Andrew Cole

I graduated with a BS in Economics from The Ohio State University where I had opportunities to complete numerous courses and internships in the financial industry. During my course of study I developed a profound curiosity for not only the internal operations of financial environments, but also for what kind of information I could gain in any environment imaginable. So, when I graduated in the Spring of 2018 I decided to put that curiosity to the test and I moved to Madrid, Spain. I had the goal of gaining Spanish proficiency while also self-educating in Python 3 and SQL. I worked as a Business English Language Consultant for professionals in the Madrid business market so that I could employ both my economic background and my pursuit of the new language.

​

As I developed my technical knowledge in the self-education process, I quickly came to realize the value of data itself as a resource and the exponential possibilities that lie within. To put it frankly, I realized that if data is knowledge, and knowledge drives value, I needed to learn how to access that knowledge . I returned to Chicago in the summer of 2019, proficient in Spanish, and ready to attack the challenges that await in the Data Science world. The Flatiron School Immersive Data Science Boot Camp allowed for me to cultivate those curiosities while using my economic and financial background to fuel the questions being asked.

​

I have completed numerous data science and machine learning projects which go beyond the surface numbers to obtain valuable insights. I employ a variety of technical skills including Python 3, SQL, NoSQL, Pandas, Matplotlib, Seaborn, NumPy, Web Scraping (JSON & API), Excel, etc. for data gathering, exploration, and statistical hypothesis testing.

​

Building off of the exploration and statistical manipulation of data, I have built several machine learning tools to amplify the information contained within. My projects employ a wide array of data science tools such as regression analysis for determining feature significance within a time series analysis to accurately model, predict, & forecast future variable movements via Scikit-learn & Statsmodels Python packages. Other data projects include classification problem modeling with algorithms such as Decision Trees, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost.