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As Data Schoolers, towards the end of our 16-week intensive training, we all take on an ambitious challenge — creating one dashboard, one blog and one presentation on a fresh dataset every day for a week! Today marks the fourth day of this challenge. In this blog, I would like to share my approach to today’s challenge and showcase my finished dashboard.

You can also find all my dashboard week blogs here:

Day 1: Global Power Plants

Day 2: Wages vs. Inflation

Day 3: Star Wars

Day 4: 2021 Australian Census

Day 5: AFL Data




The Data

Today’s dataset is the 2021 Australian Census data from the Australian Bureau of Statistics (ABS). The data and its documentation can be found here.



The Plan

Step 1: Data Understanding

It always pays off to start the analytical process by understanding our data. At this stage, it is often not necessary to develop a comprehensive knowledge of the data, but just enough so that you can start exploring relevant business questions and hypotheses.

For the census data, I have noticed that:

  1. The data is very comprehensive and complex, as it covers more than 40 different tables spanning across 15 topics. Some of the tables contain more than 9 or more sheets. For data as complex and comprehensive as the census data, it is extremely important to start with the metadata, that is we should read and comprehend the relevant documentations before diving into the data. 
  2. The census data contains topics on Cultural Diversity, and Employment and Income. These are areas that I am particularly interested in, therefore I shall focus my dashboard on them.
  3. The data seems to be in wide form, I expect transposing and other transformations will be necessary for cleaning the data.


Step 2: Business Understanding

In this part, we try to understand why we are performing our analytics project. This is also where we begin to pose hypotheses or goals. Businesses, especially retail businesses, often rely on demographics data, such as population and income to make important business decisions, such as where to open a new shop, or what types of merchandise they should sell. My goal for this project is to try to use the census data to help businesses make such decisions.


Step 3: Planning

The majority of the challenge will come from data collection and data cleaning. Getting the data into the right shape and form is very important. Without good, clean data, it will be very hard to create insightful and interactive dashboards. Since there will be multiple decision criteria, I also expect to make use to dynamic sets and parameters to build my dashboard.


Step 4: Data Cleaning and Pre-processing

I’ve used Alteryx to clean the dataset. More specifically, I:

  1. Built a batch macro for easily cleaning and preparing the Country of Birth data, which contained 9 sheets.
  2. Cleaned and prepared income data.

I also enriched the data by using Australian digital boundary Shapefiles which can be found here.



The Dashboard

Below is a screenshot of my finished dashboard. The dashboard is made up of three main sections:

  1. The Control Panel: where the user can select different combinations of criteria based on their target customers or business requirements.
  2. The Scatter Plot: where the user can see which locations satisfy the set criteria.
  3. The Map: where if the user clicks on a location point in The Scatter Plot, they will be able to see where this location actually is on a map.

Of course, Tableau dashboards are meant to be interactive and should allow the user to explore their own questions and answers, so please follow this link to go to my Tableau Public and have fun with my dashboard there!




Martin Ding
Author: Martin Ding

Martin earned his Honours degree in Economics at the University of Melbourne in 2011. He has more than 7 years of experience in product development, both as an entrepreneur and as a project manager in robotics at an AI unicorn. Martin is expecting to receive his Master’s degree in Data Science from CU Boulder at the end of 2022. Martin is excited about data and it’s power to transform organizations. He witnessed at first hand of how instrumental data driven decision making (DDDM) was in leading to more team buy-in and insightful decisions. Martin joined the Data School to systematically enhance his knowledge of the tools, methodologies and know-how of Data Analytics and DDDM. When not working, Martin enjoys readings, cooking, traveling and golf. He also thoroughly interested in the practice of mindfulness and meditation.