Final post on mapping out my data science learning journey, including an overall tracking chart with all the checkpoints.
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Checkpoint Echo: Advanced machine learning toolbox
There’s an old Chinese saying: “工欲善其事, 必先利其器”, which loosely translates to “In order to a good job, you must first sharpen your tools”. Wise words.
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Checkpoint Delta: Basic machine learning toolbox
I’ll incrementally build a toolbox of machine learning algorithms, starting with basic ones like linear regression and decision trees and then working up to more advanced techniques.
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Checkpoint Charlie: Visualisation
You could say that The Matrix digital rain is the ultimate form of visualisation, as it represents in 2D everything that happens in an entire virtual world inhabited by a race of human batteries. Too bad it isn’t real, or is it?
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Checkpoint Bravo: Basic computing tools
While strengthening my theoretical foundation, I’ll start familiarising myself with the basic tools to run data science projects. The good news here is that most of these are open source, freely available on the internet and well used by the global data science community.
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Checkpoint Alfa: Theoretical foundation
Mathematics and statistics are the core foundations of data science theory, and I’ll start with a few key topics to build a strong theoretical base for my learning journey.
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Getting from here to there
Progress is made one step at a time, and the trick is to keep moving. This is my first stab at mapping out a data science learning journey. Ikimasho!
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Begin with the end in mind
I’ve always been a firm believer of knowing where you want to end up before starting on any meaningful journey. One of the things I’d like to achieve is to be a practising data scientist. Here are some of the key skills I’ll need.
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