Tag: data science

CRISP-DM Data Preparation: Data Selection

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process.   Previously, we looked at an overview of the methodology as a whole as well as the Business Understanding and Data Understanding stages.  Next, we examine the stage of Data Preparation.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

CRISP-DM Data Understanding: Simpson’s Paradox

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process.   Previously, we looked at an overview of the methodology as a whole as well as the first step, Business Understanding.  Next, we examine the stage of Data Understanding.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

CRISP-DM Business Understanding: Creating Buy In

CRISP-DM

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process.   Previously, we looked at an overview of the CRISP-DM methodology as a whole.

For our initial step along the journey, we will examine the stage of Data Understanding, followed by Data Preparation, Modeling, Evaluation, and Deployment.  As we explore the process, we hope you follow on the journey and consider how the steps might apply to your company, department, or even simply a current project you are working on.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

The CRISP-DM Methodology

Welcome to the first in a series of posts dedicated to the Analytics Journey.  More specifically, we will demonstrate how we at Ruths.ai incorporate the industry-proven methodology, CRISP-DM, into our data science life cycle.  Over the ensuing posts, we will take the reader along each step of the journey’s path from beginning to end . . . and beginning to end . . . the Analytics Journey never truly ends, only optimizes . . .

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

Data Science Design Pattern: Train & Predict

Spotfire is a great tool that lets you run asynchronous R code right next to your data and visualizations. This makes for what I like to call the Data Science Trifecta. There’s lots of applications out there that provide the Data Science Trifecta – data, visualizations, and computation – and I prefer Spotfire’s relational data model, snappy visualizations, and embedded R engine. So let’s talk about reusing predictive models in this Trifecta. If you’re eager to try it out, you can grab the template off of Exchange.ai.

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You’ll conquer the present suspiciously fast if you smell of the future….and stink of the past.