CIO Business Intelligence

JUNE 13, 2023

As chief digital officer of Kotak Mahindra Bank, Deepak Sharma has been instrumental in driving the bank’s digital transformation, future-ready initiatives, and business model innovation strategies. As I was setting up the NRI banking and remittance platform in 2010, I started getting involved with technology.

IT 106

IT Digital Transformation Uncertainty Risk Management 106

O'Reilly on Data

JULY 20, 2021

When we’re building shared devices with a user model, that model quickly runs into limitations. That model doesn’t fit reality: the identity of a communal device isn’t a single person, but everyone who can interact with it. With enough data, models can be created to “read between the lines” in both helpful and dangerous ways.

Risk 306

Risk Software Interactive Modeling 306

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 64

Forecasting Statistics Modeling Software 64

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

The Unofficial Google Data Science Blog

AUGUST 27, 2019

The bucketing method also changes the importance sampling to a stratified sampling setting, and allows us to use binomial confidence intervals to estimate the uncertainty of our estimate (more on that later). Statistics in Biopharmaceutical Research, 2010. [4] An Introduction to Model-Based Survey Sampling with Applications. [6]

Metrics 98

Metrics Statistics Uncertainty Optimization 98