no, Data Science is NOT Analytics! This is why.
Analytics identifies trends, Data Science builds Data Products
Data Science Shop
what makes Data Science succeed, tech for non-technologists, Data Science beyond algorithms
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Dormant Last read · last published · next check
Read 8 days ago and current, but nothing has been published for 21 months.
Latest episodes
the Data Science Shop project and the need to codify Data Science
what French cuisine and aerial combat can teach us about transforming the way we think about and practice Data Science
building Data Products: start small, fail fast, scale smart
the method to reduce uncertainty through experimentation
why your Data Product Cycle needs feedback loops
TL;DR While the Data Product Cycle might look like a sequential process (diagnosis → development → adoption → evolution), it actually operates through continuous feedback loops where later stages inform and shape earlier decisions.
three essential skills to master as a senior Data Science manager
second in the series: foundational skills for Data Scientist managers
HOW are effective Data Products built in Data Science?
aka the Data Product Cycle
the Data Scientist manager launchpad: three core skills
first in the series: foundational skills for Data Scientist managers
WHAT Data Products can Data Science build?
aka a Data Product taxonomy
communicating in Data Science: the hidden superpower
first in the series: essential soft skills for Data Scientists
WHERE are Data Products built (and maintained)?
a.k.a. the tech environment used by the Data Science Shop