Second day at the Data School and the day started with a session into looking into what modern analytics pipeline looks like. The pipeline can essentially be broken down into the following 6 categories:
Raw Data –> The data that is being collected.
Ingestion –> Connection of data collection and storage.
Central Storage –> Where the data is being stored.
Prepared Data –> How the data is being cleaned, if required.
Trusted Data –> How users can know if the data is trustworthy.
Visualized Data –> Answers and conclusions that can be drawn from the data.
To help cement our understanding of the analytics pipeline, we were tasked with choosing a company or body where we apply the analytics pipeline to it. As someone who dabbles in weightlifting, I decided to use the IWF. I was able to apply most of the analytics pipeline in this case, but had struggled with ingestion and trusted data as I was overall unsure for the applications for these two categories.
Afterwards, we had an overview into the meaning of data visualization. There were a few quotes of the term which can be summed as the creation of visual representation to explore data and be able to discover insights and facilitate understanding from it. The key take is that with data visualization, analysis becomes much easier to perceive or understand as our nature of being visual creatures. A real data dynamic duo so to speak :)
That's the overall gist from the second day, and tomorrow we're starting to dive into the technical aspects of Tableau Prep. Exciting!