I graduated from UCLA in June of 2013 with a BS in Mathematics/Economics degree. My coursework included the following: Calculus Series, Differential Equations, Lower and Upper Division Linear Algebra, Elementary Statistics, Introduction to Programming C++, Matlab for Engineers, Discrete Mathematics, Introduction to Probability Theory, Real Analysis and Advanced Real Analysis, Introduction to Mathematical Statistics, Introduction to Econometrics, Mathematical Optimization, Introduction to Computation and Optimization for Statistics, Mathematical Abstract Algebra for Applications. Additionally, I took three Ph.D. level courses: Applied Sampling, Machine Learning/AI and Econometrics. I was recently admitted into George Mason University for the MS in Data Analytics Engineering degree housed in Volgenau School of Engineering. The program specializes in Big Data and all the technologies involved as well as Big Data analytics. I will take coursework in Big Data, Data Mining, NLP, Machine Learning, Artificial Intelligence, Database Management, Pattern Recognition and the core of the program. Additionally, I`m taking online Coursera coursework. I`ve completed the following courses so far: Stanford`s Computer Science Algorithms Course, UC Davis` Tableau for Data Visualization Course and UC Davis` SQL for Data Science Course. I intend to take more data science related coursework on Coursera in the future. I also have been taking on data analytics work the last 7 years. My company was Duna Analytics and I handled everything from data visualization to machine learning and AI projects utilizing Python and R. ©

Trying not to put too much __asm__ in my C++. ©