I'm a junior at the University of California at Berkeley studying data science.
B.A. Statistics • September 2015 - June 2019
Relevant Coursework: Reproducible and Collaborative Statistical Data Science • Principles and Techniques of Data Science • Concepts of Statistics • Data Structures and Algorithms • Foundations of Data Science • Probablility for Data Science • Linear Algebra
Data Science Intern • June 2017 - August 2017
work in progress to be updated.
Kaggle • Python, XGBoost, Pandas
Built a machine learning model to classify the gender of a voice based on sound data. Achieved 98.23% on a 5-fold cross validation metric using a gradient boosting decision tree, implemented with sklearn.
Kaggle • Python, TensorFlow, Pandas
Processed files from a Kaggle dataset containing images of dogs and cats, and built a convolution NeUral Net to achieve 80% classification accuracy.
Team Member • Spring 2017 - Present
I am a member of the curriculum team within the Berkeley Institute for Data Science, working with both professors and students within Boalt Law School to create a curriculum for it's pilot data science class. This involves finding and formatting relevant datasets, and creating lesson plans revolving around applying data science to the field of Law.
Languages: Proficient in Python and Java. Experienced with SQL.
Libraries: Proficient with sci-kit learn, pandas, xgboost, and numpy.