Data Science
The aim is to tell a story with your analysis.
After switching careers from a regular computer scientist to a data analyst, I realized that developing profitable skills in the data science industry was part of the switching process. Don’t get me wrong, a solid background in computer science
The data analyst collects and analyzes the data, but it doesn’t all end there. The information collected still needs to be presented to end-users who require that data to learn from or make valuable decisions. That’s where data visualization comes in handy.
To increase professionalism in data visualization, there are essential tips you need to get yourself familiar with. These tips aren’t just a one-time cheat code to success, to maximize your effectiveness you need to practice and make these tips part of your visualization process.
Without further opening, here are three tips that will aid you to take your data visualization career to the next level.
1. Focus more on your audience than the job.
Put your audience first.
There is always the drive to throw efforts into the content of your analysis, absolutely normal to want to make the project as neat and effective as possible. But a major mistake most data professionals make is neglecting the audience.
As James Stewart rightly put it,
“Never treat your audience as customers, always as partners.”
It is necessary to make every visualization project meet the information needs of your audience. Assuming our analysis was going to be utilized by us the data scientists, we can basically visualize the components of the data using scientific methods.
I can even decide to leave the data sets in my notebook — I’m still going to get the best out of the information I need.
But we have an audience, which is probably a mixture of technical and non-technical people. Put yourself in the shoes of the audience.
Getting the perfect graphics is just as important as the data you’re presenting.
In data visualization, most graphics aren’t one size fits all — not all graphics work for everything — sometimes you need to think, look and go out of your comfort zone and select the right graphics that simplifies the essence of your data.
There are certain concepts binding the idea of graphical representation, all these work simultaneously to get the best out of data visualization. Let’s point out a few:
A. Charts:
Are charts dated?
From a personal perspective, charts are the most effective basic method of data visualization. Most times, going a bit ‘traditional’ might just be what you need to maximize your data science potential.
After data sorting and analysis, the project was concluded. But we were going to be presenting to people with little/no tech background, a group of business moguls who basically spent most of their lives studying the roots of economics and finance.
Since we already know our audience, the key was to narrow our data visuals to something more subtle. Again, more benefits to understanding your audience.
B. Patterns for layout:
As important as each concept is, pattern choices remain a firm category in data visualization.
Simply put, a good pattern will set the right tone, while an inappropriate pattern might misguide the narrative of how your audience will receive the data.
The human mind is constantly looking out for objects, people, colors, and even habitual actions we have come across — either recently or in the past. We are naturally visual observant. Our eyes and brain are quick to process certain pointers that illustrate or display the information we need.
As data scientists, how can we take advantage of this?
As much as you would love to diversify while selecting patterns, it’s best if you narrow your selections to predictable and common patterns.
Plain and effective is better than complex and confusing.
C. Colors:
Colors are great. Apart from being just great, they can be your ticket to a successful data visualization project. In data visualization, colors are the best alternative to words or numbers.
Applying the perfect blend of colors in your data visualization design will give the viewer a quick glimpse of the data and its objectives even before you start your presenting.
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