I graduated from the University of Iceland in February with a master's degree in applied
statistics, achieving a first-class grade. I am seeking opportunities to gain experience in the field of data science and
analytics in Zürich.
Given that Iceland is part of the EFTA countries, I have the right to enter, live, and work
in Switzerland without complications.
Skills Overview
My education has provided me with a solid foundation in analytical subjects, preparing me for data-focused projects.
I can efficiently organize, analyze, and create visualizations for data.
Furthermore, I have experience with various statistical tests and prediction models.
With my background in psychology, I am skilled in survey design and understanding human behavior,
providing valuable insights for data analysis.
Excited to level up my data skills, I'm currently diving into programming Shiny apps with R and learning new prediction methods.
An internship or a junior role seems like a great opportunity to learn more and bring some fresh ideas to a new workplace.
The Zurich Traffic Accident Analysis Dashboard provides insightful visualizations
and summary statistics related to police-registered road traffic accidents in
Zurich since 2011. Using data sourced from open data Switzerland, which
includes detailed records of accident locations, times, types, and severity
categories, this dashboard offers a comprehensive overview of accident trends.
Built with Quarto and enhanced by Shiny components, it features interactive
filtering options to explore data by year, month, day, and type of accident.
The map visualization is powered by Leaflet, and the plots are rendered using
Plotly to provide a dynamic, user-friendly analysis tool.
See Code
In summer 2024, I worked as a data analyst for the Union of Icelandic Captains.
Their objective was to make better use of publicly available data by creating simple,
easy-to-read Power BI dashboards.
The Reykjavik Swimming Pool Attendance Dashboard provides insightful visualizations
and summary statistics related
to swimming pool attendance in Reykjavik. Using past data sourced from the official
website of Reykjavík city (https://gagnagatt.reykjavik.is/), which records the number of
people entering the pool each hour,
this dashboard offers a comprehensive overview of attendance trends.
With interactive features such as selecting specific swimming pools,
time ranges, weekdays, and dates, users can explore patterns in attendance.
This project, conducted in R, predicted housing prices in Reykjavík. The dataset underwent cleaning, and analyses included variable adjustments and outlier removal. Comparing lasso regression and Random Forest, the latter proved more accurate in forecasting Reykjavík housing prices.
In this project, I conduct a time series analysis of the number of passengers
passing through Keflavik Airport and attempt to fit a model that predicts the monthly
number of passengers for data the model has not seen. The data for the number of passengers
passing through Keflavik Airport was obtained from the
Iceland Statistics Website: https://statice.is/statistics/business-sectors/tourism/passengers/.
After relocating to Zurich, a question that frequently came up was whether it was excessively expensive.
I often explain that, while some things are pricier, others are not, like beer.
To better grasp if Zurich is more expensive than Reykjavik,
I created a very simple yet effective comparison dashboard in Power BI using data from www.numbeo.com.
In my Bachelor's thesis, I conducted a study on the psychological phenomenon
known as priming. The aim of this research was to explore whether individuals possess
the ability to encode the probability distribution of targets during a visual search
task. Additionally, the study examined whether these encoded probabilities influence
the efficacy of priming in visual search.
In my Master's thesis, I conducted research in collaboration with Statistics Iceland,
examining the potential of imputation using available administrative data.
The assessment was based on prediction accuracy.
To achieve this, I investigated various multiple imputation approaches as well as other standard prediction methods.