Saurav’s Journey: From Fresher to Data Scientist

Starting out in data science with no prior coding experience might sound impossible, but Saurav’s story proves otherwise. Coming from an electrical engineering background and even trying his hand at civil services, he eventually discovered his true calling in data science. Through consistent effort, structured training, and guidance at DataMites, he built the skills needed to land a data scientist role at Sirpi Company in Bangalore. His path is an inspiring example for anyone aiming to switch careers into this competitive field.

If you’re a fresher or professional curious about how real people make this leap, Saurav’s journey offers both motivation and practical insights. Watch DataMites success story to see how determination, the right mentorship, and hands-on practice can transform a career.

How Saurav Successfully Built His Data Science Career with DataMites

Here’s how Saurav transitioned into data science through consistent practice, hands-on projects, and guided mentorship.

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

I was born and brought up in Odisha. I completed my BTech in Electrical Engineering from VSSUT, Odisha. After graduation, I prepared for civil services for a couple of years but couldn’t clear it. That’s when I started looking for a career where I could apply my skills and secure a strong future. I came across data science and chose to make it my path.

Q2: What led you to select DataMites as your training provider?

After researching online, I came across DataMites. I attended a demo class first, where I got a clear picture of what data science is and the opportunities in this field. That convinced me to enroll in their Certified Data Scientist course.

Q3: What did the course structure look like?

The course was divided into modules, Python, SQL, Power BI, Machine Learning, Deep Learning, and more. After every module, we had assignments that helped reinforce concepts. There were assessments, mock interviews, and finally, placement support.

Q4: Did you have any coding background before starting with Python?

No. I had only used C and C++ in college. Python was completely new for me. But compared to other languages, Python felt simpler because of its libraries. It made learning smoother.

Q5: How was your experience learning Python?

At first, it was challenging. But with practice, it became fun. Solving problems every day and applying what I learned helped me gain confidence.

Q6: What about Machine Learning? Was it difficult to grasp?

Yes, the machine learning module was tough initially. Concepts like supervised and unsupervised learning or algorithms such as logistic regression, random forest, and decision trees took time to understand. But the trainers started from scratch and explained everything clearly. Eventually, I moved on to deep learning models like CNNs.

Q7: How much time did you dedicate daily to studying?

Classes were about two hours. After that, I spent another 2–3 hours revising and practicing problems. For example, if we learned about lists in Python that day, I would go home, revise, and solve 8–10 problems to strengthen my understanding.

Q8: What resources apart from institute material did you use?

I referred to GeeksforGeeks, W3Schools, YouTube channels like Code With Harry, Chris Naik, and CampusX for machine learning.

Q9: What kind of projects did you work on during your internship?

I worked on four capstone projects and one client project. My data science projects included house price prediction, digit recognition, and heart disease prediction. For the client project, we solved a credit risk assessment problem. We worked in teams, dividing tasks but reviewing each other’s work.

Q10: How many mock interviews did you give before clearing?

I cleared it on my third attempt. The first two helped me understand my weak areas, especially in machine learning. The feedback from those sessions was very useful when I faced real interviews later.

Q11: What type of questions did companies ask during client interviews?

In the first round, most questions came from Python and SQL. The second round focused on machine learning concepts. After that, there was an HR round.

Q12: Can you provide some sample questions on Python and SQL?

For Python, I was asked to write functions like checking for leap years or finding unique values in a list. For SQL, it was mostly theory-based questions rather than practical queries.

Q13: Which platforms helped you prepare for interviews?

I mainly used W3Schools and Analytics Vidhya. They were great for revising concepts before interviews.

Q14: How was the placement team’s support at DataMites?

They helped us prepare job-ready resumes, scheduled interviews, and gave feedback after mock interviews. Once I cleared the mocks, I started getting client calls, and that’s how I landed my role at Sirpi Company.

Q15: What advice would you give to students struggling to crack interviews?

Be consistent. Solve 5–6 Python and SQL questions daily. Dedicate time to revising machine learning and deep learning. Every company focuses on different areas, so you must prepare for all. Don’t lose hope if you fail in a few interviews, the key is to keep improving.

