liver patient or not.
• Home Loan Default Risk Management – This project focused on predicting loan defaults to help financial institutions minimize risk, using supervised learning models like logistic regression and decision trees.
• Teacher Performance Evaluation – A multiclass classification task where we categorized teachers into performance bands based on various metrics like student feedback, grades, and attendance.
• Health Form Prediction (ongoing) – This is related to analyzing health-related form data for predictions, though this one is still under development.

Each project helped me build skills in data preprocessing, feature selection, model training, evaluation, and visualization.

Q14: How long did it take for you to feel confident in your skills?

Roughly 4-5 months of consistent effort and project practice made me confident enough to start applying for jobs.

Q15: What are your final thoughts on the data science journey?

It’s not easy, but it’s doable with the right mindset. Leverage training platforms like DataMites, build real-world skills, and keep learning even after you land your first job.

Refer these articles:

• Komal’s Path to Data Science: A Journey of Growth and Success
• From Beginner to Expert: Sunil Kumar's Data Science Journey
• Harish Kumar's Journey: A Roadmap to Data Science Success

How Prasad Dharne Achieved Success in Data Science: Key Lessons

Discover the strategies, mindset, and skills that propelled Prasad Dharne to the forefront of the data science field.

• Prasad transitioned from a civil engineering background to data science, showcasing that non-CS graduates can succeed with the right training and persistence.
• He enrolled in DataMites in June 2022 and completed the course towards the end of that year.
• Secured his first role as a Data Analyst Intern at Findability Sciences in Mumbai, which later converted to a full-time Junior Data Scientist position in November 2023.
• Primary work involves time series forecasting and demand forecasting for electronic parts manufacturing clients.
• Main tech stack includes Python (especially Pandas), Jenkins (for CI/CD), and MLOps tools.
• Interview process focused on practical understanding of Python, logical thinking, project discussion, and basic SQL/statistics—not heavy theoretical grilling.
• The interviewer was particularly interested in his project on HED data, which he had executed as part of the DataMites coursework.
• No live coding was required during the interview, but he was asked detailed conceptual and algorithm-based questions.
• Internship-to-full-time pathway: He was first hired as an intern and promoted to a full-time role after proving his capability.
• Hybrid work model: He works from the office two days a week and remotely on the remaining days.
• Used strategic resume optimization techniques using tools like Resume.AI to tailor resumes for each job posting.
• He applied to 10–20 jobs a day, tweaking resumes to match job descriptions and make them ATS-friendly.
• Focused initially on data analyst roles knowing entry into full-fledged data science roles would be difficult initially.
• Practiced Python and SQL rigorously using platforms like HackerRank, W3Resources, and pandas-focused GitHub repositories.
• Used YouTube channels like CampusX and websites like PapersWithCode and Medium for upskilling in machine learning and deep learning.
• DataMites' mock interviews and mentorship helped him identify weak areas and improve his profile.
• Peer interaction and doubt-clearing sessions were instrumental in understanding and applying complex concepts.
• Project-based learning played a vital role, especially when it came to showcasing relevant experience in interviews.

Strong emphasis on self-practice: Prasad emphasized the need for continuous practice and self-learning beyond classroom instruction.

Refer these aticles:

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

Prasad Dharne’s transformation from a civil engineering graduate to a junior data scientist in Mumbai underscores the power of focused effort, structured learning, and adaptability. His journey exemplifies how programs like those at DataMites can serve as launchpads into high-growth tech careers—even for complete beginners.

If you're looking to make a career switch into data science, now is the perfect time to enhance your skills. A 2024 report from Grand View Research highlights that the global data science platform market reached a value of $96.25 billion in 2023, with an impressive projected growth rate of 26.0% annually from 2024 to 2030. To stay competitive in this rapidly evolving field, it's crucial to choose an institute that offers practical training, internship opportunities, and solid placement support. Consider enrolling in data science offline courses in Pune, Hyderabad, Bangalore, Chennai, Mumbai, Delhi, and Kolkata to equip yourself with industry-relevant expertise.

Prasad Dharne’s success story is built on a foundation of hands-on training and practical experience. DataMites, a leading institute offering specialized courses in Data Science, AI, Machine Learning, Python Development, and Data Analytics, can be your stepping stone. Accredited by IABAC and NASSCOM FutureSkills, DataMites provides top-notch training, expert-led sessions, real-world projects, and extensive placement assistance.

With offline data science training in Bangalore, Hyderabad, Chennai, Pune, Ahmedabad, Coimbatore, and Mumbai, DataMites prepares learners with the essential skills and practical knowledge needed to excel. Whether you're a fresh graduate, working professional, or career changer, DataMites offers a clear path to launching a successful career in Data Science, AI, and Machine Learning.

To all aspiring data professionals out there, let Prasad’s journey be your inspiration: the path to a successful data science career is not about where you start—it’s about how persistently you move forward.