Essential data visualization skills for a career in data analysis By: Kiana Seitz July 18, 2024 Estimated reading time: 8 minutes. Are you looking for a job that combines your technical skills with your passion for graphic design? A career in data analytics with a specialty in data visualization might be perfect for you. Data visualization requires a mix of technical computer knowledge, analytical skills, and storytelling. Someone in this specialized role translates raw data into a clear story that stakeholders can understand. This story helps organizations make more informed business decisions. Learn more about the essential skills needed for a successful career in data visualization, and discover how to use these skills to propel your career in data science. Technical skills for data visualization Your technical skills are important to getting hired as a data visualization expert. Here are our recommended hard technical skills needed for a career as a data visualization specialist: Data visualization tools and design skills Software can help you take your raw data and make it visually appealing, summative, and predictive. Here are some software and knowledge you’ll find helpful as a data visualization expert: Tableau This software is built around the science behind helping people make data analysis faster, easier and more intuitive. Data visualization experts use it to create dashboards, helping stakeholders understand what their data reveals about the organization's past, current, and predictive future performance. Power BI Data visualization experts can use this software to create reports quickly, summarize data from multiple sources, and write and explain DAX queries. It’s best for those already within the Microsoft ecosystem. D3 Data visualization experts can use their coding skills and open-source code from D3 to create interactive and animated representations of their organizations' data. Design skills It’s also important to understand how to present complex data visually to make it appealing and easy for your stakeholders to understand. This means having basic design skills to make your data analysis visual. Good design skills to have include: Knowledge of typography and font selection Understanding how to apply brand rules and guidelines to designed content Image and photo editing skills Infographic design experience Experience creating different chart types (bar, pie, radar chart, range chart, heatmap, table chart…etc.) and other ways to represent complex data and trends visually Get recommendations for the top data visualization tools. Data handling Data handling is a term to describe the collection, storage, use, and communication of data. It includes ethical and compliant data collection, as well as data cleaning/preparation and data processing: Data cleaning and preparation Data cleaning or preparation removes any errors, inaccuracies, and inconsistencies from your data. This prepares the raw data and converts it into a format that can more easily be analyzed (manually, using a software program, or AI). Data issues that require cleaning may include: Incomplete form submits (such as ones that leave out required fields) Inaccurate data records (such as spam submissions of your contact form or reviews from people who haven’t used your product or service). Duplicate records Human input error Data compliance Corrupt data (that’s incomplete or unreadable) Data processing Data processing “sends” the data to the analytical database or processing program for analysis. This data will then be used to generate the visualizations you need to communicate results, insights, and recommendations. Statistics and data analysis Understanding statistical and data analysis will help you excel in this career path. It helps you better understand how data was collected to help you use the right data to solve business challenges. With a robust grasp of statistical analysis, you can identify trends in your data and effectively communicate them to your stakeholders. It also equips you with the ability to predict future performance or results based on past data, providing your organization with a strategic advantage in decision-making. For example, you can use past sales data and marketing spending to predict future sales based on any marketing budget. Learn more about the skills you’ll need in data visualization careers. Programming skills Your coding skills will come in handy to help with data presentation. While WYSIWYG (what-you-see-is-what-you-get) style data visualization tools are available, those with programming skills can use the data and display visualizations to meet the organization's branding and unique needs. Here are a few common languages and frameworks used by data visualization experts: Python Python uses several code libraries (like Matplotlib, Seaborn, Bokeh, and Plotly) to help data analysts build graphics and illustrate statistics. These libraries help you tell more compelling, visually appealing stories based on your data. R R’s programming language offers extensive visualization libraries, including 3D models and multipanel charts. R is better for smaller data sets (large data sets can slow rendering time) and is best used with data on a standalone server. Data visualization experts use R to present conclusions and insights from data, look for repeating patterns and trends in data, and more. SQL SQL is a popular programming language used in data visualizations to manipulate, analyze, and visualize data. Data visualization experts use SQL to create dashboards and data grids. Using programming languages with built-in code libraries (like Python, R and SQL) prevents data visualization experts from “reinventing the wheel” by using code snippets that are tried and tested and modifying them to your needs. Curious about SQL? Learn the basics and build a solid foundation with Lighthouse Labs’ free SQL Essentials course. Soft skills for data visualization Successful data visualization experts require strong, transferable soft skills they can learn through related or unrelated career and life experiences. Critical thinking and problem-solving Critical thinking and problem-solving are two foundational soft skills you'll need in any data science career. You’ll need to approach challenges with assumptions or educated guesses, then test your theories using available data to find solutions. As a data visualization expert, you’ll need strong skills to identify the data that will help your stakeholders understand their business challenges. Data visualization careers also require strong problem-solving skills. You may have a stakeholder come to you with questions like: “Why is [something] not working?” “How can we increase sales?” “Why is our marketing campaign not bringing in quality leads?” “What are the ideal conditions that convert prospects to customers?” Example: The company