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followers: 10.0 following: 10.0 repos: 27.0 gists: 0.0

Name: David Supple

Type: User

Bio: Data & Intelligence Analyst with skills in Python, Excel, SQL, Tableau, R, & JavaScript

Location: South Florida

Blog: https://www.linkedin.com/in/davidasupple/

Hi, I'm David Supple! Welcome to my Github profile! 👋

About me

Business analyst with a background in digital marketing before taking a hard left hand and submerging myself into data analytics.

I enjoy leveraging data to find solutions and create corresponding results via presentations and reports to tell a compelling story.

My strengths include analytical problem solving abilities to leverage data with collaborating amongst cross-functional teams to find solutions to problems and help achieve their goals.

Skills

Python	PostgreSQL	pandas	Tableau
Matplotlib	seaborn	Plotly	scikit-learn
SciPy	Drupal	BeautifulSoup	AWS
Flask	D3	MongoDB	R Studio

David Supple's Projects

amazon_vine_analysis

Analyze Amazon Vine reviews with PySpark.

belly_button

Build a dynamic dashboard to analyze human navel bacteria using JavaScript, Plotly, D3, HTML, CSS, and Bootstrap.

bike_sharing

Create visualizations using Tableau to analyze New York bike sharing data.

credit_risk_analysis

Build and evaluate several machine learning algorithms to predict credit risk.

cryptocurrencies

Use unsupervised machine learning, PCA algorithm, and K-Means clustering to analyze and classify a database of cryptocurrencies.

dsupps

My personal repository

election_analysis

Analyze election data using Python

kickstarter-analysis

Performing analysis on Kickstarter data to uncover trends

kickstarter_challenge

Performing analysis on Kickstarter data to uncover trends.

mapping_earthquakes

Create interactive maps using Leaflet.js, GeoJSON , Javascript and D3 library to explore earthquakes around the world.

mechacar_statistical_analysis

R programming language, statistics and hypothesis testing to analyze a series of datasets from the automotive industry.

mission-to-mars

Web scrape NASA websites and collect data about the Mission to Mars. Save the new data in a MongoDB database and display it on a HTML page.

mixed_effects_qbs-tutorial

Mixed effects model using R - tutorial

movies-etl

Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.