Data Science: Expectations vs. Reality
This article has been making the rounds. It provides some useful insights for anyone who may have misconceptions about what it's like to be a data scientist. However, after reading, doing my own research, and being in the workforce for long enough, most of the points could pertain to most technical roles.
TL;DR; If you are in the data science (or another highly technical) field, expect people (particularly leadership) to have no idea what you do and why. The power and understanding come from the insights you find and the story you tell through your work.
What (data science) hiring managers are really looking for
Continuing with the topic of career, a Reddit user and commenters provided some handy job-seeking tips from the perspective of data science hiring managers.
Starting a Data Quality Checklist
This list is a useful starting point for managers and analysts who need to vet a dataset before digging in too far. While extensive, this list is by no means exhaustive.
Not much new data-specific news this week, but since the COV-19 pandemic is still very much in our collective attention, I’m going to keep this callout going. The more data we share about this outbreak, the better future generations will be prepared.
Dashboards: Johns Hopkins ArcGIS Dashboard [United States By County Dashboard](https://app.powerbi.com/view?r=eyJrIjoiMDkzZjQwNDMtZmI1Zi00YmVkLWExMTMtNDRjMjcwNWQ5ZGExIiwidCI6IjE1MjgxOGIxLTdmMTUtNDM3YS1hYzBiLTkyNDQwNzgwMzQ0ZCIsImMiOjN9&fbclid=IwAR0sB3j-SvuYu8dxdwSMX8Pp20m3eSBO7a5v6C1e6W6WgRrWn3-TwWz9IuA\) nCoV2019.live Dashboard
If you have any other dashboards to share, please send them my way.