CEO wants to know what contributed to lower-than-expected quarterly earnings and how to increase sales in the next quarter. A data visualization expert will look at the available sales data and trends to present evidence contributing to lower sales. They can then suggest actions to avoid this scenario in the future. Communication skills A data visualization team member collects and visually presents information to effectively communicate data insights to stakeholders (including managers, team members, other departments, investors, and more). Communicating through storytelling is especially important for this career path. You must present the data and insights in a way that’s easy to understand and encourages stakeholders to take action. This requires: Commanding your audience's attention Contextualizing the data points so they’re understandable Communicating simply to avoid confusion Communicating your insights with confidence Top 4 tips for communicating data and insights effectively Communication tip Example Contextualize the results to your audience. - Speaking to the sales team: “This resulted in a 20% increase in closed deals for the regional team.” - Speaking to the finance team: “This resulted in a $15,000 increase in revenue for the regional team.” Communicate in a narrative or story rather than regurgitating numbers and stats. NO: “We lost 20% of our customers last month. That includes 50% from Canada, 25% from the US, and 25% from overseas. That was $20,000 in annual revenue.” YES: “There was a sharp drop in subscription renewals after our pricing was increased last quarter. This showed that our customers are price-conscious. So, when we offered customers one month free with their annual renewal, we saw a 20% increase in renewals that quarter.” Communicate why the data or insights matter. “Based on this A/B test on our Google ads, people enjoyed the video ad more than the stock photo ad. We predict that posting more video ads will increase our conversion rates, adding 10-20% to our monthly revenues.” Use the most effective visualizations. - Historical sales data and trends are often best illustrated through line or bar graphs. - Sales distribution by region is easily interpretable through pie charts. Subscribe to Lighthouse Labs’ newsletter for more tips to further your data science career. Sign up Attention to detail Your organization will depend on your data and insights to inform important business decisions. Your strong attention to detail will ensure you present them with all the information they need to make accurate, data-informed decisions. Here are a few tips to avoid common visualization errors: Misrepresenting data by color Have a good understanding of color psychology. For example, most people assume green to be good, yellow to be ok, and red to be bad. If you use red to represent positive data, it can confuse your stakeholders. Double-check for visualization errors Never rely on automatically generated visualizations. Sometimes, a coding or data error can cause your visualization to display incorrect data, which could mislead your stakeholders. Always manually check the visualizations to ensure they share your expected story (and data). Forgetting axis labels Even if it seems obvious, always include labels for chart axes and a legend if needed. Be sure units are also included on the axis. Avoid clutter When it comes to dashboards and presentations, less is often more. Focus on the most important graphics to avoid cluttered presentations. Presenting the same data in multiple visualizations or squishing lots of graphics on a page or presentation is rarely needed. Remember, if someone wants to sit down with you to look at the raw data or other visualizations in more detail, you can do that 1:1 when appropriate. Building a career with data visualization skills Data visualization is used in many career paths, including: Data Visualization Analyst or Specialist: This entry-level role focuses on developing dashboards to present data. Data Visualization Consultant: This project-based role provides organizations with suggestions and strategies for data analytics and visualization. Data Visualization Developer: This technical role focuses on coding and programming to develop custom software and data applications for visualizations. Data Visualization Manager: This senior role oversees an organization's data visualization teams and may be involved in strategy, ensuring quality standards, and supporting business objectives. Data Analyst: This entry-level role could be mid to senior for more skilled candidates. They support collecting, cleaning, and modelling data points to provide analysis and insights. Some roles require you to have data visualization skills as well. Data Scientist: After you have experience as a data analyst, you can seek a more mid-level or intermediate role as a Data Scientist. You will perform many of the same duties but are expected to do them with more detail and skill. BI (Business Intelligence) Developer: Another mid to senior role in data analytics is a BI Developer. In this role, you’re responsible for designing and developing business intelligence solutions using your coding knowledge or preexisting visualization software, generating custom reports when asked, and presenting results and insights to stakeholders. Are data analytics roles right for you? Learn what makes data analytics an excellent career choice. Learn more about data analytics career paths: Understand the job of a Data Analyst How to get a job as a Data Analyst Strategies to enhance your data visualization skills If you’re seeking a career in data visualization, foundational knowledge in data science and graphic design is essential. To earn promotions or more senior roles, you’ll need hands-on experience and a portfolio of data visualizations or reports you’ve created. You can get the skills and experience you need through: Learning more about basic data analytics and visualizations in the Intro to Data Analytics Course at Lighthouse Labs Get a diploma in data analytics from a Data Analytics Bootcamp Obtaining industry credentials (such as Tableau Desktop Specialist Certification or Google Data Studio Certification) Taking design courses and programs Expanding your hands-on experience (with portfolio samples) The online Data Analytics Bootcamp from Lighthouse Labs is not just about data visualizations and dashboards. It's a comprehensive program that covers the four pillars of data analytics, including coding (Python, PostgreSQL, Git, and Jupyter Notebook), soft skills, and data analytical skills needed to succeed in your career as a Data Visualization Specialist. Browse the complete Data Analytics Curriculum to learn more about this exciting, job-ready bootcamp. Get more details and register for the next start date of the Data Analytics Bootcamp from Lighthouse Labs.