Role of Data Scientist in Data Visualization

“A picture is worth a thousand words” – and, when it comes to data science, it’s worth a million.

Data science, in all essence, is about taking data, using tools, techniques, and algorithms, and deriving meaningful insights. If you look at it closely, you’ll find out that Data Science works at the junction of the IT and management domains. You need engineering experts to manage and handle the data, alright, but where does the data come from? Who defines the business objectives? It’s the business stakeholders and management teams – the people responsible for collecting this data. There’s a little bit of both required to lay a useful roadmap.

The fact that data engineers and scientists have to convey their findings to the management team that’ll decide the further plan of action makes data visualization an absolute necessity. One of the most impactful ways of getting your point across has always been by visuals/graphics/imageries. Even in the ancient times, data visualization was practiced, with the oldest example being a cartesian plane.

As the demand for data scientists grew, so did the use of much more sophisticated data visualization tools and techniques. Today, companies dealing with vast piles of data are in always in dire need of a upskilled data scientist. So, if you’re a budding data scientist looking for a fulfilling career, it’s recommended you go through some courses and equip yourself with data science certifications. These courses are developed to strengthen your base and arm you with the required knowledge for everything in and around data science including the most advanced visualization techniques. That ways, not only will you be aware of the nitty-gritty concerning this field, but also you’ll be an asset to any organization you work for.

The organizations today have heaps of data beneficial to them only if they’re able to connect the dots. A data scientist is precisely required to help them do that. However, it doesn’t end with just connecting the dots. These data scientists are also responsible for finding patterns in that data and using relevant visualizations tools and techniques to produce the most insightful and visually appealing representation of their findings.

Role of a data scientist in visualization

From making sense of the enormous data sets to making it presentable and understandable on dashboards, all of this comes under the work scope of a data scientists. For this very reason, a data scientist is expected to have a sound knowledge of statistics.

After fetching the data and looking for patterns and trends, it’s now time to display it in a format understandable to someone who is not a techie.

There are innumerable ways of visualizing data, and it’s almost impossible to cover them all. However, below is a list of some of the more common visualizations used by data scientists:

Form Use Case Example
Bar Charts Great for comparison of discrete values Analysing how much revenue a list of companies have generated in a year
Frequency Polygons Observing the trend of a particular variable with time A depiction of how many people visit a restaurant on each day of the week
Scatter-Plots Can be one, two, or three dimensional. Great for observing the relationship between two variables To see how the number of phone sales changes with variation in pricing
Network Maps Analysing the dynamics of large and complex networks Figuring out a cluster of people most likely to buy your product from a network map of Twitter users
Gantt Chart Monitoring the progress of a task or project Most business case or research proposals are incomplete without a Gantt chart showing how the project is expected to evolve
Treemap/Pie Chart Great for depicting percentages Percentage break-up of usage of memory on your phone
Colour Map Illustrating the change in the value of a variable over space Measuring how much heat is produced in various parts of an internal combustion engine during operation

A data scientist picks one or more of the following visualizations depending on the exact business needs, requirements, and goals. It’s essential, therefore, that there is a clear communication channel between the data science team and the management team.

Aids for a data scientist

Over the years, as data has grown in both volume and variety, much more advanced and sophisticated data visualization tools have come into existence. Today, there are many platforms, frameworks, and algorithms solely to provide better visualizations.

Plotly, DataHero, Chart.js, Tableau, FusionCharts,, Jqplot, are just a few of the tools to name. Most of these leading software solutions provide flexible visualization methods and a stock of predefined graphs to choose from. They also allow individuals to make their custom dashboards with self-defined charts and other metrics counts.

Certain ideologies that are often applied by data scientists in visualization are mind maps which explain the approach, storyboards of tools for illustrating the whole process, and of course the graphs. Through these, data is presented coherently.

Going beyond the Cartesian plane

With the current exploratory work going on in the field of 3D imaging, holographic displays, and augmented reality, data scientists are trying to push the concepts of 2D data visualization across the boundaries of three-dimensional reality.

In the light of the present scenario, 3D models of graphs and maps are being projected as holograms in photo studios and data labs, but that is still in a beta stage. A lot of work needs to be done before it can be introduced as a standard practice. Integrating audio with the visual data points together with a dedicated AI is yet again a topic of ongoing research.

From simple cartesian graphs to color maps, the world has seen quite a jump in the way data was visualized. As data science becomes an even more integral part, we can expect business to invest heavily in algorithms and technologies. With that, we can hope to see much more advanced visualization tools in the near-future.

News Reporter