Larry Hatcher

Data Analyst, Problem Solver, Visionary, Data Storyteller
Statistician, Aficionado of Film Noir, DGD Between the Hedges

My Résumé

Welcome!!! I'm here just trying to make sense of a chaotic world....or a chaotic database. Any problem can be dealt with using a systematic and quantitative approach to problem-solving. In data science we need to have the right tools and knowledge. Whether it’s trying to find the highest crime area in a major city or predicting who the Dawgs will start at Quarterback against Alabama in the game next season. The goal is to develop strong and accurate models for your analysis.

Please take a look at my projects below. These are small starter projects meant to show some of my skills in Excel and Python. Currently I am working on two new major projects. One is a visualization using Tableau and the other full machine learning project. I hope to have those up soon.

Flask API Project

This is a small project I did to create a Flask REST API in Python. This project gave me insights into APIs, writing and using JSON and using CRUD operations. I completed the project in VS Code using the app Thunder Client.

Impetum Diem Tempus Fugit

"Attack The Day" "Times Flies"

My opening python project. My goal was to build an algorithm that computed a time period from one date to another. The main problem I was trying to solve was getting an accurate time while accounting for Daylight Savings Time And Leap Year. I used OOP with the datetime and timedelta built-in modules.

Future Tableau API Project

I'm interested in current events and I am creating a visualization project to try to predict the potential impact that recent tariff increases will have on American food prices.

Future Machine Learning Project

My machine learning project is on sports betting in college football. My hypothesis is "overall the underdog team has a higher probability of winning against the spread". I'm using orginal data that I collected from several sports betting APIs. I am using several methods to cleanse my data that includes the use of Pandas. My training methods includes regression, Random Forest, XGBoost and Neural Networks.