Here's how you can enhance your remote data visualization skills and training.

Online courses are the best, why? In most cases you get to set the learning pace yourself. Secondly, if you cannot pay for a course you can easily utilise the free learning platforms like LinkedIn, YouTube and other resourceful websites. The down side would be taking up many courses without a plan and ending up with confusion. Enhance these skills by starting a course and seeing it through to completion, practice and practice more before taking another one. This way your skills are on point and your learning plan is not a pile of chaos.

Online courses can be helpful, but taking too many can leave you feeling lost. I recommend visiting the official websites for Tableau, Power BI, and similar tools. Look for certification programs, as they provide a clear roadmap for mastering each tool.

I recently have had a great experience with Maven Analytics for learning Python. You can go at your own pace and create your own learning journey. As your progress through each lesson there are assignments followed by the solution. I also really appreciated that they help you set up your Python environment, which can be a bit intimidating as you start your Python journey. My final piece of advice is to carve out time first thing in the morning to dedicate to learning. This gives you the best opportunity for success as you have the most energy and are likely to be able to block time off your calendar.

Explore relevant online courses. Platforms like Coursera, edX, and LinkedIn Learning offer courses on data visualization techniques, tools, and best practices. Invest time in structured learning to build a strong foundation

Having a project to your portfolio is the most important nowadays where competition is a reality. There are plenty of online resources where you can start immediately building projects related with data visualization. The first one is datacamp.com and for Greek speakers datatutor.gr.

Apply what you learn through hands-on projects. Create visualizations using real-world datasets. Experiment with different chart types, color schemes, and interactivity. The more you practice, the more confident you’ll become.

Remember to practice regularly to stay updated on market trends. I typically use datasets from Kaggle and try to analyze them using Tableau to gain insights and present the data effectively. Sports and election data are great options to work with since they are widely known and understood.

Enhancing data visualization skills through regular practice and diverse projects is not just about learning techniques. It's about cultivating a deep understanding of how to effectively communicate insights and tell stories with data. When you actively engage with diverse datasets and experiment with different visualization types, you begin to appreciate the nuances of data representation. Each dataset presents unique challenges and opportunities, pushing you to think critically about which visualizations best reveal patterns, trends, and outliers. This iterative process of exploration and refinement is invaluable for your craft.

Seek feedback from peers or mentors. Share your visualizations with others and ask for constructive criticism. Collaborate on projects or participate in data visualization communities to learn from diverse perspectives.

Data visualization tools and trends evolve rapidly. Follow blogs, subscribe to newsletters, and engage with industry experts on social media. Stay informed about new features, libraries, and design principles.

Data visualization tools and trends evolve rapidly. I prefer subscribing to newsletters about these tools and following relevant hashtags on LinkedIn.

Master the tools you use. Whether it’s Power BI, Tableau, or Python libraries like Matplotlib and Seaborn, delve deep into their capabilities. Understand advanced features and shortcuts to work efficiently.

Regularly assess your work. Reflect on what worked well and what could be improved. Adapt based on feedback and changing requirements. Continuous improvement is key to becoming a skilled data visualization practitioner.

In today’s data-driven world, a well-designed visualisation can be the difference between insights that drive action and information that falls flat. What makes a great visualisation? It satisfies a need, is easy to use, and looks good. Think of each visualisation as a product tailored for your audience. It should deliver the insights they need with precision and style. But creating a dashboard that truly works for your audience requires more than just plugging charts on top of data. It demands a blend of art and science, of design and data expertise. Collaboration is the key. Great visualisations are not built by individuals working "home alone". They require a friendly community to critique, inspire and guide you to reach your goal.