Data Analyst

My journey began with learning HTML, CSS, and JavaScript, forming a solid foundation for web applications. I then explored frameworks like React and Node.js, building projects such as QuizMaster and an e-commerce app with seamless cart functionality. Expanding into AI, I worked on Brain Tumor Detection using CNNs and Credit Card Fraud Detection with XGBoost, integrating machine learning into development. Platforms like GitHub and Indeed provided resources and internships, including a research role and a summer internship at Cantilever. These experiences refined my problem-solving skills and ability to build production-ready applications. Preparing for roles involved revisiting core concepts, practicing coding problems, and developing AI-based solutions. This journey has reinforced the value of consistency, curiosity, and hands-on practice, shaping my passion for technology.

The Galytix on-campus recruitment process was a valuable learning experience. It began with an online test assessing aptitude, data structures, and algorithmic skills, followed by a technical interview where I discussed my analytical approach and problem-solving abilities. This journey enhanced my confidence and prepared me for future opportunities in data analysis.

I was not selected for the role at Galytix because, while my DSA skills were strong, my analytical thinking and domain-specific knowledge needed improvement. The interview process made me realize the importance of structured problem-solving, data-driven decision-making, and business-oriented analytical skills in a data analyst role. This experience has motivated me to work on refining my critical thinking abilities and gaining deeper insights into real-world data analysis.