DevOps for Data Science – Defining DevOps

I’m wading into treacherous waters here. Computing terms often defy explanation, especially newer ones. While “DevOps” or Developer Operations has been around for a while, it’s still not as mature a term as, say, “Relational Database Management System (RDBMS)”. That term is well known, understood, and accepted. (It wasn’t when it came out). Whatever definition…

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DevOps for Data Science – Who needs it?

Data Scientists have often worked in a bit of a “silo” – meaning they were off to the side in an organization, maybe not even part of the Information Technology (IT) function. But that is changing. As data science projects are adopted into the mainstream, there is a need for structure. I’ve explained a modern…

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The Keys to Effective Data Science Projects – Part 10: Project Close-Out with the TDSP

Data Science projects have a lot in common with other IT projects in general, and with Business Intelligence in particular. There are differences, however, and I’ve covered those for you here in this series on The Keys to Effective Data Science Projects. One of those areas where general projects and Data Science projects are similar…

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The Keys to Effective Data Science Projects – Part 9: Testing and Validation

We’re continuing our discussion of the series of the Keys to Effective Data Science Projects,  this time focusing on Testing and Validating the Model. We’re in the general phase in the Team Data Science Process called “Customer Acceptance“. “Testing” in the general sense is the same in Data Science projects and any other typical software project – it’s ensuring…

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The Keys to Effective Data Science Projects – Part 8: Operationalize

We’re in part eight on our journey through the series of the Keys to Effective Data Science Projects –“Operationalization” – a term only a marketer could love. It really just means “people using your solution”. And it’s this part of the process that is quite possibly the most complicated, and usually the one done with the…

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The Keys to Effective Data Science Projects – Part 7: Create and Train the Model

We’re in part seven on our series of the Keys to Effective Data Science Projects.  This is the section that most people think of when they think of “Data Science”. It’s where we take the question, the source data which has been turned into the proper Features (and potentially Labels), and select an algorithm or two…

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The Keys to Effective Data Science Projects – Part 6: Feature Selection

We’re in part six on our series of the Keys to Effective Data Science Projects. I won’t cover basic Feature Engineering in this article – it’s a huge topic and central to working in Machine Learning areas. I do recommend you check out as many articles as you can find on the subject, and once you’ve grasped…

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The Keys to Effective Data Science Projects – Part 5: Update the Data

In this series on the “Keys to Effective Data Science Projects”, we’ve seen a process we can use, we’ve determined what we want to know, and we’ve ingested the data. In the last step we explored the data, and in a different way than we might be used to when working with in a database…

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The Keys to Effective Data Science Projects – Part 4: Explore the Data

We’re in a series on the “Keys to Effective Data Science Projects”. We’ve identified the question we want to solve, and made a preliminary pass at the data we need to answer that question. Next we brought in that data to a central location we can work with. We now want to explore that data. This is a primary…

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