Currently set to No Index

## Millennials vs. Gen Z: Why Marketers Need to Know the Difference

In recent years, there’s been a common misconception that Gen Z and millennials are essentially the same.
When companies discuss reaching younger audiences, many often lump Gen Z and millennials into the same group and create one campaign strategy that they believe fits both groups.
Sure, many millennials and Gen Zers are considered “young adults.

## How to write a Transformer Recipe for Driverless AI

What is a transformer recipe?
A transformer (or feature) recipe is a collection of programmatic steps, the same steps that a data scientist would write a code to build a column transformation. The recipe makes it possible to engineer the transformer in training and in production.

## Using Matched Pairs to Test for Cannibalization

When a company introduces a new product into the same market served by an existing one, it is possible that the new product will achieve success at the expense of the first. For example, when Netflix introduced movie downloading, it knew it would put a dent in DVD subscriptions. This is called cannibalization.

## How to Leverage Model-Driven Architecture for Better Application?

Designing an application is not a child’s play in any sense and developing an enhanced user experience for the same is quite a task at hand. For this matter, there are numerous approaches adopted by developers and enterprises to design better application and its interface to transform the entire user experience.

## Advice to aspiring data scientists: start a blog

Last week I shared a thought on Twitter:
When you’ve written the same code 3 times, write a functionWhen you’ve given the same in-person advice 3 times, write a blog post— David Robinson (@drob) November 9, 2017
Ironically, this tweet hints at a piece of advice I’ve given at least 3 dozen times, but haven’t yet written a post about.

## Variance of a Product of Random Variables

If X(1), X(2), …, X(n) are independent random variables, not necessarily with the same distribution, what is the variance of Z = X(1) X(2) … X(n)? It turns out that the computation is very simple:
In particular, if all the expectations are zero, then the variance of the product is equal to the product of the variances. See here for details.

## Would you buy insights from this guy?

“The first thing you need to do is get all the data in the same place”. We all remember the mantra that launched a thousand painful IT projects. The argument was that this would enable a “360-degree view of the customer” or “data-driven” decisions.

## State Rating Agencies

People who buy cars can often get independent evaluations on them from auto mechanics. Same with house inspectors. More generally, consumers can get help in evaluating products from Consumer Reports, while bond investors can get help evaluating bonds via bond rating agencies like Moody’s. Charity donors can use GiveWell.

## Misleading Metrics and Irrelevant Research (Accuracy and F1)

If one algorithm achieved 98.2% accuracy while another had 98.