The Data Analysis Maturity Model – Level Two: Reliable Data Storage and Query Systems

I’m in a series defining a Data Analysis Maturity Model. In the first level, I described the importance of creating, validating, testing and tracking your base data collection methods. With the source data clearly defined, tracked, documented and verified, the next level of data analysis maturity is to store and process the data correctly. Data…

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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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Introduction to the Microsoft AI Platform

I recently recorded an introduction to the Microsoft Artificial Intelligence suite of tools and services you can use in your organization, from what is already built  into Microsoft applications you own, through leveraging Cognitive Services, customizing AI, all the way through writing your own AI with Machine Learning, Deep Learning, and Neural Networks. I also…

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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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The Microsoft Artificial Intelligence Landscape – And What to use When

Artificial Intelligence (AI), at its broadest definition, is simply “a machine that can act using human-style reasoning or perception”. Of course, the technologies used to enable that definition are far from simple themselves. Artificial Intelligence isn’t new – I worked with “Good Old Fashioned AI” (that’s a real thing) back in the late 70’s and…

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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 – Application Performance Monitoring

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 – Release Management

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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