Michael J McFall

Azure Data Engineer | Python | SQL | Data Scientist


Data Science Career Track

The Ohio State University
Columbus, OH
Master of Science, Physics

Butler University
Indianpolis, IN
Bachelor of Science, Physics


  • Cloud Provider
    • Azure
  • Programming
    • Python
    • SQL
    • R
  • Software
    • Power BI
    • Tableau
    • GitHub
  • Data Science
    • Data Analysis
    • Data Visualization
    • Statistics and Probability
    • Machine Learning
    • Linear Regression
    • Logistic Regression
    • Random Forest
    • Gradient Boosting
Udemy Courses Completed
DataCamp Course Completed


Talent Path

Data Engineer
June 2021 - present
  • Implementing fundamentals of Azure services
  • Building end-to-end ETL and ELT solutions using Azure
  • Data modeling in relational databases
  • Pipeline creation in Azure Data Factory and Synapse Studio
  • Python-based coding for data transformation in Azure Databricks
Projects: Global Mart Data Transformation
  • Team project designed to understand fundamentals of Azure. Case scenario to migrate on-premise datasets to the cloud while providing efficient storage, analytics services for transformation, and data warehouse for business analytics.

Springboard Data Science Career Track

May 2019 - Feb 2020
  • 550+ hours of hands-on curriculum, with 1:1 industry expert mentor oversight, and completion of 2 in-depth capstone projects. Mastering skills in Python, SQL, data analysis, data visualization, hypothesis testing, and machine learning.
Projects: Recruit Restaurant Visitor Forecasting
  • A machine learning project that focused on predicting the number of visitors per day to over 700 restaurants. The model was created using historical data and additional features created using time-series functions.
Projects: Food Access Prediction
  • A machine learning project that aimed to predict the ratio of convenience stores to grocery stores in US counties. The model used datasets from the USDA, the Census Bureau, and the IRS to add multiple socioeconomic features to the store counts. Performance of different models: linear regression, random forest, and gradient boosting, was evaluated using a hold-out set and calculating an RMSE.


AZ 900: Microsoft Azure Fundamentals – Jul 2021
DP 900: Implementing an Azure Data Solution – Jul 2021
DP 203: Designing an Azure Data Solution - Aug 2021 – Aug 2022
DA 100: Data Analyst Associate - Sep 2021 - Sep 2022