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Things to live by…no particular order.

I carry a small notepad with me to capture ideas, concepts, or just random points. Here is a list I compiled recently… Spend money to remove a negative (instead of adding a positive) At work, focus on your strengths, at home, focus on your weaknesses Everything will be hard at some point. Focus on the

Raspberry Pi – Let’s get started

Raspberry Pi Starter Kit Common Project Websites Magpi – https://www.raspberrypi.org/magpi/ Adafruit – https://blog.adafruit.com/ Install open-source classic video games with Retropie Retropie – https://retropie.org.uk/ Emu paradise – https://www.emuparadise.me/ Internet of Things Projects and Components 37 Common Sensors for Raspberry Pi 50 Most Import Sensors for IOT devices

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17 Questions to Effective Solution Development (surprise – don’t ask about solution)

You are finally leading your own project, you have been assigned a team, and it’s your first meeting to understand more…Where do you start? What do you say? Here are question to help structure the conversation. General Guideline: Start with people. Start with current understanding of problem and respective process. Use data to confirm current

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Optimize Upstream Tools – 11 tips to improve google search results

Learn how to optimize upstream tools that provide downstream results, especially if they are frequently used. Arguably one of the most utilized tool for the world…Google search. Here are tips/tricks to optimize this everyday tool. Use quotation marks to search for a phrase instead of seperate words. Place minus sign to ignore a word (e.g.

Know your terms – Inference vs. Prediction (with examples)

Recognizing if you are asking an inferencial question or prediction question is critical to choosing a model Oand algorithm. Let’s build awareness of the difference. Inference Association between response and key variables while adjusting for confounders/interactions. Normally there are few variables that highly impact the response (think pareto law). Estimate association of variables to response

Understanding the Machine Learning Algorithms Landscape…

Here is a list of all popular algorithms available for most data scientist. The real value is knowing when and where to applied them. I find that understanding the functionality of each category is more effective, then understand the difference among specific algorithm within each category. This approach speeds comprehension and recall (e.g. bigger blocks)