Data visualization is fascinating. It sits at the intersection of many very different worlds. This blog will discuss some facts about the journey and the reality of the job of a data visualization designer.
In data visualization, there are two main parts. First, there is the data part of it: the raw numbers, the statistics, and the math. Second, there is a technical part, especially in the context of creating react apps and the question of how we approach building these graphics particularly since they are often interactive or animated. Those two aspects are probably the ones developers are most familiar with, and for a lot of them, that is the place where they stop. Import the data, pass that data into a chart and you have got the major part of the data visualization process sorted out.
In today’s data-centric world, data visualization is a highly sought-after skill. Some consider the design or “organizing information” as a bizarre magic formula conjured up by magicians. This is not quite the case, given that there are many tech-based tools that assist data visualization designers available today. The principles guiding what makes good data visualizations are not intuitive. They are simple, logical, and simple to understand. Just as these principles are important for a data visualization designer, knowledge of these principles is useful for clients in need of UX and UI designers.
Data visualization is taking raw data, within any niche or subject, and organizing it into digestible pieces. These easy-to-digest fragments follow a logical sequence, making the data easy for anyone to understand visually. This generally, but not always, involves text, charts, maps, or graphs and images. This type of design is also vital in the process of organizing complex data into accessible presentations for companies. It permits a business to obtain insights quickly and efficiently and to aid in making informed decisions. Data visualizations help identify gaps, patterns, and unforeseen opportunities and threats.
The idea that information is only as useful as what we take from it is key to data visualization. This type of design allows us to gain almost immediate insights from data. This adds value to a huge amount of data that may otherwise have been unused. Transforming raw data into effortlessly understood visualizations allows you to make decisions based on reliable, trustworthy information. Good data visualizations should be accessible to the intended audience. Moreover, they should be visually pleasing, clear, and elegant.
The Choice of Learning Projects
The projects you choose when you are learning are crucial to being successful as a data visualization designer. The first consideration is what you want to learn from a project. You will learn the most by choosing projects that challenge you in an area in which you are lacking. Think back and consider what skills you already have that can help you, and what areas you need to explore further.
Furthermore, it is key to consider the scale of projects. It is recommendable to choose a mix of smaller, more manageable projects, the type of thing you could finish in a few hours, and larger projects that might take weeks or months. Smaller projects are useful for learning new tools or practicing specific skills, and there are great resources that provide weekly data and a community to tackle these projects. Larger projects should be more personalized and in-depth, as these are the projects you will highlight most in your portfolio.
When discussing larger projects, it is important to choose something unique. There are loads of pre-packaged tutorials and data that you can use, but the current data visualization environment is too crowded for these projects to stand out. A personalized project will help you to make a name for yourself and will ultimately allow you to be much more engaged in the project.
One great way to ensure this personalization is by collecting your own data. By doing this, you are going to learn the data inside and out, understanding the shortcomings and uncertainties in it. In a similar vein to collecting personalized data, you should choose something that matters to you, something you are deeply curious about. The lifecycle of every major project starts out with huge excitement but quickly descends into frustration.
These projects should be personal and engaging to you, but they do not need to be about some earth-shattering insight or a really serious topic. Some of the best projects just come from a deep dive into the favorite TV show of someone, a movie, or a weird passion. If you are weirdly obsessed with knitting, then a poster visualizing knitting patterns would be fascinating.
In order to become a top data visualization designer, you have to know your information. The skill of joining, filtering, and reshaping your data to fit a particular shape is often called data wrangling. There are numerous techniques in data wrangling but all of it comes down to visualizing the path from your current data format to the desired output. The abyss between raw data and filtered data is daunting, but by breaking the task down into elementary steps, you can tackle one piece at a time and the whole thing becomes more manageable.
Here is a strategy that is accessible and efficient. Get out a pen and paper. Then, draw your current data format. After that, draw your desired data format. Once you have done all this, try to work out the elementary steps to get from what you have to what you want
If you need the information to become a better data visualization designer than you are at the moment, you have to know that the things mentioned below are some of the most important ones in order to do so. Nevertheless, there are many skills that you are going to need to improve to get to the elite. Implement these ones and keep working, failing, and learning. It is only that way that you will get to where you want to be.