What are the best ways to learn data visualization on the job?

Sometimes, you may take things slowly by creating 2-3 charts showing different features, having drill down features. Having the main objective defined for visualization is crucial. Suppose you want to find trends over the year, or drill down on campaign dip for a quarter. Choice of charts and sometimes even tools will depend on these objectives.

The best way to learn is to set a goal and objective. Practice as much as you can with samples and examples with data sets. Create powerful data visualizations, set goals, and collaborate with others. Ask for feedback to improve the visualization. Test tools and follow best practices. Test and improve through an iterative process that takes user feedback into account. Stay up to date with new data visualization tools, techniques, and trends. Network with professionals and diversify your sources of knowledge to expand your experience and adapt to different scenarios.

In my opinion, the best way to learn data visualisation while on job is to use the data which is from your own department/function. Doing exercises on sample data won’t trigger the urgency to learn more about data visualisation.

Wow!! You're learning data visualization, keep going. If you want to learn more or boost your data visualization skills, here you go; - Keep exploring, just by exploring you learn more & more about visuals. - Keep experimenting, by experimenting with visuals helps you taking right decision which visual will be best fit, later on learn which visual correct with which situation. - Data visualization is a art, and it will be grow more while practicing, so keep practicing for your visuals. ??No worries!! YOU WILL SOON BECOME ARTIST OF DATA VISUALIZATION ??

Always start from the easiest chart Verify the msg is understandable to other people (not your department) Maybe also verify the number in a table Only than start to add layers to the chart and verify every complication you add give more value than error to end user

There are some fantastic online and written resources. In terms of books I’m a big fan of the “Big Book of Dashboards” and “Storytelling with Data” as resources. To get hands on with data visualisation I thoroughly recommend getting involved with communities like ‘Makeover Monday’. Data viz is best learnt through doing.

I've learned a lot from other people and experts in the industry. It's been super helpful to ask for feedback and join in on discussions. There are so many resources out there, especially in the data-driven world we live in. I've found inspiration from reading blogs, watching videos and even studying the dashboards created by experienced analysts. If you need help, don't be afraid to ask your colleagues or check out webinars and events hosted by visualization tool providers like Tableau.

On the job training is the most successful way to learn on real world case scenarios. Learning about data Visualization principles can be achieved by studying.

Observe colleagues or peers' techniques, ask questions, and seek feedback on your own visualizations to improve your skills through practical experience and guidance from those with expertise in the field. Explore books on data visualization by renowned authors such as Edward Tufte's "The Visual Display of Quantitative Information," Alberto Cairo's "The Functional Art," and Cole Nussbaumer Knaflic's "Storytelling with Data." These resources provide valuable insights and practical techniques for creating effective visualizations. Observe Dashboard Reports posted by others on Novypro, LinkedIn & Github. Finally, Never underestimate the power of ChatGPT.

-Learning from colleagues or experts in the field provides valuable insights and accelerates your learning curve. Collaborative learning fosters a rich understanding of effective visualization techniques. -Relying solely on others may limit your creativity. It's important to balance external learning with personal exploration to develop a unique style and approach to data visualization.

Believe it or not, working with tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn is not only fun, but it also allows you to gain practical experience that goes beyond theoretical knowledge. Don't just take my word for it, try it out for yourself! By applying data visualization to real work scenarios, you can create visuals that are directly applicable to your job tasks. This way, you can reinforce your concepts and get a better understanding of the capabilities and nuances of these tools. But that's not all! The iterative process of experimentation and refinement, combined with practical experience, contributes significantly to skill development in data visualization.

Experimenting with tools is a good approach. People having knowledge of multiple BI tools are in high demand for their versatility. For example I use both Power BI and Python libraries plotly and nltk. For a business requirement, I had to create n-garms wordcloud, so I used Power Bi and Python both for the same requirement to display different charts.

I like the try out new tools and approaches to data visualization in my current role when creating reports internally for my team and externally for my clients. One example of this is experimenting with LIVE reporting dashboards like Google's LookerStudio by thinking of new out of the box ways I can visualize various aspects of a client's account performance. This not only provides more visibility to my clients but helps myself and my team optimize from an internal standpoint. Another example of this a being creative in the way that I'm presenting data to clients in regular reports and larger presentations. I like to give myself adequate time to test new graph types or use new data viz tools in the creation process. Hope this helps!

