About Me

I am an integrative biologist with a strong background in animal biology, functional morphology, data analytics, and software development gained through 6 years of education in biology and 4 years of combined research and industry experience. I am proficient in Python, R, and git version control with experience in object detection and natural language processing. I would like to leverage my experience to contribute to the analysis of biomedical and genomics data.

Skills

When I first made this website the first sentence here read "Ensuring that data is properly cleaned, sorted, and managed is a vital first step to any data analytics workflow." I stand by that statement. I have since grown my reptoire to include a stronger focus on software design, statistics, and machine learning, but proper data engineering always plays a vital role in any project I work on. These are some of the tools I use most often in my work:

  • Python Object Oriented Design
  • Tableau
  • SQL
  • Unsupervised machine learning
  • Python: NumPy and Pandas
  • rStudio: Tidyverse and ggplot

Projects

Many of my programming projects focus on image analysis, specifically clustering algorithms in Python that can be useful to color and pattern analysis. I am continuously growing my knowledge base surrounding program design and I believe that shows with each new project. I focus on readability with well documented code bases and strive to have my projects fit seamlessly into other programs across operating systems. Click the GitHub icon at the bottom of the page to check them out.

Visualizations

Visualizations should be simple, yet visually striking, as they are key to understanding and communicating the value of data. The following visualizations were made in Tableau using publicly available data sets that I found interesting.

Factors contributing to population change by state

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The first graph represents how net births and migrations contribute to the population change of each state, shown for 2010 and 2015. It appears that population growth in 2010 was due to net births, while in 2015 it was due to migration. This second graph confirms that in the states with the highest population growth, net births was the main contributor in 2010 but not in 2015. Attempts were made to find the driving force behind these trends using various data sets such as median state income, quality of life, and job growth per state for each year, but no correlation was found. Overall, it was a good excercise that forced me to pull in data from various sources to be used in an attempt to better understand trends in the initial dataset.

Visualizing state rankings by metric

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I encountered this data from cnbc ranking each state and I was curious to see how exactly the states were ranked. The first graph shows the total score for each state by the sum of each category score. As a lower rank denotes the better state, the lower the total score, the highest rank. While the graph follows this general trend, it is not a step-wise ranking based on total score. The second graph seeks to visualize if any category significantly contributes to the total score. As a lower score is better, this graph looks at the percent of total of the inverse of each category, so a higher ranking category score appears as a larger colored area of each column. It appears, however, that no category significantly contributes to a higher state ranking, so it is unclear exactly how these scores were calculated.