The Data Analysis Maturity Model – Level Three: Distributed, consistent reporting systems

I’m covering a series of data analysis maturity levels, which are essential to performing Advanced Analytics. We’re often quick to adopt a new way of evaluating data, while sometimes ignoring the fact that analysis is built on trustworthy data. Following a series of steps in the organization starting with proper collection through a good storage…

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The Data Analysis Maturity Model – Level One: Data Collection Hygiene

Data Science and Advanced Analytics are umbrella terms that usually deal with predictive or prescriptive analytics. They often involve Reporting, Business Intelligence, Data Mining, Machine Learning, Deep Learning, and Artificial Intelligence techniques. Most of the time these technologies rely heavily on linear algebra and statistics for their predictions and pattern analysis. In any foundational mathematics,…

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Azure Machine Learning and the Team Data Science Process – Part 1

The Team Data Science Process allows you to have a repeatable, controlled progression for analytics projects. You can use it with any Data Science technologies, and Microsoft has a full suite of products you can use for AI programming. Microsoft Azure Machine Learning Services have several components that assist in large-scale AI programming, Deep Learning,…

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Data Science and Standard Patterns

In Data Science, the word “Pattern” has a specific meaning, involving the patterns that arise from data. This type of analysis is quite common in Data Mining and other technologies used by a Data Scientist. In IT practices such as systems architecture and software solutions design, the word “Pattern” has another definition, which is to…

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Analytics is Width. Feature Selection is Depth.

Most organizations don’t focus on Data Science or AI or Machine Learning as a single discipline – they group it together with the entire Analytics function. This includes everything from spreadsheets to Relation data, from documents stored in multiple locations to the structured business data in standard operations. While you might view your team independently,…

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DevOps for Data Science – Load Testing and Auto-Scale

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. You can find each Maturity Model article in the series here: Infrastructure as Code (IaC)…

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DevOps for Data Science – Continuous Delivery

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects is to implement Infrastructure as Code…

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DevOps for Data Science – Automated Testing

I have a series of posts on DevOps for Data Science where I am covering a set of concepts for a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. In this article, I’ll cover the next maturity…

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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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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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