Data
Analysis Documentation
Curated Learning Resources
- Data Science Practice 101: Always Leave An Analysis Paper TrailRandy advocates that data documentation makes the work of data scientists easily traceable and reproducible. This is particularly important when working on ad-hoc analysis requests, as it is easy for output to end up in a temporary directory with little context. There are several ways to package analysis deliverables, including Excel files, CSV files, slide decks, dashboards, and shared documents, in order to make them free-standing and easily traceable.
Related Skills
- ROI for Data Work
- MLOps Platforms
- Prioritization for Data Work
- Structuring Data Teams
- Effective Dashboards
- SQL
- Machine Learning
- Data Science Career Ladders
- Data Engineering
- Neural Networks
- Analytics
- Data Infrastructure
- Cohort Analysis
- Data Tools
- ETL
- Data Soft Skills
- Data Dictionary
- Data Governance
- Data Roadmaps
- Event Data
- Personalization
- OCR
- Data Warehouse
- Deep Learning
- Sampling Algorithms
- Data Intuition
- Linear Regressions
- Data Cleaning