People / Students / Class of 2024
Fanqi (Rachel) Song is a highly motivated and curious individual with a deep passion for data science and analytics. Graduating from the University of California, Los Angeles (UCLA) with a Bachelor's degree in Financial Actuarial Mathematics and a specialization in computing, she has honed her technical expertise in programming languages such as RStudio, Python, Java, SQL, and C++. Throughout her academic journey, Rachel excelled in diverse courses spanning mathematics, statistics, and computing, but her true passion was ignited when she delved into data analytics projects. In her advanced Python class. She led a Python group project, creating an interactive website predicting cheap flight prices based on user preferences. Excelling in Python web scraping, data preprocessing, and deep-learning model training, she skillfully designed a user-friendly Flask website, showcasing remarkable engagement. Rachel's internships, particularly her role as a product analyst at Marsh McLennan showcased her ability to leverage machine learning models and visualization tools to analyze complex insurance datasets. Her dedication to fine-tuning models and extracting valuable insights demonstrated her potential as a future data scientist. With a collaborative mindset and a desire to connect data with actionable solutions, Rachel aims to bridge the gap between the business and data worlds. Her passion for continuous growth and belief in the transformative power of data-driven decision-making drives her to explore diverse industries and make a meaningful impact. Seeking to expand her knowledge in the field, Rachel aspires to pursue further academic studies in data science. Northwestern University's Master of Science in Machine Learning and Data Science program aligns perfectly with her goals, offering a multidisciplinary approach to address future challenges in data science. Rachel is determined to embrace the opportunities presented by Northwestern University's program, paving the way for a rewarding and impactful career in the ever-evolving world of data science.
