Category: Blog


Data science discussions and general updates from Conlan Scientific.

Conlan Scientific Partners with UVA for Data Science Course

Conlan Scientific is proud to announce that it has partnered with the University of Virginia Department of Statistics to teach STAT 4559: Data Science Practicum during the Spring 2018 semester. The Arrangement Chris Conlan and Professor Jeff Holt will co-teach the class to a small group of students. Conlan Scientific ... Read more ...

Avoiding Data Leakage in Machine Learning

To properly evaluate a machine learning model, the available data must be split into training and test subsets. Data leakage occurs when, in one way or another, information regarding the test set inappropriately influences the training or evaluation of the model. This causes us to overestimated the performance of a ... Read more ...

Misconceptions about P-values

P-values are currently the most common metric used to draw conclusions from research. Given this, one would expect they are well-understood and non-controversial. Nothing could be further from the truth. Recently, misconceptions about their use and misinterpretation of their meaning have even triggered a statement from the American Statistical Association ... Read more ...

Danger in Machine Learning: Human Error

Recently a landmark study[1] was published that claimed to have identified a danger of "black box" machine learning methods. The main message of the article is that there is a trade-off between intelligibility and accuracy. They write that, while models like neural networks often more accurate, it is difficult ... Read more ...


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