Data Literacy
summary
Claims that only 11% of employees feel confident with data and 67% of executives avoid their analytics tools drives a focus on upskilling, governance and self‑service. Practical tools shown include pandas, PySpark, Dask, numpy, scikit/keras, matplotlib/plotly, ipywidgets and notebooks used as interactive playbooks that can write Excel, surface feature rankings to avoid black boxes, and host exploratory dashboards. Roadblocks listed are organizational silos, missing leadership anchoring and duplicate data pipelines; recommendation is governed pipelines plus interactive notebooks to move teams from descriptive to predictive analytics.
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chapters (16)
- 0:00Introduction and welcome
- 1:59What is data literacy
- 7:20Why data literacy matters
- 13:21Skills involved in data literacy
- 15:32Evolution of data access
- 19:06Python for data literacy
- 21:12Analyzing data with Python
- 22:54Communicating insights effectively
- 27:52Avoiding data bias
- 29:17Data literacy vs data science
- 31:03Self-service analytics
- 32:10Levels of analytics
- 34:20Encouraging curiosity and critical thinking
- 37:25Roadblocks to data literacy
- 40:15Key takeaways and future outlook
- 42:03Q&A session
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