Voici comment vous pouvez passer d’un autre domaine à une carrière en visualisation de données.

Learn the Basics: Understand graphs and charts. Technical Skills: Learn Tableau, PowerBI, Python, R, and SQL. Here’s a concise guide on how you can make the transition: Practice: Work on data visualization projects. Portfolio: Showcase your work in a portfolio. Networking: Connect with professionals and join communities. Continued Learning: Stay updated with the latest trends.

La visualización de datos es necesaria en casi cualquier área profesional, desde ciencias sociales, campo sanitario, ingeniería, retail, servicios públicos y privados e industria. Esa necesidad es un buen punto de inicio para sin abandonar tu posición actual, empieces el camino de los datos en un trabajo en el que eres experto y por tanto cliente, donde te resultará más fácil conocer qué necesidades puede cubrir el análisis de datos. Así irías de lo particular a lo general y tendrás una primera experiencia que sirva de palanca para tu salto al análisis de datos puro.

Moving from being a Postdoctoral Researcher into a Data Analyst role required deliberate steps. I focused on gathering the basic knowledge of Data Analysis. I learnt more about MS Excel, Power BI & SQL. I gained basic knowledge through hands-on tutorials and projects, creating various data visualizations to understand data storytelling. I practiced regularly, experimenting with different datasets to hone my skills in creating insightful visual representations. I had mentors and sought guidance. I applied for Data Analyst roles, showcasing my learning journey and practical experience through a portfolio of projects. Each step reinforced my commitment to mastering Data Visualization, which prepared me for a successful career transition.

Switching to a data visualization career from a different field can be highly rewarding. Start by recognizing the value of your current skills and how they can provide a unique perspective. Build a strong foundation in data visualization principles and tools like Tableau or Power BI through online courses. Develop your analytical skills, practice with small projects, and create a portfolio to showcase your work. Engage with online communities, seek mentorship, and stay updated with industry trends. With dedication and the right approach, you can successfully transition into this dynamic and exciting field.

Começar na visualização de dados exige um entendimento sólido dos fundamentos. Dominar os diferentes tipos de visualizações e os princípios de um bom design é crucial. Isso não apenas facilita a comunicação de informações complexas de forma clara, mas também ajuda a construir confiança na criação de narrativas impactantes baseadas em dados. Aprender teoria de cores e tipografia pode parecer trivial, mas é essencial para quem deseja se destacar na área.

Microsoft Excel is the most viable one especially if you just started to do data visualisation. Excel is already familiar with the majority of people and always updated to make it easier to use, the only drawback is the limited data entry, just as much as an Excel sheet can contain. Start small, make a database of things that you want to present by categorising them with its properties, make a pivot table to summarize, and make a chart of the pivot table.

Escolher a ferramenta certa é um passo crítico. Do Excel ao Tableau, cada uma tem seu propósito e complexidade. Experimentar versões gratuitas é inteligente para encontrar aquela que melhor se alinha com seu estilo e necessidades de aprendizado. Dominar a ferramenta escolhida não só facilita a criação de visualizações complexas, mas também aumenta sua empregabilidade ao demonstrar proficiência prática.

Selecting the right data visualization tool is pivotal in transforming raw data into meaningful insights. Tools like Tableau, Power BI, and even Excel offer varying levels of complexity and functionality suited to different user needs. Starting with free versions or trial periods allows you to explore their features and determine which aligns best with your visualization goals and workflow.

Equipping yourself with the right tools is the next step. In my transition, I familiarized myself with industry-standard software like Power BI, which has been invaluable in my work at INVISION Architecture. These tools are the instruments through which your data will sing.

Dans un projet de reporting mensuel, j'ai commencé par comprendre quels types de visualisations seraient les plus efficaces pour représenter les données. J'ai utilisé des diagrammes à barres pour comparer les ventes mensuelles et des cartes thermiques pour montrer les variations régionales des ventes. En suivant des cours en ligne sur la théorie des couleurs et la typographie, j'ai pu créer des visualisations esthétiques et informatives. J'ai raconté une histoire claire avec les données, aidant les parties prenantes à comprendre les tendances et à prendre des décisions éclairées.

The key to building any effective visualization is understanding what question you’re trying to answer. You can find sample data online, but ideally get some data directly related to your current role, even if you have to track your own work, and try different kinds of visualizations to see which ones are most effective at communicating the data. Sometimes simple is best: don’t underestimate the utility of just showing the number. Other times, viewing segments or progress over time is what you need, so a different type of chart works best. Incorporating color coding based on favorable/unfavorable change or performance against goals can turn a plain number into a lot more. Knowing which visual to choose starts with knowing your business.

Para tener una buena practica en modelación y visualización de datos , primero tienes que saber como esta estructurda tu base de datos , es decir , cual es tu alcance en la información que obtines y hasta donde puedes llegar a analizar , que calcúlos , que información demografica , que campos auxiliares , que dimensiones son las que cuentas , y saber que tipo de agrupaciones debes de hacer , ya que tienes que mostrar inforamcion reelevante dependiendo aquien se la estas mostrando ya que un enfoque estrátegico no es lo mismo que un operacional

Praticar é onde o conhecimento se transforma em habilidade. Começar com projetos simples e evoluir para desafios maiores não só aprimora suas habilidades técnicas, mas também constrói um portfólio sólido. Visualizar dados do seu dia a dia ou de interesses pessoais não só melhora suas habilidades, mas também mostra sua capacidade de aplicar conceitos teóricos na prática.

