Data visualization

“Good visualization can considerably improve the interpretability of data and the efficiency of communication. With the latest developments in graphic techniques, we are now able to visualize and dashboard many kinds of seemingly messy (high-dimensional) data in 2D/3D plots, networks, etc. More practically, many visualizations can be presented in an interactive way, in which graphs and data points can be manipulated and customized by users, giving great ease to check data, identify patterns, and present findings.
Importance: Data visualization and dashboarding are the graphical display of abstract information for two purposes: sense-making (e.g., data analysis) and communication [1-3]. In other words, data visualization is the representation of data or information in a graph, chart, or other visual formats. It communicates the relationship of the data with images. This is important because it allows trends and patterns to be more easily seen [1]. With the rise of (more available) data upon us, we need to be able to interpret increasingly larger and more heterogeneous batches of data. Multiple tools make it easier to visualize and present data. Data visualization is not only important for data scientists and data analysts, but also for many roles in different fields, e.g., marketing, communication, tech, design, etc. Besides, interactive visualization (hereafter referred to as visualization) enables the exploration of data via the manipulation of data embedded in the graph, with the color, brightness, size, shape, and position of visual objects representing aspects of the dataset being analyzed. The use of interactive visualizations is becoming increasingly popular in scientific research and business intelligence, and is now a common part of most analytics suites (e.g., Plotly, Tableau, PowerBI), thanks to its ease of use and added value, which allows a more effective exploration of the data.
Method: In this report, we first discuss why good data visualization is important for KWR and Dutch water utilities. Then we summarize different types of graphs suitable for data science projects. Next, we list potential tools and software to make visualization and provide a list of top 5 of recommended tools. Last but not least, we also provide a user guide for quickly visualizing data based on Python.”

(Citation: Tian, X., Vries, D., Seshan, S., et. al. – Data visualization – BTO 2022.037 – (2022) – See for Interactive presentation: https://kwrwater.notion.site/kwrwater/Report-Data-visualization-3cb5aecca3cb464d8da1e47580b1532e)