Skip to content Skip to footer

How to learn data visualization to accelerate your career

Do you love clicking around interactive maps? Frustrated you can’t get your spreadsheet charts to match your vision? If you’re drawn to data and visual design, learning the fundamentals of data visualization could be a fruitful way to bring value to an employer—and grow your career.

By becoming handy with data viz, as it’s often called, you’ll have a valuable new tool to apply to fields such as design, engineering, journalism, or data analysis. Someday, you might even be building and designing charts and other visual representations as a full-time job as a data visualization designer or engineer.

In this guide, we’ll walk you through the important concepts in data visualization, the tools of the trade, and how to learn them. Spoiler alert: CodeSignal Learn offers data visualization lessons for free. You can also find courses on the fundamentals of Python, which you’ll need to use to navigate the most common data viz libraries.

Hold onto your X-axis as we chart your way to data viz success!

Jump to a section:

Introduction to data visualization

Famed data visualization thinker Edward Tufte said, “The commonality between science and art is in trying to see profoundly—to develop strategies of seeing and showing.”

Data visualization does more than convert numbers into lines and shapes. Thoughtfully crafted charts can change minds, influence budgets, and inspire movements. Less dramatically, they’re a more efficient, easier-to-digest way to look at data than tables.

While statisticians and others have been visually representing data in print for over two centuries, the practice has shifted to computers and the internet. Today, there are three main ways to visualize data:

  • Spreadsheet apps like Excel and Google Sheets make serviceable charts, and with some practice and a few tricks, you can customize them to some extent.
  • Business intelligence and data analysis platforms, such as Tableau or Microsoft’s Power BI, offer many ways to manipulate and visualize large amounts of data from different sources without learning code.
  • Code frameworks, including Matplotlib and Plotly for Python or d3.js for JavaScript, are the most flexible and powerful.

What is data visualization?

Data visualization transforms information into images. It’s usually based on numbers, whether tracking single data points (say, the daily high temperature in a certain place) or counts (for instance, the number of babies in Canada given the name Michael every year). 

Scottish engineer William Playfair introduced the world to line, bar, and pie charts nearly 250 years ago. Other types of charts came about in the 19th and 20th centuries, and in the 21st, interactive computing and an explosion in data have combined to make all sorts of visualization possible.

In this guide, we will focus on modern tools and techniques that use code or specialized platforms to create highly customizable and often interactive visualizations.

Why is data visualization important?

A picture may be worth a thousand words, but a chart can tell the story of millions of data points. Charts, maps, and even well-formatted tables can turn numbers into stories. Persuasive presenters use them to back up bold assertions. Curious investigators uncover patterns or anomalies in the lines, shapes, and shades. 

The science and art of data visualization have coevolved with the internet and Big Data. Enormous datasets and interactive charts open up so much of the world to exploration and understanding.

In dynamic situations, from monitoring company-wide sales to a region’s incidence of disease, decision-makers often rely on dashboards with many charts to stay on top of dozens or even hundreds of variables at once. In more static situations, such as looking at combinations of historical data to inform strategic choices, charts can be even more customized, with annotations and other design choices specific to the story to be drawn out of the data.

All indications are that data viz will continue to be an important part of any role that requires understanding or communicating around data.

Is data visualization easy to learn?

Whether you’ll find data viz easy to learn depends on your current skills, your aptitude for code, and how far you want to go.

You should be able to grasp the basics of no-code business intelligence (BI) software, such as Tableau or Power BI, within 10 to 15 hours of concerted study. Getting certified with BI software could take weeks to a few months.

To use Python data visualization libraries, you’ll need to learn the fundamentals of the Python programming language if you haven’t already. That can take a few months, but don’t stress—it’s become such a common language in part because it’s easier to learn than most others, and CodeSignal Learn can help get you there. Once you know Python, learning how to use the individual libraries is a matter of practice: you can follow our learning paths quickly, but mastery takes time.

If you’re not already up to speed with core statistics concepts, expect the process to go a bit slower. You’ll need to either familiarize yourself at the outset or look up terms as you go along.

What is the difference between data analysis and data visualization?

Data analysis and data visualization are very close friends. In fact, most data analysts end up doing some data visualization regardless of their particular training.

Data analysis covers all the tools and techniques for making sense of data. Much of data analysis involves manipulating data: sums, averages, comparisons, regressions, and the like. The results end up as one or multiple data points, which the analyst may display as a chart—and that’s where visualization comes in. The overlap between the two is in the choices about how to display data, such as the type of chart, what data to include and exclude, and how to scale the axes.

We enter the realm of pure data viz with aesthetics and interaction design. This includes choices made to improve understanding, such as colors and labels, and ways users can engage with charts, such as hovering, clicking to expand, and searching across particularly large data sets.