-Hands-on experience with various visualization tools is invaluable. It allows you to discover the strengths and weaknesses of each tool, helping you choose the most suitable one for your specific tasks. -Overemphasis on tools might distract from the core principles of effective data visualization. Understanding the underlying concepts is crucial, as tools may change over time

Remember, Learning data visualization involves actively engaging with data and creating visualizations yourself. Instead of just reading or watching tutorials, you learn through hands-on practice and experimentation. By working with real datasets, experimenting with different visualization tools and techniques, and iterating on your designs based on feedback and observations, you develop a deeper understanding of how to effectively communicate insights and tell compelling stories with data. This approach not only helps solidify your technical skills but also fosters creativity and problem-solving abilities in the realm of data visualization.

You can find many resources and references online for creating effective data visualizations they will help you choosing the right type of chart but there are beat practices in data viz as using appropriate colors and fonts, avoiding clutter, adding labels and legends, and highlighting key points.

Adhering to best practices is crucial, yet it's important to acknowledge that exceptions exist for every rule. don’t be afraid to check and verify with the end users

-Adhering to established best practices ensures your visualizations are clear, accurate, and easily understood. It provides a foundation for creating visually compelling and informative graphics. -Blindly following best practices without understanding the context of your data and audience may lead to generic and uninspiring visualizations. Adapt best practices to fit your specific needs.

Here I can give some best and unique tips from my personal experiences: - Observe real-world projects (with dummy data) uploaded by others on Novypro or LinkedIn or Github. Observe which chart they are using and why? Remember, Why is a key to learn How! - Read some articles on different chart types and How and Why we use them. ChatGPT is the best tool to read these articles. - Download some freely available cheat sheets, tutorials on LinkedIn about Data Visualisation and read them in your free time. - Finally, Do it. Best wishes.

Here are some important data visualization techniques to know: Pie Chart Bar Chart Histogram Gantt Chart Heat Map Box and Whisker Plot Waterfall Chart Area Chart Scatter Plot Pictogram Chart Timeline Highlight Table Bullet Graph Choropleth Map Word Cloud Network Diagram Correlation Matrices

- Testing new visualisations is really helpful to learn new techniques and develop Good Data viz skills. For example, in Excel, we don't have an in-built scroll bar in the Bar chart but we want to do it then we will insert a scrolling button and implement a formula on it and insert it into the bar chart. Another example is the Top N Slicer Button. - Finally, Taking feedback from a client or a mentor is very important to improve our skills. - Stop Thinking and Start Doing. - Be Creative. Best wishes.

-Iterative testing allows you to refine your visualizations based on feedback and data. Continuous improvement ensures that your visualizations evolve to meet changing requirements and audience preferences. -Excessive testing without a clear purpose can be time-consuming. It's essential to strike a balance between refining your work and meeting deadlines, ensuring efficiency in your learning process.

Well, it always depends on the design pattern of the company and what type of company it is. It's never the same for every company. And it's always better to find something new. All the basic kpis are the same but try to find something new.

In my journey of data visualization, I create dashboards using BI Tools and post them in data communities for feedback. Feedback from data professionals helps me test my data visuals and later on, I improve them as suggested to make them better.

You need to experiment and improve a little of things in order to master Data visualisation tools. Small progresses over a significant period of time make a huge difference

If you want to work with a visualization tool you have to do some training first. If you just start, it will take you a lot of time and frustration to find all you need. In my opinion, training that combines the essentials of the tool with practicing will be the best choice. If you're through, you can start with your own data and decide if want to do some advanced training too. Practice the tool with a dashboard visualization challenge will help you focus on a goal while practicing. Besides that, you will have the opportunity to connect with new people and explore different possibilities on LinkedIn to grow your skills together. And from there, you can do some learning on the job. But it will take a lot of time and risks to do so.

To learn data visualization on the job, start by understanding the business context and collaborating with experienced colleagues. Engage in hands-on practice with real-world data, explore popular tools like Tableau or Python libraries, and enroll in online courses on platforms like Coursera. Read books and articles by experts, attend workshops and conferences, and actively seek feedback on your visualizations for improvement. Build a portfolio to showcase your projects, stay updated on industry trends, and experiment with different types of data to understand various visualization techniques. Continuous learning, practical application, and a curious mindset are key to developing effective data visualization skills in a professional setting

Learn data visualization on the job by practicing with tools like Tableau, Power BI or Python libraries, work on real projects, seek feedback, stay updated on best practices, and engage with the data visualization community.

Learning on the job is about continuous improvement. Be curious, explore new techniques, and apply your skills regularly. The more you practice, the more confident and proficient you'll become in data visualization.

My learning path involved: - Developing a passion for the software - Extensive reading of books for a solid foundation - Regularly following blogs to stay updated on new technologies - Dedication to consistent practice - Gaining insights from practical examples