Month: June 2017

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.

Merging In House and Public Data in Spotfire

  • Have you ever wanted/needed to merge data sets where the merge would create unwanted duplicates?
  • Have you ever attempted to merge public and private data and struggled with getting the desired output?
  • Have you ever wanted to know how to identify duplicate records in Spotfire?

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Guest Spotfire blogger residing in Whitefish, MT.  Working for SM Energy’s Advanced Analytics and Emerging Technology team!

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.