As a Data Analyst Intern at Galytix Analytics, I was a member of the Data Engineering's quantitative team, responsible for handling financial data from scratch and serving international clients. My main responsibilities during the role: - Capturing data from PDF, cleaning and organizing qualitative and quantitative data using Excel. - Organizing data in desired formats, validating data, and performing further analysis and testing.I also had the opportunity to work in a dynamic and innovative environment, with 3 teams (Data Engineering, Data Science, and Software Engineering) working under one roof. This allowed me to learn and grow my skills while collaborating with different teams to solve complex business problems.In addition, I was also a part of the cricket team and actively participated in extracurricular activities. Being part of a product-based firm helped me gain hands-on experience and develop practical skills that I can apply in my future career.Overall, my experience at Galytix Analytics has helped me develop technical, analytical, and interpersonal skills that I believe will be invaluable in my future endeavors.

The project involved a comparative analysis of four SUV cars, Hyundai Creta, Kia Seltos, Tata Nexon, and Honda WR-V, with a special focus on the Hyundai Creta. The data was collected through an electronic survey using a questionnaire designed to understand customer preferences towards different car features while purchasing SUVs.The exploratory factor analysis was performed to reduce the 19 attributes under consideration to some meaningful factors. Based on the analysis, the Hyundai Creta was ranked first, followed by the Kia Seltos, Tata Nexon, and Honda WR-V. The study recommended tapping into the fast-growing automobile market by building a global brand, innovating into hybrid and electric cars, and developing cost-friendly and fuel-efficient cars for the future. The limitations of the study were a small sample size and the inability to completely verify the data. The project provided insights into data collection, exploratory factor analysis, and customer preferences.