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.
Yesterday, I spent some time looking at new keyboard options online. Most of those searches happened via the Best Buy application. My last few keyboards have been ergonomic keyboards built by Microsoft. A replacement keyboard in the same model that I have right now is probably the best option in terms of price. The Natural Ergonomic Keyboard 4000 from Microsoft has been my go to keyboard for years. They now have a couple of different price points and options. One of the options is well over one hundred dollars. Normally, I would be able to head out to a store and take a look at the keyboards. During these strange times in quarantine that is not happening. I’m going to watch a few videos and see what people think about the Microsoft Surface Ergonomic Keyboard. It might very well be my next keyboard. This keyboard I’m using right now might just keep on working and no replacement will be ordered.
Right now my time is being split into two categories of activity. The first category of activity happens to be occurring during my morning window of writing some prose. You normally get to see that in the form of a weblog post. The things that happen during the course of waking up are covered and my ideas are converted into writing about the nature of things. Most of that ends up circling back on the idea of striving toward a perfect possible future and the efforts we make to move forward. The rest of it is muddling prose distilled from inaction and shared with the world. Nobody really wants to read about somebody else being conflicted due to a touch of writer’s block or worse procrastination. The second category of activity happens to be a little bit of Jupyter notebook development. My new Data-Analysis repository on GitHub is devoted to sharing notebooks with simple working examples of things you can do and modify to do other things. The idea is for me to explore data analysis efforts in Jupyter notebooks in a definable and repeatable method.
I’m really focused on putting together a collection of tools for people who are learning to do some complex things in the data science space with machine learning. Getting to that point means helping people get used to opening up rich data sets and doing something with them using Jupyter notebooks. The benefit of this approach is that somebody of any programming skill level can simply pick up the notebook and read the instructions clicking the executable code boxes from the top to the bottom and run the example. That is powerful in nature because reading and clicking is an easy way to start. You can also tinker with each box in the notebook until it does what you want. For somebody learning how to code this really isolates the problem in the chain of commands and you get pretty decent error messaging. Sometimes that is the key to learning enough to overcome the error.
Today, I called an audible (in honor of the Pro Bowl) and focused on the “Introduction to Git and GitHub” course by Google on Coursera. It is the 3rd course in that “Google IT Automation with Python Professional Certificate” series that was released this year. Today, it seemed like a really good idea to focus in on version control systems. This cup of coffee is starting to work. During all these courses I’m using Microsoft Visual Studio Code as my starting point to try to execute code locally. It does not have a cost and seems to be gaining popularity with people who are doing interesting things. My goal for the day is to try and finish this course.
Today I’m digging into threat detection and log analysis. This could get very interesting very fast. Don’t worry, you should be able to see the results on my GitHub probably in a Jupyter notebook format. Right now my efforts are focused on Apache logs since that sounds like a good way to start.
Instead of brewing two shots of espresso with my Nespresso Expert machine this morning I elected to go with a strong pull of Lungo style coffee. It was delightful coffee as usual.
The folks over at Google are still providing the Colaboratory research project for free and that is awesome. That is the first place I go for noodling around with Python these days.
Today I installed Microsoft Visual Studio Community edition on my Corsair Cube desktop for fun. It seemed to be a good idea to help me follow along with the really awesome set of 44 videos the fun folks at the Microsoft Developer YouTube channel posted. Specifically, it seemed like a good idea to have Visual Studio installed and ready to go to follow along with the, “Configuring Visual Studio Code | Python for Beginners [4 of 44],” video.
Well. I got Microsoft Visual Studio 2019 community edition installed. It seemed like a good opportunity to just install Python again. After all of that was done, I realized that they wanted me to install Visual Studio Code (which is a different download altogether). That was easy enough. I went with the insider edition of Visual Studio Code for fun. Installing the Python extension was wickedly easy within VS Code. It was the first link in the marketplace for extensions and was just one click away from being ready to go back and follow along with the Microsoft Developer YouTube channel videos.
Ok, I’m on video 5 of 44 and we finally started with the print statement.
print(‘hello world single quotes’)
print(“hello world double quotes”)
Why do I always find that strangely rewarding?
Update #1: I made it to the, “Demo: Formatting Strings | Python for Beginners [12 of 44],” video before it was time to take a little break and walk around the house.