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Using VS Code with Anaconda - Virtual Environments

 If you create a Python environment in Anaconda using the commands previously outlined here , you can use that in Visual Studio Code. While not very intuitive, it is pretty easy to do. In VS Code: Press Shift + CTRL + P to activate the search window at the top of the screen. Search   Select Interpreter from the list that appears. Then in the Select Interpreter list, choose the environment you set up previously. All set! If you would like to use Jupyter Notebooks in VS Code, install  the Jupyter extension from Microsoft and be sure to include this command in your Conda environment setup: conda install ipykernel 
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Anaconda Environments

Create a Conda environment by opening the Anaconda prompt.  Determine the versions of Python available to decide which one you want: conda search python [ENTER] From the list of Python versions, select one. Enter the following command to create your environment: conda create --name <your environment name> python==<python version>[ENTER] You will be prompted to install several packages. Type 'y' to proceed. Packages will be downloaded and installed in your new environment. Once it completes you will be returned to the base environment. Switch to the new environment by entering this command: conda activate <your environment name> To exit the new environment and return to the base environment, enter: conda deactivate Finally, to see the environments installed on your system: conda env list The environment with an asterisk next to it is the currently active one. And to delete an enviroment: conda env remove --name <env name> And the official Conda cheat shee...
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Data Goblins - Myths, Magic, and Copilot for Power BI

Interesting article on the use of generative AI in Microsoft's Power BI over at Data-Goblins.com.  https://data-goblins.com/power-bi/copilot-in-power-bi I'll admit to using it in PBI and the results have been mixed.
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Artificial Intelligence - Hype or Hope?

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  In my work life earlier this year, we did a pilot program using Microsoft's OpenAI implementation to create a chatbot for internal usage. The rollout went well, although the adoption was not what we had hoped for. Was it how we presented it to users, or was it we were so caught up in the tech "wow" factor that we missed something? Maybe we were trying to solve a problem that didn't exist for anyone else? Since then, it seems like AI continues to get more and more press. Is it the next big thing, or overblown hype? Every year or so, some new tech is launched and the IT world stands agog at how THIS will be a game changer. Remember how Docker containers were going to be how all DevOps were created and it would replace the existing server farms that a lot of companies used? Or how Big Data with Hadoop would revolutionize how analytics were developed?  As a follow up - I experimented with Microsoft's Copilot technology across the three tiers they offer, CoPilot Pro...
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Spreadsheets Aren't the Solution to Everything

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If I ask you what is the most widely used Business Intelligence / Analytics tools in the world, would you know the answer is Microsoft Excel? Excel and spreadsheets in general have been the go-to tool for business analysts since the days of Lotus 1-2-3.  Unfortunately, the growth of data has increased exponentially over the last decade along with how fast new data is being generated, and the variety of data that is available. Relying strictly on spreadsheets to sort through that data and garner usable insights may limit what can be gleaned. Don’t misunderstand me, spreadsheets are still an essential part of data analysis, but they are not the only business intelligence tool an analyst should be using. To unlock deeper insights, enhance efficiency, and keep up with the rapidly evolving data landscape, analysts should explore a broader range of business intelligence tools and techniques. If your data set is a small to medium size one, with a fairly well defined structure, a spreads...
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Data Science Factiod of the Week: ETL in a Visual

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 For a lot of analytics projects, we have to extract data, transform it, and load it into another system. I created this visual about a decade ago to visually explain what each step means.
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Connections in Music Data

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I am a huge music fan, listening to a pretty wide range of genres. While I work from home I will often have YouTube playing in the background. Sometimes something will jog my memory and I will remember a song I haven't heard in awhile, and with the power of the Internet I will research the group or the artist and see what connections I can find.  I think this fascination comes from an early Rainbow CD I had, that in the linear notes, was a foldout chart showing all the people who played in Rainbow, Deep Purple, and other bands from that time. The particular visual I believe was created by Pete Frame, an early rock journalist who founded ZigZag magazine and created many Rock Family Trees. The particular visual is included in one of his Rock Family Tree books and are copyrighted.  Fortunately, user BostboweL posted a spreadsheet on Reddit under the r/blacksabbath forum, showing a lot of the same information. Its a lot cleaner than the original one I remember, but it gets th...
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