I was a fresh graduate, longing to hop on a career as data scientist. Given the circumstance where I didn’t have any expertise and experiences in this field, I’ve tried to build them from scratch.
Tableau was the first sophisticated data analysis tool/software I taught myself in 2016 . I couldn’t remember the exact reason why I got interested in Tableau. One thing I knew for sure is the impression on how powerful and intuitive Tableau is to process and build interactive data viz to the world. I guess its novelty is what it has done to me.
Even though I’ve finished courses that taught me to operate the basic functions and analysis in Tableau, I didn’t have any thorough plan to advance my skills. The time limit embedded on Tableau’s free-trial setting has set even greater boundary to go further.
Frustrated with the scarce resources, I decided to pause my learning progress.
Two years later, I came across Udemy, a marketplace that provided online courses that cater to skillsets in digital industry at a much affordable price. The Tableau course that I took has helped me acquire better muscle memories in understanding Tableau. Yet, the learning obstacles still remained the same.
It wasn’t until the present times, did I realize that my learning method was the factor that hindered my progress the most.
- I went straight to follow along the tutorial.
- I handled the datasets or data projects just the way they were instructed.
- When finishing the courses or tutorial, I didn’t pick them up again in the place where I left them.
There is nothing wrong about following the tutorial the way we are told to, really. It’s a good way to get ourselves familiarized with the interface. But after getting on several courses, I find following the tutorials blindly makes it harder to trace back of what I’ve already tried. I didn’t make myself to get used to set a structured thinking first to outline the process I needed to take to handle the datasets lied before my eyes.
To improve the quality of my learning adaptability progress, I try to handle the data projects like building a compelling story.
- I set a theme to be the foundation in composing the story line. When looking at the datasets, I try to imagine the persona of the users that will need to use gain information from this dataset. Based on that, I start to choose on the grand theme and type of story genres that will guide me to make a better use in analyzing the dataset.
- I write a list of questions in each story stages. After determining the theme and structuring the story line, I can define the problems statements and questions much clearer in order to enlighten the users. These questions will give me ideas on what type of techniques I should perform to get the answers I need.
- I learn the big pictures first in brief, then go learn into the detail as I progress. I learn that scanning all the materials firsthand will give me better direction to navigate the trouble-shootings and not overcomplicate things. It’s important to map the general features and learn the basics so we can improvise by utilizing our creativity along the way. That way, our learning progress will imprint deeper in our brain.
I run my first Tableau data vis projects based on these new principles. I picked Airbnb NYC’s listings dataset that was uploaded on Tableau’s resource page. The data summary on dimensions and measurements revealed sufficient materials for a beginner like me to get a hands-on data analysis and viz.
An idea came up to pick a persona for a girl, planning for her first trip to NYC. She wanted to find the best Airbnb accommodation, in terms of property/room type and price. She also concerned about the reviews, so providing her an information on the average of rating scores on each property would come handy for her.
Based on this scenario, I’ve finally made my first interactive Tableau data viz. Mind you, that I barely scratched on the surface and it still needed a lot of improvements. One of them would be adding the descriptive narratives so the users can get quick bites of info before delving deeper into the analysis.
The first steps in learning curve are always clumsy and messy. But I find it stimulating and encouraging.