You could switch to a data visualization career from a different field by following the below step: 1) Expand your connections with Data Visualization experts and learn from them, get advice from them and get help from them in this career. 2) Data visualization not something theoretical, it is upon practice and hands-on experience. So, do practice with the Data visualization tools. 3) Enroll in certification programs or specialized courses in data visualization and it in the resume. 4) Built a strong portfolio to showcase your skills in data visualization. 5) Learn some basic and fundamental concepts about Data Visualization.

Practice is pivotal in mastering data visualization. Start by applying foundational knowledge to small projects or datasets, such as from your current role or personal interests. This hands-on approach not only enhances your skills but also builds a portfolio showcasing your ability to transform data into actionable insights. As you progress, tackle more challenging projects to further refine your creativity and proficiency in visual storytelling.

A nice tip that makes your portfolio stand out, is to apply your previous career's knowledge to your projects. Leverage relevant data and visualize KPIs tied to your past career. Create a unique project showcasing your expertise to demonstrate how data visualization adds value. Highlighting your skills in a familiar context enhances your credibility and impact.

Create an Online Presence: Portfolio Website: Showcase your projects, skills, and experience on a personal website. LinkedIn Profile: Highlight your transition, new skills, and portfolio. Connect with professionals in the field. Share Your Work: Social Media: Regularly post your visualizations on platforms like LinkedIn, Twitter, and Instagram. Blogs and Articles: Write about your projects and the data visualization process. Share insights and tutorials.

Um portfólio bem construído é essencial. Ele não só destaca suas realizações em visualização de dados, mas também conta a história por trás de cada projeto. Documentar seu processo e o impacto das suas visualizações é crucial para demonstrar sua capacidade de gerar insights acionáveis a partir de dados complexos.

A robust portfolio is crucial for establishing a career in data visualization. Showcase a diverse range of projects that highlight your expertise in both design and data analysis. Each project should include clear explanations of your approach and the impact of your visualizations. Whether through a personal website, blog, or shared folder, ensure your portfolio is accessible and well-documented, making it easy for potential employers to navigate and understand your capabilities.

Maintaining a portfolio of sample work is a great way to show your skill level and share your own ideas and innovations; Tableau in particular champions this by allowing users to maintain a Tableau Public profile, where you can curate, highlight and share sample dashboards of your own, as well as viewing other users' work.

LinkedIn: Profile Optimization: Ensure your profile is up-to-date, highlighting your skills and projects in data visualization. Connect Strategically: Connect with professionals in the data visualization field, including analysts, designers, and recruiters. Engage with Content: Like, comment on, and share posts related to data visualization to stay visible in your network. Join Groups: Participate in LinkedIn groups focused on data visualization and data science. Twitter: Follow Experts: Follow key figures in data visualization and data science. Engage in Conversations: Participate in discussions and share your insights on trending topics. Use Hashtags: Use relevant hashtags like #DataViz, #DataScience, and #Tableau.

Networking can provide invaluable opportunities and insights. I connected with professionals in the field, attended workshops, and participated in online forums. These connections have been a source of support, inspiration, and learning.

Get in to the linked in and there are karge no of groios available for data visualization enthusiasts. They are hoaying challanges and participate every possibke and showcase your talent..then you can easily connected with greats in the field

Networking é uma parte vital da transição de carreira. Engajar-se com profissionais do setor, participar de grupos e eventos online amplia suas oportunidades de aprendizado e colaboração. Estar conectado não apenas mantém você informado sobre as últimas tendências, mas também pode abrir portas para novas oportunidades de emprego e colaboração.

Networking is pivotal in transitioning to a career in data visualization. Connect with professionals via social media, forums, and local meetups to stay informed about industry trends and job openings. Join data visualization groups on platforms like LinkedIn to engage with the community, gain valuable insights, receive feedback on your work, and potentially secure job referrals. Active participation expands your knowledge and professional opportunities, accelerating your career growth in this dynamic field.

Comece aprendendo as habilidades essenciais como estatística, programação (especialmente em Python e R), e ferramentas de visualização (como Tableau e Power BI). Faça cursos online e obtenha certificações relevantes. Participe de projetos práticos, criando visualizações de dados a partir de datasets disponíveis publicamente, e contribua para projetos de código aberto para ganhar experiência. Desenvolva um portfólio sólido para demonstrar suas habilidades e conhecimentos. Além disso, procure redes de profissionais e participe de comunidades de visualização de dados para se manter atualizado sobre as tendências do setor e oportunidades de emprego.

Stay Updated: Continuous Learning: The field of data visualization is always evolving. Stay updated with the latest tools, techniques, and trends. Workshops and Webinars: Attend workshops and webinars to continue learning and improving.

O campo da visualização de dados é dinâmico. Investir em aprendizado contínuo é essencial para acompanhar as mudanças tecnológicas e metodológicas. Participar de webinars, seguir líderes de pensamento e explorar novas ferramentas mantém suas habilidades relevantes e seu trabalho impactante.

In the dynamic field of data visualization, continuous learning is indispensable. Stay abreast of evolving trends, tools, and techniques by following thought leaders, reading blogs, and participating in webinars and workshops. This proactive approach not only enhances your skills but also showcases to employers your dedication to staying current in the industry. By investing in ongoing learning, you position yourself as a knowledgeable and adaptable professional ready to tackle new challenges and drive innovation in data storytelling.

Read and learn grammer of graphics. It is the facto standard for visualization for different tools and cases. It also helps to learn different tools.

To switch to a data visualization career from a different field, start by learning foundational tools like Tableau, Power BI, or D3.js, and develop strong analytical skills using Python or R. Build a portfolio to showcase your projects, join relevant communities to network and seek mentorship, and apply for entry-level positions or internships. Continuously refine your skills and portfolio based on feedback and emerging trends.