How to Start a Career in Data Science: Tips & Experiences
summary
Claims an end-to-end starter path: spend 70% of project time on analysis, pick relevant sources, model as star schema, and build ETL mappings that validate and transform staged data into a dimensional data warehouse. Shows concrete tools and steps: Oracle SQL Developer Data Modeler for logical-to-relational design, IBM DataStage (InfoSphere) for mappings and loads, Oracle Enterprise Data Quality used on one project, and Oracle BI EE to create static and dynamic dashboards with drilldowns and filters. Cites the surprising rule of thumb that 80% of company data goes unused.
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chapters (8)
- 0:00Intro
- 0:38Welcome
- 1:57Data Issues
- 4:56Business Analysis
- 11:50Development
- 25:55Conclusion
- 26:47Questions
- 30:13Data Quality
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