One of the most popular tools for deciphering and displaying data is Tableau. A handy reference book covering all the basics will make working with Tableau faster, easier, and more effective.
We at Using less have created a tableau desktop cheat sheet to enhance your use of Tableau.
A cheat sheet has been created to give you a basic understanding of Tableau and its applications. This cheat sheet will teach you all the fundamentals of a Tableau desktop which you need to be aware of to begin using Tableau.
Although there are numerous benefits to using Tableau, these are the top three:
You can also learn more from Great Learning about Tableau. Several Tableau Courses are available.
Tableau is available in two primary versions:
Workbooks are what you will use while using Tableau. There are sheets, dashboards, and tales in workbooks, and a workbook has a similar number of sheets as Microsoft Excel. Any of the following could be on a sheet in a workbook located on the bottom left.
You may need to combine data from several sources or tables for data analysis activities. Additionally, Tableau offers the following data joining features:
Data mixing is another feature that Tableau offers. You may utilize this functionality if you discover corresponding data in many data sources and wish to study all relevant data in a single view.
Tableau also includes a selection of operators. Operators often manipulate logical or mathematical data and these operators aid in the creation of computed fields and formulas in Tableau.
Tableau introduces LOD Expressions. LOD provides data sources with even greater control over their data.
Tableau offers two different methods for you to execute sorting on your data:
An excellent tool for tracking patterns over time is a line chart. Your greatest option if you have data that progressively change over time is a line chart (like sales numbers). You may compare two data sets across time using line charts as well. Since the lines often join spots that fall on the same day, changes throughout time may be easily seen.
Unlike line charts, which only display one variable at a time, bar charts display sales by product and brand category. Additionally, it is useful for comparing many datasets at once, like bar charts. Consider that you have a list of things divided into several categories. If so, instead of utilizing distinct visuals by each category or variable, you may compare them side by side solely on a single visual element by using a bar chart.
When comparing two separate variables, a scatter chart is a fantastic tool. It’s also a wise option if you want to identify patterns or trends in your data and have many points or if the data is rather sparse.
Scatter plots are diagrams that display the relationship between two variables in a body of data. It depicts data points on a Cartesian system or a two-dimensional plane. The dependent variable is plotted on the Y-axis, while the X-axis represents the independent variable. Time or another variable is represented on the x-axis, and other parameters like revenue or profit are depicted on the y-axis. Typically, the values on both axes are expressed using numbers or sentences rather than lines or forms (such as bars).
You may determine if there is any association between your variables using scatter helpful plots. They also let you look for any trends in how these characteristics change over time (this will be more apparent when multiple points are plotted on your scatter plot).
A combined axis chart is a useful tool for simultaneously contrasting these two variables. The values of both variables could be displayed on a single graph with different axis scales. Additionally, you can compare different data sets using this graphic (such as regions).
When using a combined axis scatter chart, you may see three variables simultaneously, which merges two axes into one axis. It is helpful when you compare many variables over time or across several categories, such as sales over time by department or sales over time by category in various regions.
When displaying hierarchical data, like the organizational structure of a corporation or the structure of its product lines, a tree chart is a useful option. Suppose your data collection comprises numerous levels in each category, like the various organizational levels or the various tiers of items produced by a company. In that case, tree charts may also be helpful.
For example, you can show how much more money someone earns with a college degree compared to others who don’t use stacked bar charts. It shows how two things interact. Additionally, stacked bar charts may show percentages and proportions over time.
With Tableau’s business intelligence software, you can efficiently report insights using configurable visuals and simple dashboards. Creating your first visualization, a dashboard, a data story, and more are all covered in this cheat sheet, which is intended to get you started with Tableau. Look into these free online courses to start your Tableau learning journey.