How can you teach data visualization skills without overwhelming learners?

By defining some metrics from day to day activities, collecting some data and then visualizing with basic charts i.e. running statistics, grocery lists, transportation expenses, etc.

You can begin by establishing a solid understanding of data types (numerical, categorical), data collection methods, and basic data analysis concepts. This equips learners with the necessary background to comprehend data visualization techniques. You may then proceed to introduce basic visual representations like bar charts, line charts, and pie charts. Explain their strengths and weaknesses, and when each is appropriate for different data and goals.

When starting to learn something from the ground up, it's crucial to have a solid grasp of the fundamentals. When it comes to learning visualization, it's advisable to grasp the concepts of graphs and their applications before diving in. For instance, if you aim to represent gender distribution as a percentage, which type of graph would be most suitable? A bar graph, pie chart or Matrix? A capable mentor should possess the expertise to guide beginners effectively, offering clear examples for each type of visualization. Prioritizing theoretical understanding alongside practical, real-time visualization experiences can greatly enhance the learning process.

Treating data visualization like a journey through a magical forest. To guide our learners, we start with the basics, just like packing the essentials for an adventure – a map, a compass, and some snacks. In our case, these essentials are the fundamental concepts of visualization, like axes, labels, and chart types. We introduce these concepts using fun analogies, comparing axes to the compass guiding us, and labels to the signs pointing out interesting landmarks. We start with simple potions, like the Bubble Potion for basic scatter plots or the Line Elixir for tracking trends. These potions are easy to handle and don't require a lot of magic to brew. Each new potion unlocks new possibilities and adds excitement to our journey.

Data visualisation speaks a lot about the trends and insights within data. The best way to learn data visualisation is by using Microsoft Excel to plot multiple charts on a simple dataset. This practice helps the learner understand how to use different plot types, and Excel is the best tool for getting started with data analysis and visualisation.

How good a view can be if it is not serving its purpose? Data can be used in different ways to tell the story. The visualizations or metrics we choose to tell that story should be based on the purpose. One thing that really helps me all the time is walking the talk (in the sense of manufacturing processes) - compare the process flow vs data flow and understand the pain points then provide an initial solution that can be improved based on the feedback. The adjustments are always much needed as the learning happens on both sides and we open the box of ideas on what else can we do to make tomorrow a better day by taking these data driven actions.

When it comes to focusing on the purpose, it's about connecting the dots between data and real-life situations. Like cooking, knowing why you use certain ingredients helps make a dish that makes sense. Similarly, showing learners the real-world impact of visualizing data adds context and makes it less overwhelming.

Over time, as the landscape of data analytics have changed or rather evolved, the art of storytelling has emerged as one of the most sought after aspect. In the art of storytelling, both the story and the audience are important and the understanding of both goes a long way. Usually in data analytics and visualization, teb audience is senior leadership and it becomes difficult for data analysts and mid level managers to step into the audience's shoes to understand the requirements and preferences. It is very important to provide a clear checklist of expectations and how and what to deliver while giving reasonalble amount of space for creativity and imagination.

The key to teaching data visualization is to make sure students understand that it's a tool for communication, not just for making pretty graphs. The plan would be: - Start with the goal of making data clear and actionable, aligning with learners' goals and considering the audience. - Incorporate real-world scenarios and case studies demonstrating the real impact of good visualizations. - Interactive activities, collaborative feedback, and self-assessment that are consistent with the purpose of the visualization. - Exploration and application in real-world contexts, supplementing learning with a variety of visualization libraries and tools to build dashboards.

Purpose and audience must be the first thing, don't start a visualization project, without the understanding of: 1. Who are you talking to with your data? 2. What do you want to emphasize? Things like The audience is ready for a very detailed and technical level or they need an executive summary. The best approach is a basic layout with bars and lines plotting or maybe You can introduce maps and geographic distributions. Remember, visualization can be the end of a very long journey of data processing, modeling, hundreds of iterations; don't waste the entire team's effort.

Start with a basic visualization method, such as guided imagery or drawing simple diagrams, and provide ample practice and feedback until learners feel comfortable. Once they have mastered the first tool, gradually introduce additional tools, such as mind mapping or concept mapping, again providing plenty of practice and support. By focusing on one tool at a time, learners can build their skills incrementally, gaining confidence along the way without feeling overwhelmed by too much information or complexity all at once.

Instead of overwhelming learners with multiple tools, focus on teaching one visualization tool or software at a time. For instance, if introducing a tool like Power BI, I break down the learning process into manageable steps, starting with basic functionalities such as importing data and creating simple visualizations before moving on to more advanced features.

In my career, I have learned multiple tools. As a recruiter, if I have to recruit a Tableau Developer I will focus more on the capability of the candidate to learn a new tool than on his/her current skills. Once you know one data visualisation tool, you can easily learn a new one. It is simply question to apply your skills in a different framework, but the essence of the work doesn't change

Power BI, Tableau, QlikSense, IBM Cognos and many other to name, these tools are nothing without basics understood properly. I focus that fundamentals are taught correctly and new learners establish a good understanding. They can be moved to any tool once they understand how to deal with data.

I usually start teaching what is visual analytics, why it is important without introducing any tool. The idea is that you can use this new mindset everywhere. Even in a Powerpoint :) Tableau Trainings are always separated.

