You’re struggling to develop your data visualization skills. What’s the best way to improve?

I disagree with this statement..."Before you dive into the tools and techniques of data visualization, you need to understand the principles and best practices of visual communication." Step 1. Just create stuff, and then try to create better stuff. Analysis paralysis is real for data people trying to build their design skills. Read to get better, not to get started.

Use infographics, thresholds, Colors, type of charts to explain the data in a better way. Do not get carried away by those flashy jazzy visualisations. Most of the times they do not serve the purpose and just make it hard for the users to understand the data. One of the point I realised during my development days is to understand how business user reads the data. Left to right, top to bottom and create the flow accordingly. My boss once told me, business users shouldn’t take more than 10-20 seconds to understand the data represented in the dashboard. If it is taking more time, it’s not a dashboard ??

It all lies in the basics! One must know what kind of visualization will best tell the story. For this, we need to have the basic understanding of the different charts available, which data point to measure on which axis, how to clearly show the data, how to tell a better story, and how not to overwhelm the audience. The next step is to fine tune the charts to show labels better, determine the appropriate use of colors, determine the optimal number of data points and how to clearly and concisely render the data. We need to be aware of the changing visualization trends too, for example how everyone hates pie charts, how tree maps are considered the worst, time series data should only be represented using line charts, etc.

Improving data visualization skills needs understanding of basic principals of visualization and practice.Below are some stratagies to enhance visualization skills. 1. Learn Visualization principles - Understand fundamental principles such as the effective use of color, chart selection, and the importance of simplicity and clarity. 2. Understand the Data: Before creating visualizations, it’s crucial to have a solid grasp of the data you’re working with. This includes understanding the types of data (quantitative vs. qualitative) and the relationships within the data.

My perspective on improving data visualization skills is to start by getting rough ideas for projects. First, understand the important KPIs and analyze the data thoroughly. Then, move on to sketching rough ideas on paper or using sketching tools. The most important thing is to practice, practice, practice.

The only way you’re gonna get good at making good informative visuals is to just jump in and get to it. I really got proficient by doing projects following along with experienced Analysts on Youtube. It’s a two for one deal: you have a project to showcase and you can put your own spin to it to make it more of your own. Also, you are getting experience with making visuals. Do enough of those and you will get better.

Regular practice is crucial for improving your data visualization skills. Consistent engagement with different datasets and tools will help you refine your techniques, develop a better understanding of visualization principles, and build confidence in creating effective visualizations. Practice allows you to apply what you learn, identify areas for improvement, and gradually enhance your proficiency in data visualization.

The more you create data visualizations, the better you will become at it. Don't be afraid to experiment and try different things. Get feedback from others on your work and use it to improve your skills. By doing this you are practically learning faster than you might imagine.

Data Visualization is something that needs a lot of understanding about the intricate details of data and that comes only with practice. Practising data visualization everyday with the dataset of your choice irrespective of the tool used whether it is through coding or non coding is an essential step. It could be sone by participating in viz challenges that release datasets every week like the Makeover Mondays or the Tidy Tuesdays.

Una vez desarrolles una mayor familiaridad con la herramienta que prefieras, es buena idea practicar constantemente con múltiples fuentes de información y con tipos de gráfico distintos. Mi manera de practicar es participando en el desafío semanal de la comunidad de R4DS llamado TidyTuesday. Todos los martes suben a un repositorio de Github una fuente de datos nueva y la actividad es construir un gráfico a partir de ella.

I always like to start with the end result in mind, if you can mentally visualize how you want your dashboard to look, draw a sketch on a piece of paper, then transfer the idea from a raw concept to digital reality. Building reports on open data sources is an excellent option for practicing and gaining inspiration.

Learn from what others are doing, there are more reasons than data accuracy to choose a particular type of visualization. If your point is already conveyed, it may be more valuable to simplify your data to make it more digestible to readers.

