Data visualization is the act of representing information using a visual like a diagram, chart, or picture. Visualizing data allows non-technical audience members to understand data findings that may otherwise not be accessible when looking at a set of numbers. An important factor in deciding to learn a new skill is researching complementary and related skills. Doing so will help you make more informed decisions about what you want to learn, and it will help you decide whether or not acquiring a specific skill is the right decision for you. You will also find out what kind of skills you may want to study to complement your training later on. Read on to learn more about related data visualization training options. 

What is Data Visualization?

Data visualization uses graphics like plots, charts, or animations to represent data findings. These visual depictions communicate complicated data relationships in an accessible and engaging format to an organization’s internal and external stakeholders, even for those who don’t come from a technical background.

Data visualization has applications across industries and professions, from data analytics to business intelligence. The data visualization process is typically broken into four goals: to generate ideas, to illustrate concepts, to provide avenues into visual discovery, and to convey data in the form of visualizations.

Read more about what data visualization is and why you should learn it.

What Can You Do with Data Visualization Skills?

Data visualization provides a concise and interactive way to depict data trends. Visualizations incorporate design techniques like color and contrast so audience members can access information that may otherwise be difficult to comprehend. Data visualization can provide a better understanding of a business’s operations and how activities connect. It offers insights into important business metrics that can lead to smoother daily operations and successful project execution. Data visualization conveys specific insights into customers’ preferences, demographics, and shopping patterns. The information is presented in an easy-to-understand format, and the audience is taken on a journey that leads to a data-driven conclusion. 

Skills Related to Data Visualization

Visualizing data is a process that draws from a range of other skills. Creating engaging data visualizations requires that you have an understanding of basic visual design concepts, as well as a familiarity with working with databases. Once you’ve learned to visualize data, you may also wish to study topics such as prototyping or machine learning to take your data science and analytics skills even further. 

Basic Visual Design

While it may seem obvious, the more you know about visual design, the better prepared you will be to visualize data. Visual design is a broad field that seeks to improve a product’s usability, aesthetics, and design. Designers work with a range of techniques to do so, such as selecting appropriate imagery and typography, as well as using layout, color, contrast, and spacing. When working with visual design, it’s important to carefully consider where elements will be placed to create interfaces that can lead to higher conversion rates and improve user experience.

Knowledge of basic visual design principles is a must for those who wish to create successful data visualizations. By learning and employing a range of design principles, Data Analysts can ensure that the visualizations they generate convey data findings in a manner that’s clear, understandable, and engaging to different audiences. The following are a few design principles you may wish to learn to improve your data visualization capabilities:

• Hierarchy involves working with different placements and fonts to convey the importance of a specific idea.

• Contrast helps Designers accentuate aspects of the visualization using different colors, texts, and placements.

• Unity is applied to visualizations to enhance the harmony between various elements.

• Balance pertains to evenly distributing various visual elements.

• Dominance helps certain objects stand out from others based on their color or size.

• Scale is helpful for emphasizing some elements in a data visualization so that their importance can be communicated.

A great way to learn more about design is to enroll in Noble Desktop’s Digital Design Certificate. Participants receive training on industry-standard Adobe apps for creating print, web, and digital designs. 

Prototyping

Using visuals to tell a narrative story that includes actionable insights combines tools and skills from science and art. In addition to using business intelligence and data visualization tools like Tableau for visualizing data findings, some professionals prefer to work with a more customized visualization solution. This can involve the process of prototyping. Prototyping is a process where Designers and their teams experiment with idea implementation. They create prototypes to capture design elements, then test them on users. Prototyping is a useful tool for helping Designers refine their ideas and validate designs, ensuring that the product they eventually release will align with the needs and expectations of their target audience.

Often, data visualizations are geared toward multiple stakeholders, each of whom may want different elements to be included. This can cause the project to take longer to complete than expected and may make it complicated to satisfy all end-users. By using prototype development before data is visualized, you can cut down on later-stage design challenges such as these. Because prototypes can be reworked quickly to accommodate various requests from stakeholders, prototyping can cut down on the cost and time required for iterative design and redesign. Prototyping before visualizing data can even accommodate the need to add additional elements during the design process.

If you’re interested in learning design concepts for UX and UI design, Noble Desktop’s UX & UI Design Certificate provides hands-on training. Students receive instruction on how to conduct user testing, create digital products, and make clickable prototypes. 

Database Familiarity

Before you can analyze or visualize data, you must first find it. Databases are organized collections that store large amounts of data pertaining to people, objects, or places. The data are stored in various forms: files, customer data, product information, financial data, and sales transactions. Computer databases are designed to easily access, manage, and update the information stored within them. 

Databases are used for a range of tasks. Businesses rely on the data they contain to make better decisions pertaining to how to streamline business processes, keep track of users or customers, improve user experience, and secure personal data or health-related information. Any data professional wishing to access, analyze, and visualize data starts with a database. It’s important to know how to navigate databases so that necessary data can be retrieved and used during the subsequent analytics process. 

Machine Learning

Once you’ve learned to visualize data, you may wonder what skills to acquire next. For those interested in working in data science, machine learning is a valuable skill to acquire. This branch of AI and computer science uses algorithms to replicate the learning process in humans so it can be completed more accurately. Machine learning draws from statistical methods that are used to train algorithms to make accurate classifications and predictions, which have applications for extracting insights during the data mining process. 

One of the benefits of working with machine learning algorithms is that they provide insights into historical data, as well as how these findings can be applied to new information. This allows Data Analysts and Data Scientists to create more effective models for data visualization. Machine learning also provides a way to sculpt datasets into a more concrete and effective narrative that can contextualize the information being depicted and situate it in a larger context.

Machine learning is a field that continues to grow in popularity as more organizations understand the value of data. This tool helps organizations quickly develop models to analyze vast, complex datasets and provide quick, accurate results. If you want to study machine learning, Noble Desktop offers a Data Science Certificate. This rigorous program teaches participants to use a range of data science tools and techniques, such as Python and SQL.

Learn Data Visualization Skills with Noble Desktop

Noble Desktop has in-person and live online data visualization training. Tableau for Data Visualization Bootcamp instructs students on how to use Tableau Public’s visualization tools. Noble’s Data Analytics Certificate teaches core business intelligence, statistics, data analysis, and data visualization concepts. 

Aspiring Data Scientists can enroll in Noble’s Data Science Certificate to study machine learning, automation, SQL, and Python. The Python for Data Science Bootcamp provides instruction on how to use statistics to make machine learning models. Noble’s Excel Bootcamp teaches fundamental spreadsheet functions, including macros, formulas, and PivotTables. Those interested in working with SQL can enroll in Noble’s SQL Bootcamp.

Key Takeaway

• Data visualization provides a way to represent information visually using charts, diagrams, graphs, or animations. 
• If you’re interested in learning to visualize data, you may wish to study basic visual design concepts, such as contrast, balance, and hierarchy. It can also be helpful to learn more about how to work with databases to retrieve information.
• After you’ve learned to visualize data, you might consider studying related skills, such as machine learning and prototyping.
• Noble Desktop currently offers comprehensive coursework in data analytics and visualization for those interested in learning more about how to work with data. Classes are available in-person in New York City, as well as in the live online format.