Data Science 101
JANUARY 15, 2020
Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Bias can cause a huge error in experimentation results so we need to avoid them. We randomly recruit subjects for that.
Domino Data Lab
AUGUST 5, 2019
regulators and Volkswagen customers by implementing software specifically designed to cheat emissions tests.”. In 2010, Netflix cancelled their second recommendation contest after a privacy lawsuit. Also, data science work is experimental and probabilistic in nature. as well as consider the role of rights, harms, and justice.
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The Unofficial Google Data Science Blog
JULY 22, 2020
Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.
O'Reilly on Data
JULY 25, 2023
To provide some coherence to the music, I decided to use Taylor Swift songs since her discography covers the time span of most papers that I typically read: Her main albums were released in 2006, 2008, 2010, 2012, 2014, 2017, 2019, 2020, and 2022. This choice also inspired me to call my project Swift Papers.
Occam's Razor
SEPTEMBER 15, 2014
In blue is how much time we spent in 2010 and in blue the time spent in 2014. was the dramatic shift between 2010 to 2014 to mobile content consumption. Media-Mix Modeling/Experimentation. Media-Mix Modeling/Experimentation. Here's a graph that shows how US adults consume media, it shows time in hours.
Occam's Razor
MARCH 23, 2010
For big success you'll need to have a Multiplicity strategy: So when you step back and realize at the minimum you'll also have to use one Voice of Customer tool (for qualitative analysis), one Experimentation tool and (if you want to be great) one Competitive Intelligence tool… do you still want to have two clickstream tools?
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes. We must correct for multiple hypothesis tests. Make experimentation cheap and understand the cost of bad decisions. We ought not dredge our data.
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