Book Data Collection Experimentation ROI 
Domino Data Lab
FEBRUARY 4, 2019
We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.
Domino Data Lab
MAY 8, 2019
The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.
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Occam's Razor
AUGUST 13, 2012
Bjoern Sjut3: My main issue at the moment: How will multi-channel funnels and ROI calculations work in a multi device world? If your wish in the second part is to track effectiveness of advertising ( how to determine ROI ) then please see this post: Measuring Incrementality: Controlled Experiments to the Rescue! That is the solution.
Occam's Razor
MARCH 23, 2010
Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. It is possible that you'll be the exception and build the first clickstream data warehouse where you'll deliver positive ROI (against the Total Cost of Ownership ). Likely not.
Occam's Razor
OCTOBER 19, 2010
As defined in my second book Web Analytics 2.0 the analysis of qualitative and quantitative data from your website and the competition, 2. For more on why I recommend this specific order please see my second book, Web Analytics 2.0 , which many of you already have. Experimentation and Testing Tools [The "Why" – Part 1].
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