Remove 2016 Remove Data mining Remove Testing Remove Visualization
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Belcorp reimagines R&D with AI

CIO Business Intelligence

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs. This allowed us to derive insights more easily.”

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Unlock The Power of Your Data With These 19 Big Data & Data Analytics Books

datapine

An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”. If we had to pick one book for an absolute newbie to the field of Data Science to read, it would be this one.

Big Data 263
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Figure 4: Visualization of a central composite design. Modeling live experiment data Data scientists at YouTube are rarely involved in the analysis of typical live traffic experiments. 17:263-287, 2016. [10] Testing Statistical Hypotheses. Journal of Machine Learning Research, 17(83):1–5, 2016. [23]

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

2016) for an example of this technique (LIME). Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. After forming the X and y variables, we split the data into training and test sets. Toy example to present intuition for LIME from Ribeiro (2016). See Ribeiro et al.

Modeling 139
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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

To make sure the reliability is high, there are various techniques to perform – the first of them being the control tests, which should have similar results when reproducing an experiment in similar conditions. Here they speak about two use-cases in which COVID-19 data was used in a misleading way. 3) Data fishing.