Q16: Do companies care about educational background?

Not really. What matters most is your skill set and how well you can apply concepts in real-world problems.

Q17: Which skills should freshers focus on right now?

For data science roles, Python, SQL, and machine learning are essential. For data analyst roles, add Power BI and Excel to that list.

Refer to these articles:

• Tejaswini’s Path to Data Science Success
• Altaf’s Path to Becoming a Senior Data Scientist
• How Darshan Shaped His Career Path in Data Science

Key Takeaways from Saurav’s Data Science Journey

Here are the main lessons and insights from his experience that can help anyone aiming to start or grow in data science.

• Background shift:  Saurav moved from electrical engineering and civil service prep into data science.
• Discovery of DataMites:  He chose the institute after attending a demo class that clarified opportunities in AI and data science.
• Structured course:  Training covered Python, SQL, Power BI, Machine Learning, Deep Learning, with assignments after each module.
• No prior Python experience:  He learned Python from scratch during the course.
• Learning curve:  Python felt easier due to libraries, while machine learning concepts were initially challenging but manageable with good trainers.
• Daily study routine:  Spent 2–3 extra hours daily on revision and solving problems beyond class hours.
• Self-study resources:  Used GeeksforGeeks, W3Schools, Code With Harry, Chris Naik, and CampusX to supplement learning.
• Internship projects:  Worked on four capstone projects (house price prediction, digit recognition, heart disease prediction, etc.) and one client project (credit risk assessment).
• Team collaboration:  Projects were divided among team members, but everyone contributed and reviewed each other’s work.
• Mock interviews:  Cleared after three attempts; feedback from earlier mocks was critical for improvement.
• Interview questions:  Focused on Python (functions, list operations) and SQL (basic theory). Machine learning concepts tested in later rounds.
• Placement support:  Resume building, feedback, and interview scheduling by DataMites placement team helped secure the job.
• Interview preparation platforms:  Relied on W3Schools and Analytics Vidhya for final interview prep.
• Advice to students:  Consistent practice of Python, SQL, and ML concepts is key. Solve daily problems and revise regularly.
• Educational background:  Doesn’t matter much if you have strong skills.
• Key skills for freshers:  For data science: Python, SQL, Machine Learning. For data analytics: add Power BI and Excel.
• Mindset:  Patience and persistence are important since the job market can be slow, but preparation pays off.

Refer to these articles:

• How to Become a Data Scientist in Bangalore?
• Data Science Course Fee in Bangalore
• How to Become a Data Scientist in Mumbai?
• Data Science Course Fees in Mumbai

Saurav’s story is proof that with structured training, persistence, and the right mindset, you can transition from a fresher to a data scientist. His journey with DataMites highlights how practice, projects, and mock interviews can make the difference between just learning and actually landing the job. Since data science is considered as top IT courses today, his advice carries extra weight: stay consistent, practice daily, and focus on building real skills.

If Saurav’s journey motivates you to step into data science, there’s no better moment to start building your skills. According to Precedence Research, the global data science platform market is projected to grow from USD 175.15 billion in 2025 to over USD 676.51 billion by 2034, at a CAGR of 16.20%. With industries generating massive volumes of data, the need for skilled professionals is only increasing. Picking the right training institute that focuses on practical learning, real-world projects, and placement support is the smartest move. Enrolling in a data science course in Bangalore, Mumbai, Hyderabad, Chennai, Pune, or Delhi can open the door to countless tech opportunities.

For Saurav, structured training and expert mentorship at DataMites were turning points in his transformation. By mastering Data Science, Machine Learning, Python, SQL, and Deep Learning, and gaining hands-on experience with industry projects, he built the confidence and expertise to secure his role at Sirpi Company.

DataMites provides the same opportunities through data science courses in Mumbai, Bangalore, Chennai, Hyderabad, Pune, Coimbatore, and Ahmedabad, along with flexible online options, making career growth accessible for freshers, professionals, and career changers just like it was for Saurav.