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5 Steps To Choosing Great Data Visualizations for Your Data Science Projects

Tips and resources to help you get started

Photo by Clay Banks on Unsplash
Photo by Clay Banks on Unsplash

From health care results and business sales analytics to business apps and sports league rankings, visual representations of Data have become a tradition in our day-to-day life.

After collating, interpreting, and analyzing, your visualization models must be professional and unique. Plus, it’s easier for the human brain to understand visuals and patterns than huge chunks of text and figures.

Visualizing is easily my favorite part of the job. Data visualization conveys long and bulky raw data with the use of charts, infographics, graphs, scatter plots, and many more. Not all your audience will be technical or data-oriented so, presenting your data in simple visuals will improve communication and analytical efficiency to a large extent.

Modern Data Science technologies have made visualization more accessible and easier than ever. However, you need to have proper knowledge of certain concepts, models, and data visualization as a whole.

With the huge selections of visualization options, it is super easy to get carried away from the major purpose of visualization – converting figures into beautiful short stories.

The process might be tedious and confusing, it happens to everyone. This article will simplify the whole process and help you choose perfect data visualizations for your projects.


1. Understand the purpose of your project.

Generally, for any project – technical or not – there is always an intention to bring solutions to a particular problem.

What challenges do you want to solve? Are your concepts generic or unique? How will the information you’re presenting offer value to your organization? Are your objectives long-term or short-term?

Before I begin any project I make sure I provide clear answers to these questions, type them down and make references when I feel I’m moving off track.

Some months ago, I attended a Data convention, basically a gathering of young data scientists willing to share knowledge on the latest trends and techniques in the industry. A speaker made a presentation on the effects of cloud data management in a world of mass cyber-attacks.

A topic of necessity, he nailed it, backed up with stats and figures but unfortunately the charts and infographics he used did not correspond with the data represented.

The cause? Incorrect interpretation of the project.

More data isn’t always better – what you need is the right data for the right question.

Data visualization is transforming data into meaningful stories that everyone can effectively understand. In or Out of Technology. Getting prior insight into your data will improve your speed and accuracy when working with data visualizations.

2. Know your audience.

From my observations, the data presented by the speaker at the convention was not as excellent as the participants expected. Apart from failing to note certain objectives, the visualizations looked like an 8th-grade chart on population statistics.

Don’t get me wrong, charts are great, but if you are going to be presenting for a group of skilled data scientists you need to make your data look as professional as possible.

The visualization technique you choose should correspond and communicate with your prospective audience. They should be able to relate perfectly to your data. What is their proficiency? Are they tech-oriented enough to understand your visualizations?

People view information differently, study your audience and reflect on their response when working on your project.

If you’re analyzing transactions with the intent of presenting your results to finance moguls on Wall Street, you’d like to make your visualization technique more professional than if you were to present to first-year finance students.

3. Prepare and understand the specifications of the data you are working with.

Data is often presented in various forms. Although, the major types of data are ordinal, qualitative, categorical, and nominal. You must understand the uniqueness of the data set in your project. The majority of data visualization projects are solely backed by the quality, standard, and variety of the data sets.

Understanding your data type will ease and eliminate some visualization types.

For example, with categorical data, using a line chart will not be a good idea. The same goes for geographical data, maps and column graphs are widely preferred due to their direct storytelling efficiency.

This step begins with crucial data collection, hypothesis, and data checks, to discover insights for key information. There are varieties of tools we can use to understand the data sets – depending on the size and models.

This step is very delicate so keep these procedures in mind:

  • Define the objectives of the data.
  • Limit and convert values into units.
  • Look for errors and identify essential variables.
  • Make use of programming languages for advanced analysis.

4. Select a suitable chart type.

The key concept of data visualization is to convey a message in the simplest form possible to aid easy and effective understanding. Charts are there to interpret and break down variables, choosing the right one will determine the professionalism while presenting your data.

With the vast number of charts and visualization options, it takes precision to utilize the full potentials of one.

To get a better understanding let us go over the most common charts, their use cases when to use them, and their disadvantages:

a. Bar chart

Everyone drew a bar chart in middle school. Although simple it is the most used mode of data visualization. Often used to differentiate two or more variables over time.

However, the major downside to the bar chart is overloading. If you’re working with multiple data points, a bar chart might not be appropriate.

Avoid filling your graph with numerous variables – your graph should not exceed 10 bars. Check out this example from Datapine.

b. Pie chart

Alright so, the pie chart has garnered tons of bad reputations over the years, research conducted by experts proves that pie charts are wrong and often provide misleading analysis.

Although, there are certain projects where pie charts could come in handy in data visualization especially when dealing with small data sets and a specific variable.

c. Area chart

Most data scientists usually think an area chart is the same as a line chart. The truth is, there are some differences between the two. Although they both illustrate continuous data sets in a time series – they are similar but not the same.

The area chart is most effective when used to show part-to-whole relations. For example, a sales rep contribution to the company’s monthly revenue.

d. Scatter plot

Personally while working on large data sets, the scatter plot is what comes to my mind, especially when my variables are paired – a dependent (y-axis) and an independent (x-axis) variable.

To maximize the full potential of the scatter plot, you need to make sure the variables and values in your data set are correlated.

5. Select a powerful visualization software.

There are tons of data visualization tools out there, your selection heavily depends on your objectives, project type, scale, and analytical requirements.

Modern data science has transformed the way analysts work, 80% of data visualization projects no longer require complex mathematical analysis.

"Ten years ago, we definitely saw people who specialized in business intelligence or data or analytics, and that was their job, and everyone else was expected to send a request to those people and wait for reports back."

  • Ellie Fields, senior vice president of product development at Tableau.

Things have changed since then. Most visualization software is built with a friendly drag-and-drop interface, with a basic understanding of the tool’s concept you’re good to go.

Here are some of the top data visualization tools:


Applicable takeaway.

Honestly, I’ve had previous struggles with data visualization. Throughout my learning process, I’ve been self-taught but I realized that to get a quality understanding of data visualization, I needed to seek training from top professionals in the business. Never stop learning.

Before choosing what kind of chart or visual type to use, understand the purpose of your project, know your audience, prepare your data sets, and select a visualization tool with quality features. You’ll need a guide, write down your objectives and make sure you stick to them.

Avoid cluttering your graphs, keep your visualizations basic and understandable. In data visualization, keeping it simple is always best.


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