What I do
Software Development
Microsoft Power Apps, Python, JavaScript
Data Analytics
Data analysis, management, and visualization
Automation
Power Automate, Form Automation, CRM Automation
Who I am
Hi, my name is Dylan Rose.
I am a professional Software Developer and Data Analyst.
I am comfortable with high-code, low-level development as well as low-code rapid development.
I have significant experience with automation and development in the Microsoft Power Platform ecosystem, as well as a strong data-heavy foundation in Python and JavaScript. I have experience building and managing apps with Power Apps and connecting them to data management flows in Power Automate.
I also have experience with database design and management, writing SQL queries and reports in Microsoft Access, building ETL pipelines with web APIs, and building out advanced Excel worksheets with automation through VBA macros or Typescript on the web.
I am a Certified Internet Web Professional possessing both a CIW Data Analyst Certification and MTA Certification in Software Development.
Recent Projects
Air Quality Analysis 1985-2020
A Tableau story consisting of several dashboards exploring the US Environmental Protection Agency’s Air Quality Index (AQI) from 1985 to 2020. My main findings were that air quality has, on average, improved since the 1980s, and that this improvement has occurred along with a reduction of national SO2 emissions. The main technology used in this document was Tableau.
Investigating TMDB Movie Titles
A Jupyter Notebook that documents the data wrangling and exploratory data analysis of a dataset containing 10,000 titles from The Movie Database. Explorations include audience preferences in film genre over time, as well as specific factors that may affect a film’s profit and revenue. The main technologies used in this project were python, pandas, and matplotlib.
Red Wine Quality Exploratory Data Analysis
A knitted HTML document that presents an exploratory data analysis of a dataset containing review data for 1,599 red wine varieties. The dataset includes several chemical and physical properties, as well as the final overall quality assessment for each wine. The main technologies used in this project were R, RStudio, and ggplot2.
