Siddharth's Journey From Beginner to Data Science Professional

Siddharth’s journey into data science didn’t begin with a technical degree or prior coding experience. Like many career switchers, he started with curiosity and a desire to build skills that aligned with the growing demand for data-driven roles. The early stage wasn’t easy. Learning programming, statistics, and analytical thinking from scratch required discipline, patience, and the right guidance.

What set Siddharth apart was his commitment to structured learning and hands-on practice. Through focused training, real-world projects, and practical exposure, he gradually transformed from a beginner into a confident data science professional. Those who watch Datamites success stories will recognize this pattern right away. His journey was never about shortcuts or instant wins, but about steady progress, learning by doing, and making smart choices that ultimately led to a rewarding career in data science.

How Siddharth Built His Career as a Data Science Professional with DataMites

Discover how Siddharth transitioned into data science through structured learning, hands-on projects, and real-world problem solving with DataMites.

Q1. Can you introduce yourself and your current role?

I am currently in my 8th semester of college and I have been placed as a Junior Data Scientist at Intellisolar in Ahmedabad. I joined the company on the 1st of December while my graduation is still ongoing.

Q2. When will you complete your graduation?

My graduation will be completed in 2026. Along with my studies, I have already started working professionally, which is a great learning experience.

Q3. How did you first get interested in data science?

I started reading and researching about data science on my own. After understanding the scope and learning opportunities, I decided to seriously pursue it as a career.

Q4. Where did you complete your data science training?

I completed my data science course at DataMites Ahmedabad. I joined the program in March and went deep into learning data science concepts.

Q5. How was your overall experience at DataMites Ahmedabad?

The experience was very good. The learning was structured, and the support from mentors was strong throughout the course. It helped me gain confidence in both theory and practical skills.

Q6. How important were mentors during your learning journey?

Mentors played a very important role. Mentors like Prashant sir and others supported me in every step. They guided me like a brother and helped me whenever I faced difficulties.

Q7. Which topics did you enjoy the most during the data science course?

Python and Machine Learning were my favorite topics. I enjoyed working on ML concepts and applying them to real-world problems.

Q8. How did you approach learning data science during the course?

I focused on continuous practice. Instead of just learning concepts, I practiced daily, worked on small topics, and built small projects regularly. That helped me understand everything clearly.

Q9. How long did it take you to feel confident in data science?

The course duration was around three months, but I kept practicing every day. I feel that five to six months of focused learning and project work is enough if you are consistent.

Q10. What kind of projects did you work on during your internship?

During my internship, I worked on multiple projects, including:

• Flight Price Prediction
• Credit Score Prediction
• Rice Leaf Disease Detection

These projects helped me apply machine learning concepts practically.

Q11. How did you get the interview opportunity at Intellisolar?

I got the first interview round through a reference. After that, the process moved forward based on my skills and projects.

Q12. What were the major interview rounds like?

The interview mainly focused on what I had mentioned in my resume. They asked questions related to machine learning concepts and scenario-based problems.

Q13. What type of machine learning questions were asked?

Most questions were scenario-based. They wanted to understand how I would approach real-world problems using machine learning, not just theoretical definitions.

Q14. Were SQL questions asked during the interview?

Yes, SQL questions were asked. They were mostly scenario-based, such as how to handle or process very large datasets, including data with more than 10x or crore-level records.

Q15. How important were projects during the interview?

Projects were very important. Most of the discussion was around the projects I had done, how I built them, and how I solved specific problems using data science techniques.

Q16. How much time did you spend practicing Python and Machine Learning?

I practiced daily. Even after completing the course, I kept revising basics, solving problems, and working on projects. Regular practice made a big difference.

Q17. What advice would you give to students who want to join a data science course?

Focus on basics first. Practice every day, even small topics. Build projects along with learning. Consistency matters more than speed.

Q18. How important are GitHub and LinkedIn for data science students?

They are very important. Building a strong GitHub with projects and maintaining an active LinkedIn profile gives you an advantage during job applications and networking.

Q19. What helped you crack the data science interview?

Daily practice, strong basics, project experience, and understanding concepts clearly instead of memorizing them helped me crack the interview.

Q20. Any final message for aspiring data scientists?

Data science is not simple, but it becomes manageable with regular practice. Stay consistent, work on real projects, revise basics daily, and don’t skip building your online profile.

Refer to these articles:

• Modern Data Stack for Data Scientists: Tools You Must Know
• Top 7 Data Science Job Roles in 2026 You Should Know
• Databricks vs Snowflake: Which Is Better for Data Science

Siddharth’s Journey into Data Science: Key Insights with DataMites

Siddharth’s journey shows how consistent effort, practical project experience, and guided training helped him build a strong foundation in data science, highlighting the importance of continuous learning and applied skills.

• Self-research and early interest helped Siddharth choose data science while still in college.
• Daily, consistent practice was the biggest factor in building strong data science skills.
• Python and Machine Learning were the most impactful and enjoyable subjects during learning.
• Hands-on projects played a critical role in understanding real-world data problems.
• Project-based learning improved confidence and interview performance.
• Mentor guidance significantly accelerated learning and problem-solving ability.
• Five to six months of focused learning is sufficient for beginners if practiced consistently.
• Interviews focused more on scenario-based questions than theoretical knowledge.
• Machine learning concepts were tested through practical use cases.
• SQL skills were evaluated using large-scale data handling scenarios.
• Resume-driven interviews made clear project explanations essential.
• Referrals helped secure interviews, but skills determined final selection.
• A strong GitHub profile added clear value during the hiring process.
• An active LinkedIn presence improved visibility and professional opportunities.
• Starting a data science career before graduation is achievable with disciplined preparation.

Siddharth’s journey shows that you don’t need years of experience to start a career in data science. What you need is discipline, practice, mentorship, and a clear focus on building real skills.

Refer to these articles:

• Sathish’s Inspiring Journey to Becoming a Data Scientist
• Vinay Gaikwad’s Journey in Freelance Data Science
• How Parag Thakur Built His Career in Data Science

If you’re starting out in data science or thinking about a career switch, Siddharth’s journey shows what actually makes the difference: strong fundamentals, steady practice, and real-world experience. The global data science platform market was valued at USD 96.25 billion in 2023 and is expected to grow at a 26% CAGR through 2030, according to Grand View Research. What this really means is simple. Data is exploding across industries, and companies are actively looking for people who know how to work with it. That’s why data science remains one of the IT courses in demand today.

Siddharth’s career growth was shaped by structured training at the DataMites institute. Starting from the basics, even without a technical background, he built a strong foundation in Python, statistics, and machine learning. Along the way, he gained hands-on experience through internships as a Data Analyst and by working on practical data science projects. Backed by globally recognized certifications such as IABAC and NASSCOM FutureSkills, DataMites helped him bring together theory, real project work, expert mentorship, and placement support. Choosing one of the best data science courses in Noida, Hyderabad, Bangalore, Kolkata, Pune, Mumbai, Chennai, or Delhi, opened clear pathways to a future-focused career.

Whether you’re a fresher, a professional from a non-technical background like Siddharth, or planning a career transition, DataMites makes quality data science education accessible. With both online and offline learning options across one of the leading data science institutes in Chandigarh, Coimbatore, Pune, Mumbai, Bangalore, Chennai, Ahmedabad, and Hyderabad, Siddharth’s story makes it clear that with the right guidance, practical exposure, and consistent effort, moving into data science is not just possible, it’s a smart move for the future.