Azizul Islam
3+ years of professional research data analysis experience, specializing in developing advanced statistical tests for forensic, merchandising, and consumer behavior analysis.
Skilled in SQL, Excel, Tableau, Python, and R, with strong teamwork and problem-solving to deliver precise insights and effective solutions.
My Data Portfolio
Statistician | Data Specialist
Learn About Me
Education
- MS, Statistics - Iowa State University (Pursuing)
- MS, Merchandising - Oklahoma State University
- BS, Textile Engineering - Bangladesh University of Textiles
Work Experience
Statistical Research Assistant @ Iowa State University (January 2024 - Present)
- Developing an adjusted statistical test for bullet comparison, reducing observed Type I error rates from approximately 29% to 5%, increasing test sensitivity and forensic accuracy, and improving statistical power by 50%.
- Used selective inference methods to adjust null distributions, reducing p-value bias, lowering the fold change from 5.78 to 0.9, and achieving 95% alignment with expected error rates.
Data Research Analyst @ Iowa Agriculture and Home Economics Experiment Station (August 2020 - May 2023)
- Conducted interviews with business assistance providers, identifying key patterns and insights on post-COVID recovery strategies.
- Used rank analysis method to sort and prioritize post-COVID recovery strategies, enhancing the accuracy of identifying the most impactful factors by 25%, automating the process, thereby reducing the time to identify trends in recovery planning by 30%.
- Analyzed interview data using MAXQDA with 93% inter-coding reliability, presenting findings at an international conference.
Data Projects
Ames Weather Data ETL Pipeline
I used the Open-Meteo API to collect Ames, Iowa weather data from March to April 2024 and built a serverless ETL pipeline using Python, SQL, and AWS tools like Lambda, Kinesis Firehose, and Glue. This enabled me to transform real-time data and create insightful dashboards in Grafana.
Global Layoffs: Data Cleaning with SQL
In this project, I used SQL to clean and prepare a dataset on global layoffs between 2020 and 2023 for exploratory data analysis (EDA). The primary goal was to transform raw data into a clean, standardized format suitable for in-depth analysis.
Global Layoffs: An EDA with SQL and Tableau
In this project, I conducted an exploratory data analysis on layoff trends from 2020 to 2023, using SQL to query and analyze the data, and Tableau to visualize key insights such as industry impacts and geographical distribution. I focused on identifying patterns and significant factors contributing to layoffs.
Data Cleaning in MS Excel
In this project, I used MS Excel to clean and organize a dataset by applying various data cleaning techniques, such as removing duplicates, correcting formatting, and splitting columns. By leveraging Excel’s built-in functions and tools, I transformed the raw data into a structured format ready for accurate analysis.
Email Sign-Up Optimization: A/B Testing with Python
In this project, I used Python to design and run an A/B test for CityFit’s pre-launch page, comparing a red submit button (control) to a green one (treatment). I analyzed visitor data, calculated sample sizes, and conducted statistical tests. The results showed a significant improvement in sign-ups with the green button, leading to a recommendation for its use.
