You're looking to improve your data visualization skills. How can you find the best resources to help you?

Recently, I started training in IBM's Data Science. One of the initial lessons emphasized that it's not about being the best engineer or statistician but being detail-oriented and passionate about obtaining information. The importance of cultivating a curious mind and being in constant pursuit of data for comparison and analysis was stressed. The first step is to identify your strengths, your specific focus, whether it's in e-commerce, agriculture, urban planning, retail, etc. Once you've identified your field, focus on developing it and enhance your skills through conferences, courses, and don't forget to find a good mentor.

Start by looking at where you are with data visualization – are you just starting, kind of okay, or already a pro? Think about what you're good at and what you want to learn more about. Once you know that, finding the right help is like picking a video game level that's just right for you. So, figure out your starting point, what you want to learn, and then choose resources that match your data adventure!

Comece identificando suas áreas de interesse dentro da visualiza??o de dados, como gráficos, dashboards, ferramentas de visualiza??o ou técnicas de design. Em seguida, explore uma variedade de recursos disponíveis, como tutoriais online, cursos em plataformas de aprendizado, livros especializados, comunidades de visualiza??o de dados e conferências da área. Priorize recursos que ofere?am exemplos práticos, exercícios interativos e feedback construtivo.

Alright, before you jump into the data visualization learning frenzy, take a minute to size up your current skills. Hit up some online self-assessments or quizzes—get that digital measuring tape out. Don't shy away from feedback either; let your peers or mentors spill the tea on your strengths and areas that need a bit of love. Dive into your past projects; see how they stack up against the cool kids in data viz. It's like a friendly reality check to map out where you shine, where you stumble, and where you're just getting started. Once you've got the lay of the land, set those goals—realistic, achievable, and ready to take your data viz game up a notch. Time to level up!

Do "SOME" research on what tool people/companies are using in the market. For this YouTube, google, blogs, etc would be a good start. Based on it, assess yourself on do you know any tool which is similar to it? If you do how good are you and how confident are you with it? Can you represent whatever is needed with it? These questions would help you understand your stand. Look into advanced visualization projects and see if you can establish link in your head on how it was done (backtracking).

It's a bit like choosing the right crayons for your drawing. Each tool has things it's good at and some things it's not so good at. Your job is to pick the tools that fit what you need, what you like, and what you can afford. Think about how big your data is, how much you want to play with it, and if the tool works well with your other stuff. Also, check if it's easy to learn and if there's someone there to help if you get stuck. It's like picking the right toys for your playtime – you want them to work well together.

It's tool-picking time in the data viz playground, and the options are aplenty. When making your choices, think about your data—what type and size are we talking about? How much customization and interactivity do you crave? Consider the compatibility dance with other tools and systems, and don't forget to factor in the learning curve and support you'll need. It's a bit like picking a superhero team; each tool has its own powers. Dive into online reviews, comparisons, and tutorials to get the lowdown. Your goal? Find the tools that vibe with your needs, preferences, and budget. Time to build your data viz arsenal!

When embarking on a new endeavor, taking baby steps marks crucial milestones along the way. Becoming an expert with a tool doesn't happen instantly. Instead, you start by mastering something simple and gradually expand your skills. Are you only able to plot charts? Can you only draw graphs? Well, that's a start! Numerous tools facilitate these tasks effortlessly. Once handling basic data becomes instinctive, it's time to venture into your next baby steps: advancing to render your data more dynamic, comprehensive, or even interactive. These enhancements will naturally ensue. Excel and PBI would be a great start. Then you can use Alteryx if you do a lot of modeling or if you want dynamic templates.

Lo principal aquí no es pensar en el ahora, sino más bien en el largo plazo. ?Qué herramientas se utilizan en los lugares en los que yo quiero participar de aquí a 2/3/5 a?os? Y enfocarse en esos. Algunos serán más de ámbito empresarial, como #PowerBI. Otros seguramente apunten a desarrollos más de código abierto, como #Tableau. Y saber cuánto estamos dispuestos a invertir, no solo en dinero, sino en tiempo y esfuerzo

Some data visualization tools for reference: Business Intelligence Platforms: Tableau, Power BI, Qlik Sense Programming-based: R (ggplot2), Python (Matplotlib, Seaborn) Drag-and-Drop Tools: Looker Specialized Tools: Chart.js (for web), geospatial tools There's no single "best" tool. The right choice depends on your specific requirements and circumstances.

After picking your tools, it's time to learn the basics. Start by checking out the official instructions or guides that came with the tools. Think of it like reading the manual for your game. You can also look for online classes, books, or videos that explain how to do cool stuff with your tools. It's like watching someone play with those toys to see how it's done. To get good at it, practice making simple charts or maps, and learn how to play around with the data.

Learning, learning and learning are my three keys We are more valued because we have knowledge that other people do not have Knowledge is power

Es sumamente importante familiarizarse con la interfaz, las secciones de funcionalidades, y la tan olvidada sección de ayuda (que , ?vamos!, para algo la ponen los desarrolladores de ese software). Saber a donde ir a buscar cada cosa que vayamos necesitando

Now that you've got your tools in hand, it's time to hit the books—figuratively speaking. Start with the official documentation, user guides, or tutorials generously provided by the tool developers or vendors. Hunt down online courses, grab some books, or binge-watch videos covering the ABCs of data visualization with your chosen tools. Get your hands dirty—practice creating those simple charts, graphs, or maps. Learn the art of manipulating, filtering, and formatting data like a pro. It's all about mastering the basics, building that foundation to unleash your data viz wizardry. Let the learning adventure begin!

