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Moore’s Law – Dashboard Week Day 2

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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Kicking off Day 2 of Dashboard Week, we were presented with a dataset focusing on GPUs and CPUs. Having some familiarity with computer hardware, I found today’s challenge somewhat less daunting than the previous day’s task. The day began with us presenting our work from Day 1, followed by writing a blog about that dashboard. Although this consumed a significant portion of the morning, I wasn’t overly concerned, given the constraints of the dataset we were working with.

Our assignment was to “Build a dashboard visualising the improvements made over time for various manufacturers and see if you can make a good recommendation for some CPUs I should consider for my next media server build .” Unfortunately, the dataset lacked the necessary information to select an appropriate CPU for a media server build. Consequently, I decided to take some creative liberties and slightly alter the scope of my dashboard.

Rather than focusing solely on hardware recommendations, I chose to explore Moore’s Law. My new objective was to create a dashboard that educates people about Moore’s Law, along with highlighting potential challenges it may face in the future. As this wasn’t an extensive project, I managed to approach the day at a relaxed pace and finished relatively on time. Sorry about the empty area at the bottom ill fix that once i get the time.

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