Qian Zha

I am an aspiring applied analytics major student at Columbia University. I’ve always been fascinated by numbers, and working in data analytics has been a long-term goal of mine. I have strong technical skills and an academic background in finance, coding, statistics, and machine learning.

My experience in data analysis and proficiency in various tools and techniques, such as Python, SQL, Excel, and Tableau, have equipped me with the ability to extract insights from complex data sets and present them in a clear and concise manner. I have also worked on projects involving data cleaning, data visualization, and predictive modeling, which have honed my analytical skills. For example, I applied machine learning and Python models to help S&P Global understand how climate change is affecting the operation of the company.

I also utilized multiple R language packages and 10 machine learning models to predict New York City’s Airbnb rental prices, achieved the lowest RMSE with XGBoost model and awarded Top 10 best performance students during the Kaggle competition.

In addition to my technical skills, I have strong communication and team management skills. During my previous role as an IT Strategy Consulting intern at Deloitte, I coordinated a team of 10 consultants in the design and implementation of an Oracle EBS system for a large healthcare client in Shanghai. Through my coordination, we were able to achieve a 20% improvement in operational efficiency for the client.

I also developed functional specifications and test plans for the Oracle EBS customizations, providing training and support to end-users, resulting in a 25% improvement in user adoption rates and a 15% improvement in user satisfaction ratings.