How can you prepare for a data visualization interview that tests communication skills?

Sometimes you cannot prepare yourself specifically as you want, for example, when the company has a generic technical challenge. In these cases, having a wide general portfolio that presents all your skills in different software and different contexts, and explains why you made some choices. This is a good gateway to data visualization opportunities. Às vezes você não consegue se preparar especificamente como deseja, por exemplo, quando a empresa tem um desafio técnico genérico. Nestes casos, ter um amplo portfólio geral que apresente todas as suas habilidades em diferentes softwares e diferentes contextos, e explique porque você fez algumas escolhas. Esta é uma boa porta de entrada para oportunidades de visualização de dados.

Understanding the domain in which you are visualizing your data is very important. It's best to acquire functional knowledge as a data visualization expert. Having some knowledge of color theory also helps.

Sure thing! To get ready for a data visualization interview that checks your communication skills: Practice Simple Explanations: Practice explaining complex visuals in a simple way. Use clear, everyday language. Know Your Data: Be super familiar with the data you're presenting. Know the story behind it. Use Real-Life Examples: Share examples from daily life to explain your visualizations. Make it relatable. Highlight Key Insights: Focus on the main points. What's the story your data is telling? Highlight that. Get Feedback: Practice with a friend or family. Ask for feedback on how easy it is to understand your explanations. Remember, clarity is key. Break down the data so anyone can follow along easily!

You just need to master one data visualization tool. Most people choose power bi or tableau. More important than the tool is knowing data aggregation and data modelling. After mastering these, just pick any data visualization tool and practice.

No one is good at all tools. My advice? First, master one tool and the theoretical knowledge behind data visualization, and learn the fundamentals of other tools. This may be enough for people to give you opportunities. Furthermore, from time to time recycle your knowledge and little by little invest in learning other tools. Ninguém é bom em todas as ferramentas. Meu conselho? Primeiro, domine uma ferramenta e o conhecimento teórico por trás da visualização de dados e aprenda os fundamentos de outras ferramentas. Isso pode ser suficiente para que as pessoas lhe dêem oportunidades. Além disso, de tempos em tempos recicle seus conhecimentos e aos poucos invista no aprendizado de outras ferramentas.

It's very important to have a portfolio to showcase your skills. Novypro is a good platform for this. Various platforms also keep dashboarding competitions (Codebasics for example). It's a good idea to participate in these competitions and get better at your craft.

Having an updated portfolio is very important. But most times, your first contact with one opportunity is through HR specialists (who are not obligated to know everything of data visualization). So, good tips beyond the portfolio are to create articles, carousel posts on Linkedin, and interact with the community. This is going to open many job opportunities to you. Ter um portfólio atualizado é muito importante. Mas na maioria das vezes, o seu primeiro contato com uma oportunidade é através de especialistas de RH (que não são obrigados a saber sobre visualização de dados). Então, boas dicas além do portfólio são criar artigos, fazer posts de carrossel no Linkedin e interagir com a comunidade. Isso te abrirá muitas portas e oportunidades.

There are two things that need to be explained about the process: 1) Where the data is coming from. 2) What data transformations were made to this data to make the data visualizations? This process gets a lot easier if you have the right domain knowledge and data modeling skills.

Another way of looking at this is to consider hypothesis testing. We have a pre-existing belief, and we are using data visualization and statistics to challenge this belief. The pre-existing belief is the null hypothesis. And the belief that we will form if the null hypothesis is proved wrong is the alternate hypothesis. Our data analytics is all about figuring out which hypothesis is correct. You can also approach this process like a story telling process.

Preparing for a data visualization interview that assesses communication skills involves technical knowledge & storytelling. Review fundamental data visualization concepts, such as types of charts/graphs, color theory, and best design practices. Tailor your visualizations and explanations to the audience's level of technical expertise. Work on telling a cohesive story with your data by identifying insights. Practice solving real-world problems using data visualizations. Conduct mock interviews with a friend or mentor. Stay informed about the latest trends and best practices in data visualization. Remember that effective communication is not just about creating visually appealing charts but also about conveying meaningful insights.

There are many different processes, software, and data visualization products (such as data stories, dashboards, charts, metrics, subscriptions, alerts, actions, metrics, etc). Create communication channels (such as LinkedIn posts for example) to let companies and other professionals know that you know all the products and how to better develop and use them. Existem muitos processos, softwares e produtos de visualização de dados diferentes (como histórias de dados, painéis, gráficos, métricas, assinaturas, alertas, ações, métricas, etc.). Crie canais de comunicação (como posts no Linkedin por exemplo) para que empresas e demais profissionais saibam que você conhece todos os produtos e como melhor desenvolvê-los e utilizá-los.