Continuing the coding quest

My continued quest to build new examples of data analysis has been going well enough. Today will be a real test of my efforts to produce things. Very shortly I’m going to go spend the morning walking around nature. Yesterday, I managed to play 18 holes of golf and it worse me out. After returning from the golf course I didn’t really want to sit down and write or work on any coding projects. I mostly just wanted to drink some water and rest. During that period of restful contemplation I did give some thought to reworking my presentation and corresponding talk on, “Effective ML ROI use cases at scale.” The previous versions of that title had been “Building effective ROI ML use cases” and “ML use cases at scale with effective ROI.” Based on the three titles I’m sure you can see how one was superior. My preference is for what I feel is the crisper title. Normally, when I’m doing research and writing a paper I give the title a few searches using the Google search engine to see if something shows up. During the course of a literature review I end up searching other databases as well. That is one of those things that just has to be done.

Later today I’m going to try to do better than yesterday and pick back up my coding efforts and write a little bit more prose. My strategy yesterday which was effective was to start work on both the writing and coding early in the morning before my expedition to enjoy nature. My golf game is not particularly good or anything. These two days of vacation and golfing are mostly about spending some time outdoors and enjoying the beautiful Colorado weather in June. The high today is going to be about 83 degrees and the morning weather looks to be perfect for walking around. During the course of walking around and seeing the mountains in the background some of my thoughts will drift back to the best way to teach and demonstrate to others how to move along the path to finishing a solid coding quest. Part of that is slowly bringing people into the world of modifying data and doing machine learning within Jupyter notebooks. Working in Microsoft Excel is something that most people have done to work with data. You can see it right in front of you and it is easy to manipulate. Eventually people working with larger and larger datasets graduate into using Microsoft Access as a database management tool. Eventually that type of effort graduates into using a Microsoft SQL server or maybe one of the open source database alternatives. 

Interrupted. School and golf. 

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