Data Scientist | Data Analyst
Hi there, I'm Lauren!
I currently help lead Digital Analytics at Cru. For over nine years, I've been helping organizations solve problems and put their data to use. I have extensive experience mining data for insights and scaling organizational data maturity. I enjoy using statistics, Python and SQL to wrangle data. I am passionate about AI and Machine Learning, and enjoy learning new libraries and toolsets.
I have given talks such as 'Creating Dashboards That Work' and led analytics workshops for nontechnical audiences.
Besides data science and data visualization, I enjoy tennis, gardening and playing guitar.
2021
Georgia Institute Of Technology | Atlanta, GA, US
24-week intensive program focused on technical programming skills in Python, R, JavaScript, Web Scraping, API Interactions, SQL/NoSQL Databases, ETL, Tableau, Front End Web Visualization, Fundamental Statistics, Big Data, Machine Learning and Deep Learning.
2020
Nashville Software School | Nashville, TN
Month-long intensive program focused on learning Python & SQL, EDA, and best practices for Data Visualization.
2013
Barrett, The Honors College at Arizona State University
Interactive web application that predicts the risk of early hospital readmission for diabetic patients (within 30 days) using Supervised Learning.
Tools used : Python/sklearn, Keras, PostgreSQL, Flask, Heroku, html/css/Bootstrap
Category : Data Science, Machine Learning, Optimization, Web Application
Year : Nov 2021
Big Data ETL using AWS RDS and PostgreSQL. Used PySpark to investigate whether Amazon Vine reviews are free of bias.
Tools used : PySpark, AWS RDS/S3 Bucket, PostgreSQL/pgAdmin, Google Colab
Category : Data Science, Big Data, ETL, AWS
Year : Nov 2021
Evaluating and optimizing a neural network to predict successful charity funding applications using Deep Learning and hyperparameter tuning.
Tools used : Python, Sklearn, Matplotlib, Neural Networks, Keras, Google Colab
Category : Data Science, Machine Learning, Deep Learning, Optimization
Year : Nov 2021
Cryptocurrency classification system using Sklearn, dimensionality reduction with PCA & t-SNE, and cluster analysis with K-means
Tools used : Python, Sklearn, Matplotlib, PCA, t-SNE, K-means
Category : Data Science, Machine Learning, Unsupervised Learning, Statistical Inference
Year : Oct 2021
Machine learning model that attempts to predict whether a loan from LendingClub will become high risk using Logistic Regression and Random Forest classifiers.
Tools used : Python, Sklearn, Matplotlib, Logistic Regression, Random Forest
Category : Data Science, Machine Learning, Supervised Learning, Statistical Inference
Year : Oct 2021
As frequent flyers, our team was interested in exploring in-flight emergency air situations using squawk 7700 codes. We created an interactive web dashboard where multiple charts update from data that is stored in PostgreSQL. The project is powered by a Python Flask API.
Tools used : Python, SQLAlchemy, PostgreSQL, Javascript (D3.js, Chart.js, Leaflet.js), HTML/CSS/Bootstrap, Flask
Category : Web Application, Deployment, ETL, Full Stack Development, Teamwork, Data Visualization
Year : Sept 2021
Our team was interested in exploring healthcare data. We extracted global pharmaceutical spending data to determine how much each country is spending on off the shelf pharmaceutical medicines. Flask was used to deploy results to a pharmaceutical web application.
Tools used : Python, Pandas, SQLAlchemy, PostgreSQL, HTML/CSS/Bootstrap, Flask
Category : Web Application, Deployment, ETL, Full Stack Development, Teamwork
Year : Aug 2021
This project builds a web application that scrapes various websites for data related to the Mission to Mars and displays the information in a single HTML page. MongoDB is used with Flask to deploy results to an HTML page.
Tools used : Beautiful Soup, Pandas, Splinter, MongoDB, Flask, Bootstrap, HTML/CSS
Category : Web Application, Deployment, Database, ETL
Year : 2021
This project analyzes potential changes to the Citi Bike NYC program during Covid-19. Data was transformed in Jupyter Notebook in Python/Pandas and imported into Tableau to create interactive story points and two dashboards for the city.
Tools used : Python, Pandas, GeoPy, Tableau Desktop
Category : Jupyter Notebook, EDA, Data Visualization, Analysis
Year : Oct 2021
Analyzed 45,000+ films to explore the relationship between book adaptations and movies using Python, Pandas, Matplotlib, Numpy and Scipy. Visualizations: linear regressions, T-tests, ANOVA, Word Clouds.
Tools used : Python, Pandas, Matplotlib, Scipy, Numpy, Statistics, Linear Regressions
Category : Statistics, Correlation, Data Visualization, EDA
Year : Jul 2021
This project uses Javascript, Plotly and D3 to build an interactive dashboard exploring and analyzing belly button microbial diversity. The dashboard dynamically populates based on the selected test subject ID.
Tools used : Javascript, D3, Plotly, JSON, HTML, Bootstrap, CSS
Category : Web Application, Deployment, Data Visualization
Year : Aug 2021
This project uses Pandas, HTML, CSS and Bootstrap to create a responsive website displaying climate and weather data and visualizations.
Tools used : Pandas, HTML, Bootstrap, CSS
Category : Web Application, Web Design, Deployment, Data Visualization
Year : Jul 2021
Created a table schema and ERD for employee data using PostgreSQL database. Used SQL to analyze data.
Tools used : Postgres/pgAdmin, SQL, ERD
Category : Data Analysis, Database, Data Engineering, Data Modeling
Year : Aug 2021
Used SQLAlchemy ORM queries, Pandas and Matplotlib to analyze a climate database. A Flask API was then created to store the analysis.
Tools used : Pandas, Matplotlib, SQL-Alchemy, Flask
Category : Data Analysis, ORM, Web Application
Year : Aug 2021
Analyzed the weather of 500+ cities using the OpenWeatherMap API. Displayed results using pandas, linear regressions via matplotlib, and a heatmap using Google Places API.
Tools used : Python, Pandas, Matplotlib, Numpy, Google Maps API, Scipy
Category : Data Analysis, API, Statistical Analysis, Data Visualization
Year : Jul 2021
Analyzed anti-cancer pharmaceutical study results in 249 mice across various drug regiments to determine if drug regimens and final tumor volume are correlated.
Tools used : Python, Pandas, Matplotlib
Category : Data Analysis, Statistical Analysis, Data Visualization
Year : Jul 2021