Essential skills for breaking into data visualization

As we’ve mentioned, data viz is a balance of art and science. We’ll start by discussing design considerations such as which type of chart to choose. Then we’ll look at the technical options that are commonly used in data visualization today.

Understanding the different chart types and how to choose between them

What type of data are you displaying and what story do you want it to tell? Are you looking at how a single factor changes over time? Are you comparing different data series? Is the data geographical? Does it follow a multi-step process? This analysis is the first part of your chart selection strategy.

Let’s examine some of the most common types of charts and when to use them.

Line charts

Line charts show points in a data series connected by a line. The Y-axis—the vertical one—represents the value of the data, while the X-axis—the horizontal one—specifies each point in the series. Most commonly, the X-axis is time, but it can be some other unit, such as price or size. A line chart can contain multiple lines, allowing the comparison of one or more data series.

Line chart example
Playfair, the inventor of line charts, demonstrates the trade balance between England and the kingdom of Denmark & Norway.

Bar charts

Bar charts are used to compare the values of categorical variables, representing clear demarcations such as blood type or political party affiliation. (They can also be used for ranges like income bands or years.) A bar chart can compare one or multiple data series; usually, color is used to represent different types of data.

Bar chart example
Average temperature per year in Asia, 1901–2020. Author: Ed Hawkins, University of Reading, under Creative Commons Attribution 4.0 International license

Pie charts

Pie charts show the components of a whole, whether on a percentage or absolute basis. A donut chart is simply a pie chart with a hole in the middle; which one to use is purely an aesthetic decision.

Pie chart example
Incidences of types of lung cancer. The inner ring distinguishes between smokers (light) and non-smokers (dark). Author: David.haverthuer, under CC 4.0 license.


Histograms look similar to bar charts, but they show the distribution of values within a given data series. A large data set is broken into chunks—say, household income in a country, broken down by $5,000 increments. Histograms show more nuance than basic statistical features like average and median.

Histogram example
Deaths by horsekick in the Prussian cavalry corps, 1875–94. This histogram distributes deaths per year by two-death clusters, so here were four years in which either five or six Prussian cavalrymen died from a horsekick. We’re not morbid; this is from a famous dataset.

Scatter plots

Scatter plots let you show every single piece of data in a series at once. You’ll often see these when comparing a sizable but manageable number of data points, such as statistics across countries of the world or the sports teams in a league. Often, they’ll have a best-fit line, which is a mathematical function that represents the overall trend of the data.

Scatter plot example
National wealth and CO2 emissions by country. Author: RCraig09, under CC 4.0 license.

Bubble charts

Most bubble charts are scatter plots with variously sized circles adding another dimension of comparison. For instance, when comparing countries’ life expectancy, the circle size could represent the amount spent on healthcare per person. Color can denote a category, such as continent. There are also linear bubble charts, which are an alternative to line charts, and bubble clouds, which have no axes but simply represent labeled data points in proportionally sized circles.

Bubble chart example
Female versus male life expectancy per country—explore the interactive version of this chart.


Heatmaps represent data values using color. These are particularly useful for showing data within a spatial context, such as on a map. They can also be used on a grid to highlight patterns that a simple line graph may not highlight as clearly, such as phenomena more likely to happen on a certain day of the week. In other cases, they simply make it easier to see the range of values in a large number of datapoints at a glance.

Heatmap example
Debt-to-GDP ratio over time among several European countries. Notice how debt increased after the financial crisis of 2008.


Treemaps are like pie charts, but square. They’re better when there are many parts to the whole, for a few reasons: squares are easier to compare than wedges, you have more space for labeling, and you can more easily cluster data points to show categories within the whole. Treemaps are often used for complex breakdowns like the components of a country’s economy. 

Treemap example
A treemap of Senegal’s exports. Colors to represent categories of exports, such as minerals and fish.

Choropleth maps

Choropleth maps use one or more gradients of color to represent the value associated with a geographic region, such as a neighborhood, state, or country. These can be visually stunning, but it can be hard to see the values of smaller geographic units.

Choropleth map example
Craft breweries per capita, per state.

Sankey diagrams

Not all data viz is done in charts that plot data. Sankey diagrams, named after the Irish captain who invented the concept, are a compelling way to show the magnitude and direction of flows between stages, such as data, money, or energy. 

Sankey diagram example
Estimated U.S. energy consumption in 2016. Almost all the gray rejected energy is waste heat.

Gaining a good grasp of the data visualization process

Successful data viz is more than knowing how to put points on a chart. You need to understand the data, the audience, the presentation format, and the capabilities and limits of the technology in your toolkit. You also need patience and persistence, as you rarely get the chart you want on the first try. Let’s walk through some visualization best practices.

