Trenton McKinney
Data Science and Analytics with Python, and Hardware Test Automation Consultant
Bachelor of Science: Electrical Engineering - Portland State University, Portland, Oregon
Managing Member and Consultant at
I'm available for consultation related to data science/analytics, and electrical hardware test automation projects. I enjoy learning, solving challenging problems, data munging and visualization.
With a B.S. Electrical Engineering and 10+ years of electrical hardware testing, hardware test automation and data analytics experience, I bring a quantitative background of curiosity, critical thinking and problem solving to provide timely and effective solutions using python to automate data collection, wrangling, analysis and visualization.
That same engineering mindset, and acumen is also applied to staying abreast of the ever-evolving data science and analytics ecosystem. I enjoy solving problems, providing data driven insight and continually expanding my knowledge.
Data are only as valuable as the insights gleaned from analysis and I excel at using the python data science software ecosystem for data analysis, prediction, visualization and storytelling.
In between contract assignments, I'm a stay-at-home parent with grade school children and a continuous learner.
Knowledge
- Python
- Data Science & Analytics
- Data Munging
- Jupyter Lab
- PyCharm
- Pandas
- Visualization: Matplotlib, Seaborn, Bokeh
- SQL
- Numpy
- Electrical Hardware Testing
- Electrical Engineering Lab Test Equipment
- Hardware Test Automation with Python
Links
GitHub Repositories
For Fun
- How to Create a Pivot Table in Excel with the Python win32com Module
- Portland OR Temperature Visualization
- OCR Image Processing with PyTesseract & CV2
Projects & Notebooks
- DataCamp: Unsupervised Learning in Python
- DataCamp: Supervised Learning with scikit-learn
- DataCamp: Statistical Thinking in Python II
- DataCamp: Introduction to Network Analysis in Python
- DataCamp: Interactive Data Visualization with Bokeh
- DataCamp: Introduction to Data Visualization in Python
- DataCamp: Introduction to Relational Databases in SQL (Postgre)
- DataCamp: Joining Data in SQL
- DataCamp: Statistical Thinking in Python I
- DataCamp: Fraud Detection with Python
- Skillz: Data Exploration
- DataCamp: Merging DataFrames with pandas
- DataCamp: Intro to Databases in Python
- DataCamp: Intro to Python for Finance
- DataCamp: Manipulating DataFrames with pandas
- DataCamp: pandas DataFrames
- UDACITY R Project: Prosper Peer to Peer Lending Data-set Exploration
- UDACITY Python Analysis: Titanic Survivability Parameters
- UDACITY Python Analysis: Open Street Map: Portland
Certifications
- Unsupervised Learning in Python
- Introduction to Shell
- Conda Essentials
- Supervised Learning with scikit-learn
- Statistical Thinking in Python (Part 2)
- Introduction to Network Analysis in Python
- Interactive Data Visualization with Bokeh
- Introduction to Data Visualization in Python
- Introduction to Relational Databases in SQL
- Joining Data in SQL
- Statistical Thinking in Python (Part 1)
- Fraud Detection with Python
- Intro to SQL for Data Science
- Merging DataFrames with pandas
- Python Programmer Track
- Introduction to Databases in Python
- Manipulating DataFrames with pandas
- Intro to Python for Finance
- pandas Foundations
- Cleaning Data in Python
- Udacity Data Analyst Nanodegree
- Introduction to Big Data
- Importing Data in Python 2
- Importing Data in Python 1
- Python Data Science Toolbox 2
- Python Data Science Toolbox 1
- Intermediate Python for Data Science
- Intro to Python
- Machine Learning
- DAT206x: Analyzing and Visualizing Data with Excel
- Using Python to Access Web Data
- Using Databases with Python
- Python Data Structures
- Getting Started with Python