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The mainframe is dying: Long live the mainframe application!

CIO Business Intelligence

IBM’s current mainframe models, the z15 T01 and z15 T02, were introduced in September 2019 and May 2020 respectively, and the company still offers follow-on service for machines right back to the zEC12 released in September 2012.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. A naĂŻve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means.

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Real-Real-World Programming with ChatGPT

O'Reilly on Data

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.

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Estimating causal effects using geo experiments

The Unofficial Google Data Science Blog

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. A geo experiment is an experiment where the experimental units are defined by geographic regions. Structure of a geo experiment A typical geo experiment consists of two distinct time periods: pretest and test.

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Unintentional data

The Unofficial Google Data Science Blog

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.