Understanding the goal of the visualization

The first step is to understand what you’re trying to achieve. Consider the following:

  • Argument. What are you saying with this chart? Is the takeaway clear? If you don’t have an opinion, what range of conclusions could you expect a viewer to take away? 
  • Audience. Who’s going to see this chart? If multiple types of audiences, who’s most important? What do they already know? What’s important to them? What types of design, words, and information density will engage or repel their interest?
  • Context. Where will this chart be seen? Is there text or other content nearby? Will it be near other charts? Can we expect our audience to linger on the chart, or will it have to make a clear point quickly?
  • Medium. Will this be interactive or static? Printed or on a digital slide? How might it meet accessibility standards? Will it load quickly enough to maintain interest?
  • Effort. Is this worth a lot of time to make excellent, or is quick and dirty good enough? Could you repurpose templates or other charts? 

Ability to analyze and understand the dataset

Open up the data, take a look at the labels of the columns, and peek into random rows to get a feel for the details.

In many cases, what you start with isn’t enough. You might need to do some pre-processing, such as handling missing data or performing calculations such as averages. Sometimes you’ll need other datasets—for instance, if your raw data uses a two-letter code for each country, you might need a table that lists both the code and the full name. If you do this, be sure to save all your work in a separate file; don’t write over the original table!

If the data is dynamic, meaning it’s updated on an ongoing basis, you’ll need to understand the frequency and nature of the updates. Then, you’ll want to think through how to display the data: for instance, will you show it for all time or a certain span, or will you make it interactive so that the user can choose?

You also might want to create preliminary charts to understand how the data in various columns look. It’s quick and easy to play around this way.

Technical skills to create a preliminary design

Some data viz pros like to first sketch their vision by hand or in a visual design app. This is an especially smart idea if you need other people’s input—you can save a lot of time by agreeing on how it should look before you build it.

Once you get to building the chart, that’s where your technical skills get put to the test. We’ll go over the major libraries and frameworks you might want to learn in the next section.

Visualization design skills for crafting the final product

Once you’ve built out your chart, it’s time to tweak it. Try out different color schemes, labeling approaches, and dynamic features such as what happens when you hover on or click a certain point. 

At this point, you’ve spent a long time with the data and chart. Someone with a fresh set of eyes can suggest improvements and catch errors that you wouldn’t have. If possible, find someone representative of the target audience.

Python tools and frameworks for data analysis and visualization

If you want to present yourself as serious about data viz, your best bet is to become proficient with some Python libraries. Of course, if you don’t know Python yet, you’ll need to get up to speed on that first, but don’t be daunted. More on that later.

A library is a collection of pre-written code, created and tested by others, that you can easily load and use. Most Python libraries, including the ones mentioned here, are open source and free.

There are several data visualization frameworks and libraries for plotting, a term borrowed from physical printing devices that draw lines directly on paper. Unless you have a specific reason to learn another library, we recommend starting with the most common: Matplotlib for static graphs and Bokeh or Plotly for dynamic ones.

Matplotlib, which claims to “make easy things easy and hard things possible,” has long been the standard for static charting with Python. It’s powerful and flexible, offering hundreds of types of charts with myriad ways to customize. It’s also the basis of many other data viz libraries.

Learning tip: Get hands-on practice with Matplotlib and Seaborn in the Intro to Data Visualization with Titanic section of the Journey into Data Science with Python learning path from CodeSignal Learn. 

For exploring datasets, you can use Seaborn, a library built on top of Matplotlib that “lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them.” It provides what it calls “opinionated defaults,” so you can quickly plot nice-looking charts without making design decisions, while allowing you plenty of customization. In fact, you can use Matplotlib parameters to edit a Seaborn chart. (Check out the lesson on Mastering Scatter Plots with Seaborn in Python from the Intro to Data Analysis path in CodeSignal Learn.)

If you want to create interactive graphs in Python, learn Bokeh or Plotly. They can both create stunning and complex graphs that load quickly. You’ll likely want to choose Plotly to make interactive dashboards, quick prototypes, or 3D plots. Bokeh shines for real-time data with live updates and allows for more customization and integration.

Not all tables will be ready for plotting, so you’ll need to know how to manipulate them. Going a step beyond Excel or Google Sheets, Pandas is the standard for cleaning, analyzing, and transforming data in Python. While plotting libraries can do some on-the-fly transformations like finding averages, most of the time, you’ll want to use Pandas to make data ready for graphing. (You guessed it, we’ve got a lesson for that too.)

