Since the beginning of my undergraduate studies in computer science, I have been fascinated by how data can radically alter how we perceive the world. I have honed my abilities in data analysis and visualization using software such as Python, R, Orange, and SQL through a variety of data science classes and academic projects. I have seen firsthand how data-driven insights can be used to solve real-world problems in industries ranging from finance to healthcare to earth-sciences. Generally, the capability of data science to enhance cybersecurity and detect fraud is what really excites me. As we become increasingly reliant on technology, the need for robust and secure data systems has never been greater. I am very interested in exploring the intersection of Data Science with cybersecurity and finance, used to address various applications such as anomaly detection and unsupervised learning for fraud detection in finance. Moreover, I collaborated on several projects pertaining to finance such as implementation of neural networks to predict stock prices and prediction for a credit card approval using logistic regression, neural networks and random forests.