Book Data-driven Experimentation Interactive 
O'Reilly on Data
JULY 28, 2020
It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Fair warning: if the business lacks metrics, it probably also lacks discipline about data infrastructure, collection, governance, and much more.) Agreeing on metrics. Don’t expect agreement to come simply.
CIO Business Intelligence
FEBRUARY 21, 2024
One is knowledge of the emerging mega trends in technology — data, AI, and machine learning — and the other is understanding organizational culture needed to advance the technology goals and to inspire contributors,” he says. We expected a couple thousand interactions when we implemented it.
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Occam's Razor
APRIL 8, 2013
We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This thought was in my mind as I was reading Lean Analytics a new book by my friend Alistair Croll and his collaborator Benjamin Yoskovitz. But it is not routine.
Occam's Razor
JUNE 6, 2007
I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. It has been such an amazing journey to write the book, and for it to come up almost exactly a year after I started this blog. Damini, Chirag and now the book! :). Part One: The book (my side of the story, details).
FineReport
APRIL 24, 2024
Learn Data Visualization Understanding the Importance of Visualizing Data Data visualization is a powerful tool for conveying complex information in a clear and impactful manner. Whether it’s through charts, graphs, maps, or other visual formats, mastering data visualization is crucial for anyone working with data.
Domino Data Lab
FEBRUARY 4, 2019
In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science. Introduction.
Domino Data Lab
MAY 8, 2019
For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. ML model interpretability and data visualization. back to the structure of the dataset.
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