Projects I completed during the Data Science Immersive course at General Assembly:Analysis of the Ames, IA Housing Dataset-- In-depth data exploration and analysis-- Data visualization-- Feature engineering-- Linear regression, various regularization techniques-- Accompanying Kaggle competitionDad Joke Classifier-- Webscraping (Reddit)-- Data cleaning and exploration-- Natural Language Processing (CountVectorizer)-- Logistic regression, K-nearest neighbors, Random Forest classifier-- Streamlined modeling process with Pipelines and RandomSearch hyperparameter tuningHotel Classifier From Room Photos (Group effort)-- Massive dataset (~100,000 pictures, 25GB)-- Large-scale data analysis-- Convolutional Neural Networks and Logistic Regression-- K-means clustering-- Batch-based approach to modeling-- Accompanying Kaggle competitionIntro to Reinforcement Learning and Deep Q-Networks-- Introductory lesson on RL and Q-learning for non-technical/somewhat technical audience-- Described Deep Q-learning with the Cart Pole problem-- Solved Cart Pole using by-hand implementation of deep q-learning

Examined and evaluated communications and changes in communication modes to drive improvements. Demonstrated exceptional competencies in operating airborne signals intelligence systems and mission equipment. Oversaw gathering, review, and dissemination of intelligence information from electronic sources. Developed insightful and useful intelligence products to support field operations. Devised well-written documents detailing Arabic language foreign intelligence and offered insights into significance of intercepted correspondence.