Zuri’s journey from a non-technical background into artificial intelligence and machine learning is a powerful example of resilience, a growth mindset, and the impact of structured upskilling. She started with no prior coding or data science experience but, through determination and consistent learning, rewrote her career story and found her place in a rapidly evolving tech industry.
Zuri’s Interview Experience, Learning Journey, and Career Advice
Q1: Zuri, can you please introduce yourself and share your academic background?
Hi everyone, I’m Zuri Okafor. I hold both a Bachelor’s and Master’s degree in Mathematics, with my postgraduate studies completed at a leading university. My passion for numbers and logical reasoning led me to discover the world of data science. I am now a certified data science professional from CCI (Center of Computer Intelligence) and excited to be part of Rubixe.
Q2: How did you make the connection between mathematics and data science?
There’s a natural overlap between mathematics and data science, especially in statistics, modeling, and machine learning. My academic foundation made data science a logical next step. I realized this during my career exploration and decided to transition into IT through CCI’s structured program.
Q3: Coming from a non-coding background, was learning Python a challenge for you?
Initially, I had minimal exposure to Python. But with consistent effort and practice, I became comfortable within a few months. Python turned out to be intuitive and beginner-friendly, especially compared to other languages. It took me about 3–4 weeks to become proficient enough for data science applications.
Q4: How did your mathematics background help you with statistics and machine learning?
My training gave me a solid understanding of statistics, so that part was manageable. Machine learning was new, but the mathematical underpinnings made it easier to grasp. With dedicated learning, I built a strong understanding of ML in about two months.
Q5: How long did it take to complete the course and become industry-ready?
The journey—from learning Python and statistics to completing projects and preparing for interviews—took about nine months. I spent the first four months in live sessions, then a few months on capstone and live projects, followed by focused interview preparation.
Q6: What kind of projects did you work on during the course?
I worked on various capstone projects involving classification, regression, and clustering. My most significant project was a live client assignment on time series forecasting, predicting the availability of sports spare parts for inventory management. Completing this project individually boosted my confidence.
Q7: When did the placement support begin, and how was the experience?
Placement support from CCI began after my internship phase. They helped with resume building, interview scheduling, and feedback sessions. I appeared for two interviews; while I didn’t clear the final stage of the second, it gave me valuable insights into industry expectations.
Q8: How did you prepare for job interviews after completing the course?
After my live project, I spent another 1–2 months refining my technical and soft skills, doing mock interviews, and reviewing common data science interview questions. My goal was to be comfortable discussing projects, justifying model choices, and handling real-world business scenarios.
Q9: Could you share your interview experience? How many rounds were there?
The interview process included three rounds. The first was an assessment of basic Python programming and statistics. The second and third focused on technical knowledge and soft skills. In one interview, I was asked about machine learning, especially ensemble techniques like bagging and boosting.
Q10: Does doing an internship help in getting a job in AI/ML?
Absolutely. Internships provide real-world experience and credibility. Recruiters are interested in the projects you worked on, challenges you overcame, and your team role. Practicing is crucial, and internships offer hands-on learning and valuable talking points during interviews.
Q11: How long does it take to complete the CCI capstone and client projects and secure a job?
It usually takes 2–3 months to complete all capstone and client projects. Job placement varies: 25–30% of students get placed during or immediately after their project phase, while others land roles within a few months post-completion.
Q12: How is the work-life balance for a data scientist?
Work-life balance is generally manageable, though it can be unpredictable depending on project timelines and model performance. It varies by organization and project type.
Q13: Can a basic data science curriculum help crack interviews if companies are now focusing on Generative AI (GenAI)?
A strong foundation in data science fundamentals—Python, machine learning, neural networks, and statistics—is still highly valuable. While GenAI is gaining traction, many companies are open to training candidates in specific tools and frameworks after hiring.
Q14: What would be your message to other non-coders or math graduates wanting to pursue data science?
Don’t let a non-programming background hold you back. If you have a passion for problem-solving and data, this field is open to you. Choose a structured program, stay consistent, and practice regularly. Python and machine learning are learnable skills.
Key Findings from Zuri’s Career Talk
- Work-Life Balance: Manageable, though sometimes unpredictable based on project needs.
- Timeline: 2–3 months for capstone and client projects; 25–30% placed during or right after project phase; others take a few months more.
- Curriculum Relevance: Regularly updated to reflect industry trends; GenAI, LLMs, and NLP included in advanced programs.
- Hiring Focus: Employers want strong ML, neural networks, Python, and statistics fundamentals; GenAI can be learned on the job.
- Support for Career Gaps: Alumni can access free mentoring and revision sessions, even years later. Career gaps are not a barrier with the right preparation.
Zuri’s journey proves that with persistence, the right learning environment, and a willingness to upskill, anyone can transition into AI and machine learning. The global AI market is booming, offering vast opportunities for those committed to hands-on learning and continuous growth. Whether you’re a student, career switcher, or job seeker, success in this field is within reach if you leverage internships, real-world projects, and strong self-discipline.
CCI (Center of Computer Intelligence) offers expertly designed AI and ML courses for all levels, blending theory with hands-on projects and internships. With flexible learning options and strong career support, these programs provide an excellent launchpad for a rewarding tech career in artificial intelligence and machine learning.