Seeking inspiration from others in the field is invaluable for enhancing your data visualization skills. Explore the work of experts like Bas Dohmen and immerse yourself in resources like his YouTube channel "How to Power BI". Analyze different styles, techniques, and approaches to gain insights into what makes a visualization impactful. Additionally, venture beyond the realm of data visualization to find inspiration from diverse sources such as art, design, and nature. This cross-pollination of ideas can spark creativity and innovation in your visualizations.

Creativity flourishes when we seek inspiration from diverse perspectives. Joining communities on LinkedIn, like Dashboard Feedback, or creating challenges, not only sparks innovation but also invites invaluable feedback. Embrace the collaborative spirit, for in the exchange of ideas, true creativity finds its wings.

I Share my visualizations with peers, mentors, or online communities specializing in data visualization, drawing from my experience as a data scientist in NIRSAL PLC. Solicit feedback on my work to identify areas for improvement and learn from others' perspectives. Constructive criticism can help you refine your skills and develop a critical eye for evaluating and improving visualizations. Leveraging my expertise from NIRSAL PLC, engage with the data science community to foster collaborative learning and continuous improvement in data visualization techniques.

No hay una única herramienta que sea mejor a todas las demás. Nada garantiza que vayas a tener que trabajar solamente con un solo entorno o con los mismos requerimientos por parte de tu cliente. En este sentido, es buena idea que tengas una mente abierta y experimentes con nuevas formas de hacer los gráficos. Quizás termines encontrando algo que te gusta aún más o mejores formas de trabajar en tu herramienta actual.

Experimenting with various tools is essential for finding the ones that best suit your needs and preferences. While Power BI may be your personal favorite, it's beneficial to explore other tools like Tableau, Python libraries like Matplotlib and Seaborn, or web-based platforms like D3.js. Each tool has its strengths and limitations, and by experimenting with different options, you can expand your skill set and adapt to different project requirements. Don't be afraid to step out of your comfort zone and embrace new technologies – it's often through experimentation that breakthroughs occur.

Start with the simplest and most straightforward tools first, particularly no-code/low-code. Use these to quickly mock up different types of visuals from the same data set, and decide what's the best way to represent the data. Once you have an idea of what kind of data, trends, and insights are best represented by different kinds of plots, you can put in the time with complex and code-based tools. With these, you have more flexibility on customizing plots and creating custom visuals, but there's a steeper learning curve.

You don't always need heavy-duty tools like Tableau or R, especially if you're creating one-off charts that don't need constant updates. It's worthwhile starting with Excel or Google Sheets and giving Google Looker Studio a shot. They're user-friendly, have a gentle learning curve, and are likely to be quite helpful in getting that chart done.

Some ways to learn more about visualisation : 1. Experiment with different tools and visuals. 2. Start from the basics and grow into it. 3.Make sure the data is aggregated properly. 4. Finally, be creative and play around with different visualisation techniques till you find your groove.

Rather than asking or waiting for feedback, I would like to emphasise the importance of proactively anticipate the feedback itself. That requires good interpersonal skills as data analyst need to be emphatic and always think in 360 manner. Ask yourself questions such as "will the audience get my insights easily?", "are the presentation confusing?" "is this chart important for them?". Data visualisation is all about your selling skills, a tool for data analyst to achieve the goals.

Existen dos niveles de retroalimentación que puedes tener. Uno que está relacionado con la audiencia a la que le presentas tu gráfico y que está centrado con la claridad con la que transmites los mensajes de tu visualización. Debes estar atento a sus necesidades, su contexto, sus intereses puntuales y a la manera en la que acceden a tu gráfico. El otro tiene que ver con el aspecto técnico de la construcción del gráfico. Para esto es importante que documentes la manera como trabajas de alguna manera y que converses con otras personas en el medio. Si eres la única persona que trabaja en visualización de datos en tu organización, las redes sociales y los desafíos de la comunidad son una buena forma de encontrar con quien conversar.

One of the things I realized while presenting your findings to C-level executives is that they seldom care about your research and supporting arguments. They want to hear conclusions then supporting arguments and finally, only if required, your research. Only way to understand what your user wants is to ask for feedback. Bonus: Always ask for input before making a dashboard, ask for the idea they have, ask for what they want to see, ask for specific things they might have(KPIs, aggregation, accumulations) to make sure your thinking aligns with the objectives your end user may have.

