Sadman Chowdhury Data Analytics track
Skills
- Excel
- Power Query
- Pivot Tables
- Data Analysis ToolPak
- Regression Analysis
- Scenario Modeling
- Tableau
- Dashboard Design
- Calculated Fields
- LOD Expressions
- Parameters & Containers
- Joins & Blends
- SQL
- Subqueries
- CTEs
- Window Functions
- CASE Statements
- SQL Joins
- Python
- Data Cleaning & Wrangling
- Exploratory Data Analysis (EDA)
- Merging & Reshaping Data
- JSON
- Time Series Analysis (ARIMA)
- Python-SQL Integration
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Classification
- Clustering
- Anomaly Detection
- AI Agent Development
- Project Management
- APIs
- Generative AI
- LLMs
- Prompting
- Streamlit
- Feature Engineering
- Statistical Testing
Featured project
PlayBase
A four-person team project that predicts a game's ownership tier (Flop, Niche, Moderate, Hit) from its first 30 days of review activity and outputs an investment recommendation (Walk Away, Hold, Double Down). The team engineered and validated a feature set, reducing it from 39 to 26 features with no loss in validation performance, using a ~42.5GB Steam reviews dataset covering ~22,086 eligible games. Sadman served as project coordinator and contributed to feature engineering, model evaluation, and development of the Streamlit application.
Resume
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Beyond the resume
Clubs and activities
- Dean's List
- Honors Program
Meet Sadman
Tell us who you are and what the role is. Darion Cole from TTPR will follow up by email and introduce you to Sadman.