I started at Microsoft in 2006 - in the SQL Server team. I've been a DBA/Developer/BI dev since 1981, on systems from mainframes to HP VAX Clusters, Solaris systems, and more. I worked as a Program Manager (someone who owns a feature within a product at MSFT) for the management tools - SQL Server Management … Continue reading Buck Woody is Returning to SQL Server
(A "Syllabuck" is like a Syllabus, but more like that second definition, and certainly more random) I recently attended, presented, worked and did interviews at the Microsoft Ignite 2018 Conference in Orlando. If you have never been, you should go sometime. 30,000 people, 2.1 million square feet of space, and ten+ miles of walking per … Continue reading Syllabuck: Ignite 2018 Conference
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 … Continue reading The Keys to Effective Data Science Projects – Part 10: Project Close-Out with the TDSP
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 - … Continue reading The Keys to Effective Data Science Projects – Part 9: Testing and Validation
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 … Continue reading The Keys to Effective Data Science Projects – Part 8: Operationalize
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 … Continue reading The Keys to Effective Data Science Projects – Part 7: Create and Train the Model
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 … Continue reading The Keys to Effective Data Science Projects – Part 6: Feature Selection
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 … Continue reading The Keys to Effective Data Science Projects – Part 5: Update 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. … Continue reading The Keys to Effective Data Science Projects – Part 4: Explore the Data
Artificial Intelligence is a vastly complicated topic. It's not just that it has a lot of components (because it does) it's because it isn't really a single topic. It's a surprisingly old field of study that has morphed and changed over the years. I worked with "AI Systems" in the 1970's - and even then … Continue reading Simplifying (?) Artificial Intelligence
Caffeinated Data Science 