Welcome to Easley's Data Scientist Day!
Please make sure to register for the event here if you haven’t done so already.
Thank you for joining us as we showcase the talent of our Easley cohort! We’re so proud of all that these graduates have achieved over the last 6 months. Since beginning their journeys into data science on December 7th, 2020, they’ve gained hands-on experience in Applied Statistics, SQL, Python, Pandas, Matplotlib, Machine Learning, Natural Language Processing, Data Storytelling, Git, Jupyter Notebooks, Tableau, and Seaborn. They put all of these skills to use to develop their capstone –an end to end data science project with actionable insights. As you watch, you can learn more about each grad by clicking their headshot to visit their Alumni Portal profile.
The event will start promptly at 3:00pm below! If the video doesn’t auto-play for you, please click the play button.
What’s the 311?! Predicting Speed of Response to 311 Calls
Using data acquired from the City of San Antonio, our team aims to create a classification model to predict the level of delay in a call’s response time. From this project, we want to answer what drives the level of delay and if there is a way to minimize late response times for 3-1-1 calls in our city.
America’s Blues: An Analysis of Fatal Police Encounters
This project will analyze attributes of civilian fatalities in police altercations in the United States in order to shed light on changes that can be made to save more lives. Dataset features will be used to build a classification model predicting the threat level of a victim.
Off The Rails: Analyzing US Rail Accidents
The railroad industry accounts for 1/3 of U.S. exports every year. Rail accidents result not only in economic loss but also in human-sustained injuries and even death. Our team acquired 8 years of data on U.S. rail accidents from the Department of Transportation. We utilized the full data science pipeline and tools to analyze the data and provide recommendations. We used classification machine learning algorithms to predict which company would be involved in a rail accident, and under what conditions, to further illuminate our analysis for identifying drivers of these costly accidents.
The Cagey Bee: Detecting Fake News with Machine Learning
Our goal is to create a classification model that accurately identifies fake news articles across the web. We created “The Fake News Detector,” a web application that assists users in identifying which articles they input are fake or misleading. By doing this, we hope to add perspective, reduce noise, and educate the general public on the information they receive while acting as a counterweight for other fact-checking organizations.
Impressed? Start hiring today!
Find a great candidate to interview? Let us know! Need some guidance? We can help you find the best fit for your team, for free! Email firstname.lastname@example.org and you’ll have a new data scientist in no time!