📅 2024-11-06 — Session: Streamlined ETL and DHS Data Handling
🕒 15:00–16:15
🏷️ Labels: ETL, DHS, Data Analysis, Python, SQL
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to streamline ETL processes using SQL and enhance DHS data handling techniques for socio-economic analysis.
Key Activities:
- Reviewed methods to capture clipboard history across different operating systems to improve productivity.
- Explored a typical ETL workflow using SQL, including data extraction, cleaning, and initial transformation steps.
- Discussed tips for managing LinkedIn posts and integrating them into ETL processes for data engineering.
- Assisted in DHS data analysis by aligning dataset variables and verifying file paths for CSV files.
- Converted .DO and .DCT files to .CSV using Python and Stata, ensuring variable presence in the resulting files.
- Handled .dta files using pandas and explored methods for loading .DAT files using .DCT specifications.
- Addressed missing data columns for socio-economic analysis and outlined steps for data retrieval and merging.
Achievements:
- Finalized a list of variable names for DHS data analysis, identifying key socio-economic and health indicators.
- Developed a modular approach to load data from .DCT and .DAT files, facilitating data verification.
- Provided guidance on handling DHS weights in data analysis to ensure accurate population representation.
Pending Tasks:
- Further exploration of clipboard management tools to enhance cross-platform productivity.
- Complete the integration of LinkedIn data into ETL workflows for comprehensive data analysis.