Student
I learned data science in a hands-on, project-based learning environment. This is a 12 week, full time technical training program with instructors from the industry and curriculum that is grounded in high-demand industry skills. I am learning Python, SQL, Machine Learning, web scraping, Tableau, statistics, modeling techniques, and more.Through the duration of this course, I completed four real-world projects:King County, WA Housing Affordability Study (March 2020) I obtained the last 18 years of data for population, median home value, median household income, and number of housing units for King County, WA and created machine learning models via linear regression and random forest. I found how significantly each of the variables correlated to increasing home values.Predicting Neighborhood Affluence with Yelp Ratings (February 2020)A group project, I used a Yelp API to obtain data on Seattle restaurant prices and we web scraped data for Seattle median property values, median rent rates, and median household income. I created a variety of models to observe correlations.Correlations and Differences Between Lesbians and Gay Men on Reddit (January 2020)I utilized pushshift API on one gay and one lesbian reddit page and compared the use of different words on each page using CountVectorizer (CVEC) and TfidVectorizer (TVEC). I utilized Logistic Regression and Naive Bayes Multinomial models with CVEC and TVEC through pipelines and gridsearch to find the best correlations. Ames, Iowa Correlations in Housing Data (December 2019) I explored and cleaned data, set up linear and logistic regression models, and utilized the models to show correlations for what features of a house make more money when selling. I showed home sellers or flippers what typically correlates to higher home values.