There is a lot of different softwares allowing interactive data exploration, like Flourish, Tableau or Datawrapper (non exhaustive list). Why it is important ? Because, by having an interactive visualisation, you can introduce, comments, instructions, how to guide, explanations for your users. But you can also create a drill down into the data to facilitate the comprehension of a phenomenon. As for instance, when an user click on a category, he will be able to drill down into a different view providing a sub category split.

Teaching data visualization is easier when we make it fun and team up. Starting with simple ideas, like how to draw charts and graphs. Real examples show where we use this in real life. Do cool projects together, making learning more like playing. Games and stories make it interesting. Web talks let us ask questions right away. Go step by step, and can go at our own speed. Working on fun challenges and share our ideas. Learning becomes a group thing, like a team. It's like solving puzzles together, and it's not too hard. That way, we learn and have fun without feeling stressed.

Incorporate interactive activities and group projects to engage learners and encourage collaboration. For example, I often organize workshops where participants work together to analyze datasets and create visualizations. This not only enhances understanding but also allows learners to learn from each other's experiences and perspectives.

Nada mejor que proponer dinámicas donde el alumno tengo que poner el cuerpo, levantarse de la silla, salir de lo habitual. Por ejemplo, un gráfico de 2 dimensiones que sea todo el piso del aula y que los alumnos sean los puntos del gráficos. ?Queres trazar rectas ? Utiliza cinta de papel.

Incorporating interactive and collaborative methods helps learners actively engage with the material. This approach allows them to participate in the learning process, ask questions, and collaborate with peers. Interactive elements, such as hands-on exercises and group projects, make the learning experience more dynamic and enjoyable, preventing learners from feeling overwhelmed while fostering a sense of community and shared learning.

Offer constructive feedback and support throughout the learning process. Encourage learners to ask questions and seek clarification whenever they encounter challenges. I often provide personalized feedback on assignments and projects, highlighting both areas of strength and areas for improvement.

One service that I provided, while working as a Center of Enablement Lead, was retrocession where the developer showcased his dashboard and we were asking him questions about his goals, to provide the feedbacks in term of best practices

Don't hesitate in taking help from your trainers and colleagues/friends in this field. By doing this you will learn new ways to handle a data visualisation task. Every one has a different point of view, sometimes these observations provide the best results.

Do not get attached to your creation. It's just a dataviz and the feedback is more important. Once you finish, test it. Test filters, and interactions in some different devices. After that, get feedback from the person or team who is going to be using it daily.

I cant stress enough how important it is to ask for feedback and give feedback. Asking learners to teach the teacher after a lesson and ask and give feedback allows for a two way path to growth. Remember, without feedback there is no growth

Data visualization is a rapidly evolving field, so it's important to stay updated with the latest tools and techniques. As an instructor, I continuously seek out new resources, attend workshops, and experiment with different visualization tools to enhance my skills, which ultimately benefits my teaching.

Attending a workshop is only the first step. As good as your teaching may be, it’s not going to revolutionise people’s lives (sorry if this breaks your heart). At best, you’ll inspire participants to practice continuously and learn more from the additional resources you provide :)

I encourage participants on my courses to be more attentive to high quality visualizations they might come across in their daily lives (e.g. on TV sports programmes, economic news articles, weather reporting), to see if there are visualization ideas they can steal and apply them to their work.

Storytelling in Data Visualization never ends. One should not only be aware of new charts but also its audience. Different clients gives us more perspective on the requirements and their capability to digest simple or complex visualizations.

Being a mentor to someone, attending live interactive webinars, contributing to open source events and technical documentations would encourage learners to upskill themselves and facilitate constant growth and learning. Another way to improve and learn is to choose a current real life problem or scenario and brainstorm to solve it so that the skillsets can be put into practice.

Always keep data visualisation simple, and always remember that less is more. Focus on the story you are telling and don't clutter

Step by step. Tell them how long it took you to be where you are today. That it took you too a lot of time and practice. If you look at LinkedIn, to your role models, it seems it is so easy but it isn't. By showing your path and helping them to be patient, it is more likely that they will trust and follow you. Let them feel comfortable and safe, so they dare to ask anything they need to. Create a safe atmosphere.

One of my passions is developing user-centered interactive dashboards to support evidence-based decision-making in healthcare and medical education. I always stress the importance of simplicity in data visualization. It ensures clarity and accessibility, empowering users to extract insights efficiently.

The whole point of data visualisation is not to overwhelm the audience, but to help the key points stand out. If the DataViz Teacher doesn’t understand that, they probably shouldn’t be teaching this topic! DataViz can be defined in many ways: creating a table or chart is a simple way of visualisating data. Creating a dashboard is a more complex array of data visualisations. Considerations to start: - Start with the story, NOT with the data: what’s the reason for the analysis and what’s the outcome of the analysis? - Who is the audience? Specialists might require more source data than senior leaders. - How complex is the issue? Are there are multiple drivers that need to be understood (levers for decision-making)? - Reduce ‘visual noise’!

Eu come?o mostrando a importancia dos dados, com simples exemplos, para despertar o interesse e fazer com que fa?a sentido. I generally start by showing the importance of data, with simple examples, to spark interest and make sense.