DA101 is DAC’s annual flagship event; it is a series created by students for students which aims to introduce several of the most widely used languages and software in the field of Data Science and Analytics.
To kick off this year’s DA101 series, DAC EXCOs Jia Hao and Lawrence conducted Tableau101. Tableau is an easy-to-use and easy-to-learn visualization tool that does not require any prior knowledge of coding!
For any aspiring data analyst or data scientist, mastering a data visualisation tool such as Tableau is essential. The objective of Tableau101 was two-fold. Firstly, Tableau101 seeks to provide a beginner-friendly platform for SIM students to gain hands-on experience of Tableau software. Secondly, while utilising Tableau, we hoped to impart basic data visualisation techniques and theories which can be applied to other data visualisation softwares.
Part 1: Introduction to Tableau by Lawrence
We began the workshop by introducing Tableau, key features like multiple integrations and visual customizations, and more on the Superstore dataset.
We introduced various visual cues and their descriptions. Moving on, we created a worksheet of a geographical map and the sales and profit by category. We then merged the 2 worksheets into a dashboard. These are the fundamentals of visualisation using Tableau and we wanted our participants to gather insights from the dataset before moving on to create a story.
Part 2: Creating a story by Jia Hao
For the second segment, we conducted a Region Analysis. While guiding the participants to construct a Region Analysis dashboard, we covered three important visualisation features of Tableau. The three visualisations were Area Chart, Bar Chart and a Treemap respectively.
The first visualisation was the Area Chart. An area chart is useful to represent volume changes over a period of time. We plotted Profit against Ship Date to visualise profit volume over a period of four years. In this particular visualisation, we introduced the feature of a stacked area chart to extract aforementioned information of each region, namely Central, East, West and South.
Next, we constructed a Histogram by plotting Profit against Ship Date. A histogram is effective in visualising distribution of data over a continuous interval. Each bar in the histogram represents the profit of the respective year. Similar to the area chart, we introduced an additional layer by adding in Region column to the histogram.
Finally, we led the participants to construct a Treemap. A treemap is useful when dealing with a significantly large dataset., With the treemap, we aimed to visualise sales of each product category in the four regions. Represented by coloured nested rectangular boxes, users are able to clearly identify the product category generating the most sales.
Upon the completion of the individual charts, we proceeded to combine all three visualisations to form a dashboard which concluded our second segment.
We appreciate the support and overwhelming feedback from all our participants. We were thrilled to have garnered so much interest amongst fellow data enthusiasts in Tableau, and we hope you had a great time, as did we!