Julian Olbinski

About me

• Check Out My CV

Welcome to my portfolio! I'm Julian, a Polish-American with a diverse professional and academic background eager to apply my broad skillset to a career in data analysis.

With a Master's in Psychology, a Bachelor's in Biology, and a minor in Mathematics, I have developed a deep appreciation for data-driven decision-making. My academic journey has also equipped me with the analytical skills essential for extracting meaningful insights from data.

I am proficient in SQL and Python, and skilled in visualization tools like Tableau and Excel, which allow me to apply my interdisciplinary expertise while working with complex datasets to generate sophisticated and detailed visualizations.

Additionally, my professional experience spans various roles, from organizing candidate screenings and interviewing subjects for a documentary at Big Other Productions to conducting exposure therapy sessions and managing program operations at the McLean Hospital OCD Institute. Through successfully navigating diverse work environments, I have developed strong interpersonal and administrative skills which will enable me to excel as a data analyst.

I can therefore confidently say that I have the organizational, communication and technical skills necessary to effectively integrate and contribute to your data analysis team. Feel free to explore my portfolio, in which I demonstrate my data analysis skills with real-world data.

SQL Projects

The overarching theme of projects 1-3 is the theme of progress, which I conceptualized as: to what degree is our world equal in terms of education and economic equality, and how far have we come in the development of green technologies, specifically in terms of the production and use of electric vehicles. In these projects, I explore data from various sources using MySQL Workbench. My final project is a data cleaning SQL query for a publicly available data set concerning affordable housing construction in New York City.

Project 1: Electric Car Use

In my first project, I analyze the manufacture, sale, and use of electric vehicles (EVs) between 2010 and 2022 among various countries. Some of the statistics generated include the change in EV sales over time per country, most recent rankings of EV sales and use, as well as EV use and sales relative to the total car market.

• Project 1 Query

Project 2: Worldwide Education and Literacy

In my next project, I explore the most recent/robust data on the differences in education and literacy between different countries. Some metrics include youth literacy rate (between 16 and 24 years old), average years of schooling, and GDP per capita vs literacy test scores.

• Project 2 Query

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Project 3: Economic Inequality and GDP

In my final data exploration project, I examine worldwide economic inequality before the COVID-19 pandemic. Since the pandemic had far-reaching negative effects on the global economy, I decided to focus on inequality measures before 2020. I analyzed various GDP metrics – including total GDP, GDP per capita and GDP per employee – as well as two measures of economic inequality: the Gini coefficient and the Atkinson index.

• Project 3 Query

Project 4: NYC Affordable Housing Construction

This SQL script is designed to clean a dataset covering the construction of affordable housing units in New York City from 2014 to 2018. Data cleaning tasks included: renaming columns, changing data types, removing duplicates, and miscellaneous reformatting tasks.

• Project 4 Query

Tableau Dashboards

To complement my SQL projects 1-3, I created Tableau dashboards corresponding to each project. For these dashboards, I selected the most salient data from my SQL queries and generated interactive visualizations. Click on "See Dashboards" to view them in my Tableau profile.

• See Dashboards

Python Projects

The following projects showcase my versatility in using Python for various data analysis tasks. Some of the skills I demonstrate include object-oriented programming, web scraping, file handling, SQL integration, as well as time-series analysis and forecasting.

I also employ numerous python libraries including NumPy, Pandas, Statsmodels, SciPy, Scikit-learn, and Seaborn.

Repository 1: Analysis of 5 Largest Green ETFs

This repository contains a jupyter notebook file in which I analyze the performance of the top 5 largest ETFs geared towards sustainability and green technologies with respect to each other and the Vanguard Total Market ETF (VTI). In this project I use the following libraries to conduct my analyses: NumPy, Pandas, Matplotlib, Seaborn, and SciPy.

• See Repository

Repository 2: Central Park Temperature Time-Series Analysis

For this project, I conducted a time-series analysis of the average monthly temperatures in Central Park, NY from 1870-2023. My analysis culminated in a SARIMA model which I used to predict the temperatures for the remainder of 2024. To run my analyses, I used the following libraries: NumPy, Pandas, Matplotlib, Statsmodels, and Pmdarima.

• See Repository

Repository 3: Remote SQL Query Tool

This is a script that allows you to connect to a remote MySQL server and execute an SQL query.

• See Repository

Repository 4: Web Scraping for Images

This repository contains a webscraping script for downloading and saving many of the images that I used for this website.

• See Repository

Repository 5: English/Polish Number Translator

This script asks the user to input an integer from 0 to 1,000,000, and returns that number written out in English, and Polish, along with the IPA and English pronunciations of the Polish numbers.

• See Repository

Repository 6: Text File Generator and Word Counter

Finally, this script asks the user to write to a text file they created, and subsequently returns a total count of the English words in the text, together with a count of the unique English words it contains.

• See Repository

Repository 7: Games

This repository contains simple scripts for the games tic-tac-toe and blackjack in which I employ object-oriented programming.

• See Repository

Excel Projects

The following excel projects were selected to demonstrate my ability to work with large data sets which need to be cleaned/adjusted for clarity. The first project contains a dashboard which visualizes the data pertaining to the rates of heart disease among Americans in 2020, and the second provides a breakdown of the construction of affordable housing in New York City from 2014-2018.

Dashboard 1: U.S. Heart Disease Statistics

This dashboard explores the effects of lifestyle and various health conditions on the rates of heart disease among Americans. Sepecifically, it examines these effects between men and women, and between difference races and age groups.

• See Project

Dashboard 2: NYC Affordable Housing Construction

This dashboard organizes the statistics on the construction of affordable housing in New York City from 2014 to 2018 according to borough, start/finish year, and level of income.

• See Project

Elements

i = 0;

while (!deck.isInOrder()) {
    print 'Iteration ' + i;
    deck.shuffle();
    i++;
}

print 'It took ' + i + ' iterations to sort the deck.';