Why is data visualization so important for data scientists?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data (see our ‘what is data science’ guide for a deeper explanation). However, the challenge data scientists face is that it is not always possible to connect the dots when faced with raw data; this is where data visualization is extremely valuable. Let’s look at why data visualization is such a powerful tool for data scientists.

Discoverability and learning

Data scientists are required to have a good understanding of the data they use. Data visualization makes it possible for data scientists to bring out the most informative aspects of their data, making it quick and easy for them (and others) to grasp what is happening in the data. For example, identifying trends in a dataset represented in a table format is much more complicated than seeing it visually. We will talk more about this in more detail later in the article.

Storytelling

Stories have been used to share information for centuries. The reason stories are so compelling is that they create an emotional connection with the audience. This connection allows the audience to gain a deeper understanding of other people’s experiences, making the information more memorable. Data storytelling is a form of visualization: it helps data scientists to make sense of their data and share it with others in an easily understandable format.

Efficiency

Insights can undoubtedly be derived from databases (in some cases), but this endeavor requires immense focus and is a significant business expense. Using data visualization in such instances is much more efficient. For example, if the goal is to identify all of the companies that suffered a loss in 2024, it would be easier to use red to highlight all the companies with profits less than zero, and green to highlight those greater than zero. The companies that broke even remain neutral. This technique is much more efficient than manually parsing the database to mark the individual companies.

Aesthetics

While raw data may be informative, it is rarely visually pleasing or engaging. Data visualization is a technique used to take informative yet dull data and transform it into beautiful, insightful knowledge. Making data more visually appealing plays into our nature of being primarily optical beings. In other words, we process information much faster when it stimulates our visual senses.

How to Learn Data Visualization

Data visualization is a critical skill for anyone working with data, from business analysts to data scientists. Mastering it allows you to communicate insights effectively, making your data-driven decisions clearer and more impactful. If you’re wondering where to start, we offer a variety of resources designed to guide you at every stage of your data visualization journey—whether you’re a beginner or looking to sharpen your advanced skills.

Here’s how you can get started:

1. Start with the basics

If you’re completely new to data visualization, it’s essential to understand the fundamentals before diving into advanced tools and techniques. DataCamp provides structured learning paths to guide you through these basics.

Recommended Resource: Understanding Data Visualization. This course introduces you to the core concepts of data visualization, teaching you how to design and interpret simple but effective visualizations. It covers essential chart types, visualization best practices, and how to think critically about data.
Topics Covered:
- Basic chart types (bar charts, pie charts, line graphs)
- Common mistakes in data visualization
- How to choose the right type of chart for your data

2. Learn to visualize data with code-free tools

For those who aren’t yet familiar with programming, we have courses on tools that allow you to create visualizations without writing code. These tools are great for business users, managers, and anyone looking to quickly create insightful visualizations.

Recommended resource: Power BI Fundamentals. Power BI is a powerful, code-free tool for creating interactive dashboards and reports. This skill track will teach you how to use its intuitive interface to create compelling visualizations that communicate data insights effectively.
Recommended resource: Tableau Fundamentals. Tableau is another popular tool for creating interactive, visually appealing dashboards. With drag-and-drop functionality, you can easily explore data and create insightful visualizations.
Topics covered:
- Building interactive dashboards
- Data cleaning and preparation for visualization
- Best practices for visual storytelling

3. Develop coding skills for advanced visualizations

If you’re ready to get hands-on with coding, learning to create visualizations using Python or R can greatly expand your ability to customize and automate your data visualizations.

Recommended resource: Data Visualization with Python. Python offers a variety of powerful libraries (like Matplotlib, Seaborn, and Plotly) for creating everything from simple line graphs to complex interactive visualizations. This track will help you build a strong foundation in Python-based visualization tools.
Recommended resource: Data Visualization with R. R is widely used for statistical computing and data visualization. The ggplot2 package, in particular, is an industry standard for creating beautiful, informative plots. In this track, you’ll learn how to create high-quality visualizations and understand the power of R’s data manipulation capabilities.
Topics covered:
- Creating custom visualizations using Matplotlib, Seaborn, and ggplot2
- Understanding advanced charts like histograms, heatmaps, and scatter plots
- Building interactive visualizations with Plotly and Shiny

4. Practice through projects

Once you’ve learned the basics, practice is key to mastering data visualization. DataCamp’s hands-on projects let you apply your skills to real-world datasets, helping you gain practical experience.

