#### Instructor Spotlight: Prof. David Unwin

Prof. David Unwin has guided, developed and taught the spatial analysis curriculum at Statistics.com since 2005. David lives in central England, about an hour north of the storied Rothamsted agricultural research center. Until his retirement in 2002, he was Professor of Geography at Birkbeck College,…

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#### Statistics in Agriculture: Encycloweedia

Weeds are big business - the global herbicide market is over \$35 billion annually. Weeds are also big government (think "invasive species"). California's listing of weeds is called Encycloweedia, and the state publishes a quarterly newsletter called Noxious Times. Colorado publishes a similar periodical, Invader.…

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#### Tensor

A tensor is the multidimensional extension of a matrix (i.e. scalar > vector > matrix > tensor).

#### Problem of the Week: Missing Data

Question: You have a supervised learning task with 30 predictors, in which 5% of the observations are missing.  The missing data are randomly distributed across variables and records. If your strategy for coping with missing data is to drop records with missing data, what proportion…

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#### Student Spotlight: Barry Eggleston

Barry Eggleston is a health research statistician who has worked on both clinical trials and observational studies, and is currently with RTI in North Carolina. In his early career, his work was solely designing and analyzing clinical trials using typical biostatistics methods ranging from t-test…

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#### A Deep Dive into Deep Learning

On Wednesday, March 27, the 2018 Turing Award in computing was given to Yoshua Bengio, Geoffrey Hinton and Yann LeCun for their work on deep learning. Deep learning by complex neural networks lies behind the applications that are finally bringing artificial intelligence out of the…

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#### Industry Spotlight: Credit Scoring

In the U.S., credit scoring is dominated by three companies - Experian, TransUnion and Equifax, employing roughly 30,000 people. An important player in the scoring methodology is FICO, previously Fair Isaac Corporation, and the scores are typically called "FICO scores." Credit scoring is the oldest…

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#### Industry Spotlight: The IRS is Watching You

The IRS (U.S. Internal Revenue Service) has been using computers to choose tax returns for audit since 1962. Early on, the selection was rule-based, but the IRS turned to statistical modeling in 1969, using the oldest predictive analytics model in the toolbox - discriminant analysis.…

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#### Book Review: Weapons of Math Destruction

Cathy O'Neil's Weapons of Math Destruction, when it was first published in 2016, sounded an early alarm about the big data algorithms and their potential for social evil. The cover is adorned with a robotic death's head and the subtitle reads "How Big Data Increases…

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#### Historical Spotlight: Alan Turing

80 years ago, in 1939, Alan Turing began work on the code-breaking system that would eventually prove key in helping Britain survive the German submarine threat in the Atlantic. Last month, the Turing Award in computer science prize (sometimes referred to as the "Nobel Prize…

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#### Confusing Terms in Data Science – A Look at Synonyms

To a statistician, a sample is a collection of observations (cases).  To a machine learner, it’s a single observation.  Modern data science has its origin in several different fields, which leads to potentially confusing  synonyms, like these:

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#### Confusing Terms in Data Science – A Look at Homonyms and more

To a statistician, a sample is a collection of observations (cases).  To a machine learner, it’s a single observation.  Modern data science has its origin in several different fields, which leads to potentially confusing homonyms like these:

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#### Confusing Terms in Data Science – A Look at Synonyms, Homonyms and more

To a statistician, a sample is a collection of observations (cases). To a machine learner, it's a single observation. Modern data science has its origin in several different fields, which leads to potentially confusing homonyms and synonyms, like these: Homonyms (words with multiple meanings): Bias: To…

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