The two subpages linked (above) provide more detail into the two tools we used to create data Visualizations using the same resource data. Looker Studio Visualizations were generated from data housed in BigQuery. Tableau Visualizations were largely generated from loaded .csv file of source data and shape-files containing matching Geography Identifying Information (Geo Ids).
While knowing coding languages such as SQL can make it much easier to analyze datasets, it is possible to do analysis and visualization without expertise in this language.
However, you will need to understand data cleaning, data collection, and the functions of the specific tool you are using for your analysis.
As an alternative, we would suggest becoming better acquainted with data cleaning in Google Sheets or Microsoft Excel, as well as a visualization tool like QGIS or Tableau if you are interested in making geospatial visualizations with your data.
Visualization Types
There are a ton of different sort of impactful pieces of visualization you can embed into your analysis. Below is a list of visualization types you can make use of regardless of the visualization tool you are using.
Resources such as can be a helpful starting point in deciding the best options and tools for data visualization.
Here is a summary of some helpful visualization types:
Bar Chart/Graph
Characteristics:
Very common data visualization Can quickly compare data across categories, highlight differences, show trends, reveal outliers, and reveal historical trends Effectively visualizes ‘at a glance’ Text Table (Crosstab)
Characteristics:
Can show data comparatively (rows/columns) Can supplement a visualization Tables can also be represented by images, icons, or trend information (yes/no) Histogram
Characteristics:
Shows data distribution across distinct groups (bins) Visualization is proportional to the records within each category Color bars can further differentiate data Bullet Chart
Characteristics:
Displays data relative to a goal This is a variation of a bar chart Simplified point of comparison visualization Not best for exploring historical data trends Pie Chart
Characteristics:
Add detail to other visualizations Can be overlaid to maps to break down trends Not usually the focal point of a dashboard Similar charts include: Donut, Half Donut, Multi-Layer Pie
Scatter plots
Characteristics:
Show the relationship between different variables Can present multiple distinct data points in one chart Cluster analysis and trend lines can enrich data Clustering groups data points into distinct segments based on selected variables
Visualization Alternatives to Tableau
: This is a free and accessible tool hosted by Google. We included a tutorial and sample visualization in this guide. : Periscope is a visualization tool often used by progressive organizations. You can connect it to your data warehouse, write SQL, and then created visualizations in the tool itself. It isn’t free, but pricing can be more reasonable than Tableau. is a free, open-source desktop Geographic Information System (GIS) software application that supports the viewing, editing, printing, and analysis of geospatial data. This can be a helpful resource because it is open-source and therefore free. If you are interested in using QGIS, it is advisable to buy an introduction to QGIS book and also sign up for a course on how to use the tool. Note: QGIS is routinely updating and is freeware. We encourage you to download the most stable version. It is usually considered a leaner version in functionality to ArcGIS, a cloud-based mapping and analysis tool. Originally, ArcGIS was released as a command line based GIS system. You can learn more about the differences in ArcGIS and QGIS .
Data Visualization
