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

Linear Programming in Python

Francisco Alvarez shows us an example of linear programming in Python: The first two constraints, x1 ≥ 0 and x2 ≥ 0 are called nonnegativity constraints. The other constraints are then called the main constraints. The function to be maximized (or minimized) is called the objective function. Here, the objective function is x1 + x2. Two classes of […]

Read More

Exploratory Data Analysis with inspectdf

Laura Ellis continues a dive into Exploratory Data Analysis, this time using the inspectdf package: I like this package because it’s got a lot of functionality and it’s incredibly straightforward to use. In short, it allows you to understand and visualize column types, sizes, values, value imbalance & distributions as well as correlations. Better yet, […]

Read More

Categories

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