Last week in class we learned about how data can be visualised in ways to make data more readable. Large clumps of data are far too complex to understand initially, this is why we use charts and graphs to utilize and understand these data sets. Descriptive and inferential statistics are used to show relationships of different variables.
Big Data makes data handling and visualisation simple and it is the most accurate it has ever been. Since Big Data has surfaced, new ways of visualising data has emerged to handle the larger sampler sizes. Traditionally, data would be interpreted using small scale graphs using pen and paper, however the advancement of computers allows us to interpret data in ways never seen before, revolutionising the process entirely! How cool is that?
Here are a few examples:
Basic Line Chart
Bar graph with negative stack
Overall, these data sets would not have been made possible before due to the limited sample size and inefficient data collection methods. These visualisations help display data in a manner that is readable and consistent in a relevant format, allowing new predictions and theories to be created. The larger sample sizes also means information is more accurate and reliable. I am excited to see what new methods of visualisation will arise in the near future.
References:
https://www.highcharts.com
https://ilearn.fife.ac.uk/course/view.php?id=9751#section-15
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