Hi! My name is Shania Thomas

Inquisitive | Data Enthusiast | Passionate Educator | Lifelong Learner | Dog Mom

Lifestyle

Hi! My name is Shania Thomas and I am an eager data scientist. I have completed General Assembly's very rigorous Data Science Immersive, and hold a Bachelor's degree in Biological Sciences from California State University, Channel Islands. Both of these have given me an immense amount of transferable skills and knowledge that I am excited to use.

I've always appreciated the saying "there's more than one way to skin a cat" because I've always enjoyed finding different angles to accomplish a goal. Sometimes that goal was how to get out of doing the dishes, other times it was how to increase sales or student performance. Regardless, I always based my decisions on some type of data or statistics.

When I'm not working on passion projects, I enjoy playing fetch with my pup, trying new foods, and sightseeing all of the beauties of nature.

Career

I am a personable, pragmatic, and collaborative data science professional with education and leadership experience. I'm skilled in identifying growth opportunities, forging pathways to success, and fostering a fun and effective environment in tandem. Using Python and libraries such as Pandas, SciKit Learn, Matplotlib, Seaborn, Numpy, and NLTK, I remain flexible when navigating a problem because I believe every problem has a solution.

See my career journey on my resume page.

Projects

Academia and Sustainability-
Is There Crossover?

This project was completed using Natural Language Processing techniques and multiple supervised models to predict which subreddit, Academia or Sustainability, the post came from. I used an API to scrape the posts from Reddit to determine if conversations within communities of Academia are encompassing language associated with sustainability.

Predicting Number of COVID-19 Infections

This was a group project completed using two different time series models, ARIMA and SIR, to predict how many COVID-19 infections a state could expect. We aimed to determine which model was going to be a more accurate predictor of new infections with the angle of assisting health care officials prepare hospitals, clinics, or any other necessary organizations to handle COVID-19 cases.

What Makes Employees "Antiwork?"

This project was completed using Natural Language Processing techniques and Latent Dirichlet Allocation (LDA) topic modeling to understand what employees main concerns regarding unhappiness in the work place are. Using LDA, I found and analyzed the main topics of concern over the years beginning in 2013, up until 2022, with focus on the years 2019-2021.

Get in touch