The Data Science Delusion

Anand Ramanathan has a strong critique of “data science” as it stands today:

Illustration: Consider the sentiment-tagging task again. A Q1 resource uses an off-the-shelf model for movie reviews, and applies it to a new task (say, tweets about a customer service organization). Business is so blinded by spectacular charts [14] and anecdotal correlations (“Look at that spiteful tweet from a celebrity … so that’s why the sentiment is negative!”), that even questions about predictive accuracy are rarely asked until a few months down the road when the model is obviously floundering. Then too, there is rarely anyone to challenge the assumptions, biases and confidence intervals (Does the language in the tweets match the movie reviews? Do we have enough training data? Does the importance of tweets change over time?).

Overheard“Survival analysis? Never heard of it … Wait … There is an R package for that!”

This is a really interesting article and I recommend reading it.

Related Posts

Introduction To Bayesian Statistics

Kennie Nybo Pontoppidan has just completed a course on Bayesian statistics: Last month I finished a four-week course on Bayesian statistics. I have always wondered why people deemed it hard, and why I heard that the computations quickly became complicated. The course wasn’t that hard, and it gave a nice introduction to prior/posterior distributions and […]

Read More

Time-Varying Models

Lingrui Gan explains how to model for parameters whose effects change over time: We can frame conversion prediction as a binary classification problem, with outcome “1” when the visitor converts, and outcome “0” when they do not. Suppose we build a model to predict conversion using site visitor features. Some examples of relevant features are: time of day, geographical […]

Read More

Categories

November 2016
MTWTFSS
« Oct Dec »
 123456
78910111213
14151617181920
21222324252627
282930