In my experience, some of the best teachers and/or mentors for any new desired skill or skill sets can be found within your company (sometimes sitting right next to you). As you cross-collaborate or have casual conversations, raise your idea and navigate through your peers until you find either the department or co-worker who can share their knowledge. Who knows, they might have stumbled upon a similar scenario and might have best practices or a solution handy

Master key principles of data visualization, emphasizing simplicity, clarity, and accuracy. Strive for relevance, consistency, and thoughtful color usage. Embed a narrative in your visualizations to engage viewers effectively. Resources like books and online courses can deepen your understanding, while hands-on practice with tools like Tableau or Matplotlib enhances your skills. Joining data visualization communities provides valuable feedback and inspiration for continuous improvement.

After figuring out how to use your tools, you also need to know the tricks for making your data look great. This means picking the right kind of chart, using colors and shapes in a smart way, and making sure things aren't messy or confusing. It's like making your game characters stand out so you can follow the story easily. You can learn these tricks from easy-read books, blogs, podcasts, or online classes by experts who know how to make data look awesome.

Beyond mastering the tools, let's delve into the art of effective data visualization. Picture it as the Jedi training of the data world. Learn the ways of choosing the right chart—mastering the lightsaber of data representation. Tread wisely with color, shape, and size, avoiding the dark side of clutter and distortion. Add context and annotations like secret scrolls in your visualization arsenal. And always design with your audience and purpose in mind—tailor-made Jedi robes for your data story. This wisdom isn't just found in scrolls; seek it in books, blogs, podcasts, and webinars where the masters of data visualization drop knowledge. Embrace these principles, and may the data force be with you!

Take up side projects and freelance projects along with regular job. Working on diversified requirements will eventually add to the skills.

Getting better at data visualization is an ongoing process. The more you practice and experiment with varied data sets, the more this skill develops.

Hacer, elaborar y exponer el producto...una y otra vez es el ciclo de mejora continua. Retroalimentarse desde la experiencia del usuario, del cliente de quien es el que tiene la necesidad. No es una prueba de verificación, es el ejercicio que debe de ser un paso más en la obtención de objetivo de poder mejorar la visualización de datos.

Now that you've got the basics and principles locked in, it's time to put your skills to the test. Experimentation is your data playground—apply your newfound knowledge to diverse data sets, scenarios, or challenges. Dive into online data sources, be it government websites, open data portals, or academic databases. Don't hesitate to use your own work or personal project data as a canvas. Level up by joining online communities, forums, or events where data enthusiasts unite. Share your visual masterpieces, soak in feedback, and learn from the journeys of fellow data explorers. It's the wild west of data creativity—so saddle up, chart your course, and let the data experiments commence!

The best way to build upon a learned skill is to practice. Taking on a project about something or a topic you find interesting can help. I'm a deep believer in following one's curiosity. So, pick a project of interest and find a challenge you can use your skillset to provide a solution to.

Join online communities and forums dedicated to data visualization, such as Reddit's r/dataisbeautiful and LinkedIn groups focused on data visualization professionals. Engage with fellow enthusiasts, ask questions, and share resources to expand your knowledge and network.

To enhance the data visualization skills, one can explore resources like online courses at moment such platforms are available on eg Coursera or Udacity also

Online Courses: Enroll in courses on Coursera, Udemy, LinkedIn Learning, or similar platforms. Look for courses that offer a mix of theory and practical application, especially those focusing on tools like Tableau, Power BI, or Qlik Sense. Essential Reading: Books like "Storytelling with Data" by Cole Nussbaumer Knaflic offer valuable insights. Practical Experience: Practice with real datasets from Kaggle or Tableau Public, and participate in online challenges. Community Engagement: Join forums and follow blogs for feedback and the latest trends. Interactive Learning: Attend workshops and webinars for hands-on learning and expert advice. Networking: Connect with professionals and groups in the field for mentorship and peer learning.

To stay updated and continuously enhance data visualization skills, it's crucial to establish a personalized and comprehensive learning strategy. Begin by cultivating a habit of regularly following reputable blogs, newsletters, and other online accounts dedicated to analytics and data visualization. Enroll in online courses (free or paid) to keep updated in analytics and BI tools.

In the ever-evolving realm of data visualization, staying in the know is key. Trends, tools, and techniques pop up like mushrooms after rain, and you want to be on top of it all. Dive into the data visualization chatter by following blogs, newsletters, podcasts, or social media accounts of the mavens in the field. Keep your radar tuned to updates from organizations or platforms pushing the data viz boundaries. Take it a step further—attend online or offline conferences, workshops, or meetups. It's not just about learning; it's a chance to rub virtual (or real) elbows with fellow data enthusiasts. Network, swap ideas, and absorb the freshest insights in the data viz universe.

Books such as "The Visual Display of Quantitative Information" offer valuable insights. Follow YouTube channels like Data School and participate in webinars by Tableau or Microsoft. Engage with data visualization communities on Reddit or blogs like FlowingData. Practice with real-world datasets on Kaggle. These resources provide hands-on learning and keep you updated on industry trends.

Mastering data visualization is like learning a new language, one that allows you to communicate complex ideas simply and effectively. It’s not just about pretty charts; it’s about telling the story within the data. The right visualization can turn data noise into music to the stakeholders’ ears.