Tableau and other software tools

In addition to those code-based libraries, there are several no-code tools in a category called business intelligence (BI). That term rightly hints that these were developed for the needs of the enterprise—tracking sales and other business-critical data—but they can be used for fields such as healthcare and public policy.

The leading BI platforms are Tableau, Power BI, and Looker. 

  • Tableau is the most visually focused, with a drag-and-drop interface that allows a lot of customization.
  • Power BI is part of the Microsoft universe, so it’s most often seen at companies that already use Office.
  • Looker is the most analytically powerful, and least graphically-oriented, of the three.

Think twice before training on a particular platform before you need to. Typically, a company or organization that uses business intelligence software will commit to one platform, and they work quite differently so the skills aren’t fully transferable. Unlike Python libraries, they charge for access, so let an employer pay for the software (and the time you spend to learn it!).

That said, if you have your eye on a particular industry and you glean that one or another of these tools is dominant, it may be worth the effort to learn or even get certified. 

D3.js and tools for interactive visualization

JavaScript is a programming language mainly used to make web pages interactive. Just as there are Python data viz libraries, there’s one major one for JavaScript, known as D3.js.

D3.js is extremely powerful but can take time to learn, particularly if you aren’t already proficient in JavaScript. Fortunately, there are libraries built on top of D3.js, such as Plotly.js, that are easier to pick up. (Yep, the same Plotly you can use with Python.) For an example of a tool built on D3.js, check out SankeyMATIC—and play with making your own Sankey diagrams.

While D3.js is today’s standard, it’s not the only one. Chart.js, Vega, and Vega-Lite are alternatives that are worth considering if you’re a JavaScript person.

How can I practice my data visualization skills effectively?

Once you’ve become familiar with the tools, it’s time to put them to use.

Ideally, you can find a way to apply these skills in your job or schoolwork. Otherwise, come up with a project on a topic that interests you and see how you can put data to work.

Cost is no issue: the Python libraries you need are free, and there is an abundance of freely available data on just about every topic available online. You’ll also find active and friendly data viz communities around the internet with people who are happy to critique and provide guidance—just make sure to be respectful of people’s time by first searching for answers that may already be documented.

Where to learn data visualization online

The best way to learn data visualization is to get some guidance on the fundamentals and then learn through doing. All of the libraries we’ve discussed are thoroughly documented and discussed online.

Best online courses for learning the fundamentals of data visualization

Data science, including data viz, lends itself to self-paced online coursework. You can apply the skills you’ve just learned directly in the browser, and if you’re a tinkerer, you can experiment to your heart’s content.

Use CodeSignal Learn to acquire and refine essential Python and data visualization skills

While some courses, such as the ones offered by CodeSignal Learn, are well-templated, so you can get through them without knowing a lick of code, you’ll learn much more—and be better prepared for professional application—by learning the fundamentals of Python first. You don’t need to become an expert, but you should understand enough that you know how to look up what you don’t know.

As you look at online courses, inspect the credentials of the instructor or organization you’ll be learning from, especially if they’re charging a hefty fee. In fact, there is excellent instructional material available for free on just about any data viz topic.

Learning tip: CodeSignal Learn can teach you Python Programming for Beginners and Intro to Data Analysis with Python. It’s free to sign up, and you can get started anytime.

Practice with real-world data and build a portfolio website that showcases your skills

Once you’ve gotten a handle on the basics, the fastest and most effective way to bulk up your data viz skills is to practice.

The best way to stay focused and motivated is to create a portfolio. Start by thinking through what lies at the combination of what interests you and the visualizations you’d like to become good at. Find relevant data sources, and get plotting! Expect to run into challenges; by troubleshooting and seeking answers, you’ll learn through experience.

Other resources to enhance your skills and connections

In parallel to hands-on practice, broaden your perspective and sharpen your technique by studying what others have done. Consider a book by Edward Tufte, who is well known for having strong opinions and a perspective that spans centuries. Another popular data viz thinker, who brings a journalism perspective, is Alberto Cairo.

There are so many communities to join and thinkers to follow. Find inspiration in the r/dataisbeautiful subreddit, expert analysis of great charts from FlowingData, perhaps even join and attend events from the Data Visualization Society. When looking up local groups, broaden your search to include data science, as there is a large overlap of interest. 

Accelerate your career in data visualization with CodeSignal Learn

Data visualization is a worthwhile competence for data-minded visual storytellers (or story-minded data analysts!) to pursue. While there’s a bit of a learning curve, especially if you’re new to Python, the ability to create beautiful and useful data visualizations is always in demand.

Whether you have a clear vision of a long and prosperous career as a data visualization expert, or are wondering if it’s a skill worth learning, the first step is simple and free: take some CodeSignal Learn courses. You’ll be generating charts within minutes!