Recommended resource: Data Visualization Projects. These guided projects let you work with real datasets, allowing you to practice what you’ve learned. You’ll create visualizations from scratch, gaining experience with both the process of data exploration and the design of effective visual communication.
Example projects:
- Exploring the Evolution of LEGO – Learn how to create data visualizations that explore trends in LEGO set production using Python’s Matplotlib and Seaborn.
- Understanding the Gender Gap in College Degrees – Create line charts and area plots using Python to visualize trends in gender disparity across different college majors.

5. Continue learning and stay updated

Data visualization is an evolving field, with new tools and techniques emerging regularly. DataCamp regularly updates its courses and introduces new ones to keep you informed of the latest trends.

Recommended resource: Data Visualization Cheat Sheet. Keep this cheat sheet handy as a reference for commonly used charts, design best practices, and tips on choosing the right visualization based on your data and audience.
Explore new tools: Stay ahead by learning about cutting-edge tools like Plotly Dash or D3.js (a JavaScript library) as you advance in your journey.

Data Visualization Tools

Data professionals, such as data scientists and data analysts, would typically leverage data visualization tools as this helps them to work more efficiently and communicate their findings more effectively.

The tools can be broken down into two categories: 1) code free and 2) code based. Let’s take a look at some popular tools in each category.

Code-free tools

Not everyone in your organization is going to be tech-savvy. However, lacking the ability to program should not deter you from deriving insights from data. You can lack programming ability but still be data literate – someone who can read, write, communicate, and reason with data to make better data-driven decisions.

Thus, code-free tools serve as an accessible solution for people who may not have programming knowledge (although people with programming skills may still opt to use them. More formally: code-free tools are graphical user interfaces that come with the capabilities of running native scripts to process and augment the data.

Some example code-free tools include:

Power BI

Power BI is a highly-popular Microsoft solution for data visualization and business intelligence. It is among the world’s most popular business intelligence tools used for reporting, self-service analytics, and predictive analytics. This platform service makes it easy for you to quickly clean, analyze, and begin finding insights into your organization’s data.

If you are interested in learning Power BI, consider starting with Datacamp’s Power BI Fundamentals skill track.

Tableau

Tableau is also one of the world’s most popular business intelligence tools. Its simple drag-and-drop functionality makes it easily accessible for anyone to begin finding insights into their organization’s data using interactive data visualizations.

DataCamp’s Tableau Fundamentals skill track is a great way to get started with Tableau.

ChatGPT

ChatGPT has quickly become a tool for helping people visualize their data. As we explore in our article on GPT-4o, with a few prompts, you can use the tool to visualize trends in your data. Generative AI is changing the way we think about our data, and there is much more to come! Check out our ChatGPT Fundamentals skill track to learn more about how to use this powerful tool.

Visualization with code

If you are more tech-savvy, you may prefer to use a programming language to visualize your data. The increase in data production has boosted the popularity of Python and R due to their various packages that support data processing.

Let’s take a look at some of these packages.

Python packages

Python is a high-level, interpreted, general-purpose programming language. It offers several great graphing packages for data visualization, such as:

The Data Visualization with Python skills track is a great sequence of courses to supercharge your data science skills using Python’s most popular and robust data visualization libraries.

R packages

R is a programming language for statistical computing and graphics. It is a great tool for data analysis, as you can create almost any type of graph using its various packages. Popular R data visualization packages include:

ggplot2
Lattice
highcharter
Leaflet
RColorBrewer
Plotly

Check out the Data Visualization with R and Interactive Data Visualization in R skill tracks to level up your visualization skills with the R programming language.