Ideas from books, articles & podcasts.
Most people approach data the wrong way: They start with a data set, then use their favourite tools and techniques on it. This produces a narrow set of unsurprising results.
When we want to gain knowledge from the data, we should first do some thinking. Before we can answer how we first need to ask why. But this can be surprisingly challenging. The answer is to have a structure to think through all the aspects of a problem.
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We can find structure when we create the scope for a data problem. A scope is the outline of a story of why we are focusing on this problem.
The scope of a project consists of four parts, which is expressed as the mnemonic CoNVO.
The data scientist wants to know how the data and/or insights will be used. How will it be integrated into the organisation? Who will use the data, and why?
An example of outcome: The marketing team needs to be trained in using the model (or so...
We find context when we know who we are working with and why they're doing what they are doing. We learn the context when we talk to them about their long-term goals. The context provides a project with larger goals and helps to keep us on track.
An example of c...
What needs could be fixed by intelligently using data? When we clearly explain a need, we are showing what could be improved by better knowledge.
An example of needs: We want to place our ads in a smart way. What should we be optimising?
Vision is when we take a glimpse of what it will look like to meet the need with data. The vision consist of the following:
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Start extremely simple and work your way up. There's always room to increase the difficulty later.
If you want to build an exercise habit commit to 1 minute per day instead of an hour per day. If you want to build a writing habit start with 3 sentences per day instead ...
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