Finn Mokrzycki
About Me
Data Science @ UVA
I am currently a masters candidate at the University of Virginia School of Data Science. Click here to see my current courseload.
I have both industry and research experience in the field, applying analytical techniques to solve real-world problems and contribute to innovative studies.
LinkedIn
linkedin.com/in/finn-mokrzycki
GitHub
github.com/finnmok
Portfolio
My Skills
Data
Gathering
RESTful APIs, Docker, SQL, Databases, CSVs. You name it, I use it.
Manipulation
Avid Numpy enjoyer. Pandas frequenter.
Analysis
Statistical, Written, Graphic, Numeric? Yes.
Modeling
Statistical, Machine Learning, Deep Learning, Large Language, or a Pre-Built, and I'm game.
Deployment
Integrate, Monitor, Update. Repeat!
Presentation
Slide Deck, White Paper, Test Run, or Email? Your pick.
Coursework
“
Prior Semesters
- Foundations of Computer Science
- Bayesian Machine Learning
- Statistical Learning
- Linear Models
- Data Ethics
Masters
Summer & Fall Semesters
“
Other Graduate Level Courses
- (All Below are from the UVA Graduate Statistics Department)
- Statistical Computing with Python and R
- Statistical Machine Learning
- Clinical Trials Methodology
- Categorical Data Analysis
Undergraduate
at University of Virginia
“
Current Courses
- Decoding Large Language Models
- Exploratory Text Analytics
- Big Data Systems
- Deep Learning
- Capstone
Masters
Current/Final Semester
“
Prior Semesters
- Foundations of Computer Science
- Bayesian Machine Learning
- Statistical Learning
- Linear Models
- Data Ethics
Masters
Summer & Fall Semesters
“
Other Graduate Level Courses
- (All Below are from the UVA Graduate Statistics Department)
- Statistical Computing with Python and R
- Statistical Machine Learning
- Clinical Trials Methodology
- Categorical Data Analysis
Undergraduate
at University of Virginia
“
Current Courses
- Decoding Large Language Models
- Exploratory Text Analytics
- Big Data Systems
- Deep Learning
- Capstone
Masters
Current/Final Semester
“
Prior Semesters
- Foundations of Computer Science
- Bayesian Machine Learning
- Statistical Learning
- Linear Models
- Data Ethics
