Welcome!
👨💻 I'm Divij Sinha, a data scientist and policy analyst, recently graduated in the MS in Computational Analysis and Public Policy at the University of Chicago.
🏛 I am currently a Research Engineer at the Mansueto Institute for Urban Innovation, at the University of Chicago. Previously, I've had the privilege of working at the Urban Informatics Lab at the Indian Institute for Human Settlements.
🌟 Throughout my career, I've been driven by the belief that technology, when thoughtfully applied, can be a powerful tool for public good. My experiences have spanned across data science, policy analysis, and urban informatics, each reinforcing my commitment to using data for societal improvement.
📚 I am seeking opportunities to apply my skills in real-world settings, collaborating with peers, academics, and industry professionals. I'm particularly interested in projects at the intersection of technology, data science, and social justice, aiming to contribute to equitable and effective public policies.
🌱 More code samples and project details are available upon request, demonstrating my approach to problem-solving and innovation in public policy.
- Python: Advanced data cleaning, analysis, modeling, backend development to inform policymaking and research.
- R: Advanced data cleaning, analysis, and modeling to inform policymaking and research.
- SQL: Database management and complex query formulation for data analysis projects.
- C: Knowledgeable in foundational programming concepts and applications.
- STATA: Utilized for econometrics and statistical analysis in various research contexts.
- GIS: Geospatial data analysis and visualization to support urban planning and policy decisions.
- Git: Version control for collaborative development and code management.
- Data Science Techniques: Machine Learning & Deep Learning, Web Scraping, Spatial Data & Cluster Analysis, Time Series Analysis
- Statistical & Economic Analysis: Descriptive & Inferential Statistics, Applied Regression Analysis & Econometrics, Microeconomics, Spatial Economics
- Research Methodologies: Experiment & Sampling Design, Survey Data Analysis, Program Evaluation & Analysis, RCT Design & Execution
I have extensive experience working the following datasets -
- Indian Census: From 1871 - 2011, used the Indian Census extensively, have huge amounts of data collected from the Indian Census
- American Census and ACS: Have worked deeply with federal US datasets, also extends to some state and local datasets, especially in Cook County, and the City of Chicago
- National Sample Survey, and Periodic Labour Force Surveys: Highly skilled at manipulation of large scale survey data
- Demographic and Health Surveys, and the National Family Health Survey: Lots of experience with international health data stemming from DHS and DHS-adjacent sources