Example of reproducible research workflow using R Markdown
Course project predicting excercise errors
Rpres presantation to pitch app created for class
Bayesian A/B Testing Shiny App
During school I learned to use quantitative methods to solve problems. My education has given me the opportunity to learn several important things including finding insights from both observational and experimental data. Several classes including Design of Experiments, Statistical Quality Control, Response Surface Methodologies, Regression Analysis, Stat Learning for Data Mining, and Time Series Forecasting, have given me a solid foundation for using data to answer questions.
In terms of technical tools I have fairly broad experience. At my first job out of school I used mostly R, SQL, and Excel. I became a much better R programmer throughout that time and feel confident in my ability to clean, model, and visualize data. I also had great experience learning to navigate enterprise databases using SQL. I truly learned how important attention to detail is. At PayPal I have continued to use SQL to pull and aggregate data and Tableau for visualization. I have completed a MOOC from Coursera where I used MATLAB for the homework assignments. Finally, I have spent many hours playing with and studying various IPython notebooks. Though I haven’t used Python in a work setting yet, I have loved watching and reading tutorials from conferences about Machine Learning with scikit-learn and data munging with pandas.
Lastly, I just want to say that I LOVE learning. I think platforms like Coursera, edX, and Udacity are just amazing! I had such a fun time taking Andrew Ng’s Machine Learning class and Statistical Learning from Rob Tibshirani and Trevor Hastie. I hope to continue taking such courses and never stop learning. I feel like I am a quick learner and not afraid of learning new things.