skill gap in analytics
summary
Argues that the skills shortage is role-specific not a lack of mythical all‑in‑one data scientists, with 50% of data science work being data preparation and distinct needs for Big Data admins, developers, data scientists, domain experts and visualization specialists. Lists practical tools and skills: Hadoop + MapReduce + Flume, Linux, Python/SAS, R, Tableau. Recommends hiring plus upskilling via a three‑tier training pyramid, measured by assessments with capstone projects, project‑readiness and platform completion/ROI rather than attendance alone.
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chapters (18)
- 0:00Introduction
- 0:15Overview
- 1:55Data Science ML lifecycle
- 5:45Data Science roles
- 7:45Business Analyst roles
- 14:08Questions to ask before training
- 15:25One size doesnt fit all
- 17:04Just in time training
- 17:25Application is key
- 18:27Unreasonable expectations
- 18:47Ideal way to teach analytics
- 20:07Look for insights
- 20:37Conclusion
- 21:23Three things you can do
- 22:40Is there a skill gap
- 23:38Who is playing multiple roles
- 24:40Is it available
- 26:22Best practices
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