Ben’s story is a powerful example of how determination, continuous learning, and practical experience can propel someone from a non-technical background into a thriving data science career.
Q&A: Ben’s Path to Data Science Success
Q1: When did you get placed, Ben?
Just recently, I received an offer from Trinity Mobility, and I’m excited to share my journey here.
Q2: Could you tell us about your educational background and how you decided to pursue data science?
I graduated with a Bachelor of Science in Physics in 2021. Initially, I aimed for graduate school but didn’t clear the entrance exams. Friends suggested IT, and after researching, I found data science was a great fit for my math and coding interests, so I joined CCI (Center of Computer Intelligence).
Q3: Why did you choose data science specifically?
Physics involved a lot of math and working with small datasets. I wanted to apply those skills practically, and data science combines math, statistics, and coding in a way that really attracted me.
Q4: When did you start your data science journey?
I decided in late 2022 and joined CCI in January 2023, officially starting my learning then.
Q5: How was your learning experience at CCI?
The course started with Python programming, which felt simple at first. As we progressed through Python data structures, statistics, and machine learning, things got more challenging, but daily practice helped me gain confidence.
Q6: How much time did you dedicate daily for learning and practicing?
Sessions were about two and a half hours each day. Initially, I didn’t practice enough after class, but once I started coding daily, I quickly caught up.
Q7: Did you find machine learning difficult to learn?
Not really. Once I understood the concepts and algorithms, it became manageable, especially with clear explanations from mentors.
Q8: Were you asked about the mathematics behind machine learning in your interviews?
Not really. Interviews focused on practical application—how to implement techniques and solve real problems—rather than math derivations.
Q9: How long did it take you to become job-ready after completing the course?
It took about five to six months of consistent coding and project work to confidently apply for jobs and clear interviews.
Q10: What projects did you work on during your learning at CCI?
I worked on projects like linear and logistic regression, a self-effectiveness project, and a bank credit scoring project. These capstone and placement projects helped me apply techniques practically.
Q11: What kind of interview questions were asked by Trinity Mobility?
Questions covered machine learning basics, algorithms used in my projects, and scenarios for choosing between supervised, unsupervised, or reinforcement learning. Data analysis functions like group by and aggregation also came up.
Q12: How many interviews did you attend before landing your current job?
I attended two main interviews. I failed the first technical round but succeeded in the second.
Q13: What went wrong in the first interview?
I struggled with a Python coding question involving regular expressions. This made me realize the importance of consistent coding practice.
Q14: How much did your projects help you in interviews?
Immensely. Working on more than 10 projects and following proper data preprocessing steps helped me confidently explain my process during interviews.
Q15: What do you think differentiated you from others who took longer to get placed?
I focused on deeply understanding concepts and practicing a wide range of projects, which gave me real-world insight and made me stand out.
Q16: What message do you have for aspiring data scientists?
Stick to the course material, practice regularly, and work on projects to apply your knowledge. Practical application is key.
Q17: How important are communication skills in data science interviews?
Clear communication is important, but it’s more about confidently articulating your understanding than perfect grammar.
Q18: How do you practice coding for interviews?
I revisited class recordings and notes, created simplified references, and practiced coding problems daily.
Q19: How long is the typical duration of the CCI Certified Data Scientist course?
For me, it was about five to six months, including practice and projects.
Q20: Do employers expect deep theoretical knowledge or applied skills?
Employers now focus more on applied skills—how you implement machine learning and handle data problems.
Q21: What advice do you have about balancing course learning and self-practice?
Attend classes but supplement them with regular coding practice and project work.
Q22: Any final thoughts for future data science professionals?
Believe in yourself, be consistent, and focus on practical learning. Data science can be challenging, but with dedication, you can succeed.
