Alexander
Levin-Koopman
As a lecturer in Applied Data Science at the University of Michigan School of Information, I'm passionate about solving problems through insights extracted from complex data sets. With a Master's degree in Applied Data Science (4.0 GPA) from the University of Michigan and a Bachelor's degree in Mathematics from the University of Colorado at Boulder, I've developed a strong foundation in statistical analysis, machine learning, and data visualization.
My academic background has equipped me with a unique ability to approach problems from multiple angles, think creatively, and communicate complex ideas effectively. While my experience is rooted in academia, I'm eager to apply my skills in a real-world setting and drive business outcomes through data-driven decision making.
I thrive on solving intricate problems and uncovering hidden patterns in data. My goal is to leverage my expertise to inform strategic decisions, optimize processes, and create value for organizations. If you're looking for a driven and analytical problem-solver who is passionate about data science, let's connect!
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Optimal Clustering KMeans
Project Description: Custom scikit-learn KMeans clustering implementation based on the Davies Bouldin score and the Calinski and Harabasz score. These scores don’t require ground truth knowledge.
Davies Bouldin score | Calinski and Harabasz score
Figure 1: Optimal Clustering Selection
This is an example of the clustering selection on a created dataset with 12 clusters
