Welcoming Tableau in my Data Science Journey
To be honest, I initially encountered the word, “Tableau” when I was looking for a job through a job search engine. So, my impression was the same with the people who don’t have the idea of it. You heard it right, I really don’t have the idea about this tool. It was my first time to formally get acquainted with Tableau in our Data Visualization course for my graduate study program in Data Science.
Through my data science journey, I am now trying to embrace this tool as one of the powerful data visualization platforms.
To begin with, I’d like to give these following impressions:
1 User Friendly — this is one of the fascinating features of tableau. At first, you have to learn how the home screen works, especially the canvas or view where the table will draw visualization.
By dragging the “INPUT” from the data set to the column/row/canvass, allowed visualization will be suggested by the “POSSIBLE OUTPUT”. Once you’ve selected the applicable visualization graph, “ACTUAL” visualization output will be automatically generated. See, it’s instant!
2Amazing Graphs — generation of graphical interpretation of data is quite impressive. This tool has the capacity to suggest the applicable graphs that can be generated from your inputs. Guides/requirements will suggest the needed info or inputs in order to generate a specific graph. In addition, there are variety of visual images. This will help give an interesting interpretation and analysis to your data. Amazing, right?
3Honing user’s creativity using dashboard and story telling — The generated graphs from the sheets you’ve worked with are readily available for drag and drop in the dashboard. You have the liberty to strategize how your dashboard and story will look like. Play with your graph and it’s more fun!
4Connecting other datasource — Relating one dataset to another is one of the best features of tableau. Just click “add” to add your secondary dataset. “LINK” icon is a representation of how based dataset links into your secondary dataset. Yes, this is hassle-free!
Takeaways:
I’d like to use the following terminologies from the cycle of analytics to provide something I learned in using tableau.
Data Discovery — learning your data is easy in tableau. All you have to do is the power and skills of “drag and drop” without the hassle of typing code. Thus, visualization of your data is readily available for your interpretation and analysis.
Data Preparation — As I learned to analyze the first dataset, I realized I needed to look for different data sources to support the initial analysis I have had. Tableau gives the convenience in linking the main dataset to secondary dataset. Indeed, data preparation is manageable in tableau.
Data Analysis — it has something to do with the user’s capacity in reading, interpreting and analyzing the data. Visualization helps me to understand and analyze my data. I was able to generate insights from the generated visualized data.
Data storytelling — I recalled the dashboards I am seeing online from different fields and now I realized how the users paid details in order to speak the dashboards with the readers. As a result, it gives the user an idea on how to maneuver the story.
Final Words:
In general, any data visualization platform has a common goal, and that is to transform data into meaningful graphs. These graphs will be of great help in interpreting and analyzing the data, locating problems and insights, and triggering the next stage of a particular workflow/process.