Feedback is a crucial component of growth in data visualization. Solicit feedback from peers, mentors, or online communities to gain fresh perspectives and identify areas for improvement. When seeking feedback, consider your audience, purpose, and message – these factors play a significant role in shaping the effectiveness of your visualizations. Embrace constructive criticism and use it as a springboard for refinement. Remember, feedback is not about validation but about continuous improvement and refinement of your craft.

Feedback is incredibly important because it allows us to gauge how well others comprehend our project and assess our success. Through our dashboards, we aim to communicate with people and convey a compelling story. If our narrative isn't clear, our project is at risk of failure. Seeking feedback and understanding others' opinions is the most critical aspect of this process.

The journey of learning in data visualization is ongoing and dynamic. Stay abreast of new developments, techniques, and tools by actively seeking out learning opportunities. Online platforms like Coursera and Udemy offer a wealth of courses and resources tailored to different skill levels and interests. Additionally, attend webinars, conferences, and workshops to stay connected with the latest trends and insights in the field. Cultivate a mindset of curiosity and lifelong learning – it's the key to staying ahead in the ever-evolving landscape of data visualization.

In my experience, Data Visualisation and Music has been one and the same thing. It is a powerful and language-agnostic way of representing essential information. An interesting approach is looking at visualisations and trying to extract as much information from it as possible. While doing so, think critically on the completeness of the information being conveyed and come up with alternative approaches. There is no right and wrong answer unless data is misrepresented or the audience is mislead. One of the best books to get started with data visualisation is “Storytelling with Data” by Cole Nussbaum Knaflic.

The best way to get a knack at this topic is start small and keep learning at a steady pace. Aspirants can start with as basic as types of visualisation and its use cases. Then go ahead to attend a free courses online to further broaden their understanding and if needed do a specialised course (even if paid). At the end of it - nothing beats hands on experience.

Try to join DataViz competitions and get inspiration from great visualizations. This is a great way to be motivated and to getting better at creating meaningful and attractive visualizations. When you make great visualizations, take pride in them and share with your friends and colleagues.

Fa?a conex?es que agregam em sua time-line. Permita que seu feed tenha sempre uma postagem de conteúdo relevante, pra isso, conecte-se com pessoas da área, e se possível, troque experiência com elas. Busque desafios para além do trabalho, estude conceitos e ferramentas. Curiosidade na área de dados é combustível para evolu??o e te manterá sempre atualizado e aprendendo. Sempre que possível pratique, crie seus códigos, seus gráficos, seus projetos, mesmo que seja de poucos dados ou de cases já conhecidos, a título de estudo eles sempre agregaram conhecimento. Conhecimento n?o ocupa espa?o na nossa cabe?a. Bons estudos...

All of the above tips are great. But one more thing I'd add would be to start posting things to a public portfolio. Tableau Public is the one I generally recommend. Going to a site like this helps you to find interesting data sets, often posted as part of a weekly challenge or competition. It's easy to find inspiration by seeing what others are posting. And if you are struggling and need help, there's a built-in community you can turn to. Importantly, you'll have a record of your growth over time. Even your poorest early vizzes help show you how much you've grown, which can be a source of motivation as well. Eventually, this sort of gallery might help you land a job as well. What better way to show a potential employer what you can do!

In addition to the great recommendations by others, I would recommend to read "Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations" by Scott Berinato. In short this book provides these steps as foundation of good chart: Understand, Create, Refine, Present and Practice.

The best data visualizations communicate complex ideas clearly, effectively and with precision. I have found when choosing charts to build tableau data visualizations it is best to try and communicate a story and remove anything that does not support the story I am trying to tell. It is always best to design for comprehension and choose the most effective visualization. Lastly, remember to tell the full story!

From my experience, a lot of people get wowed by flashy dashboards on LinkedIn and immediately want to dive into making their own. But for me, understanding your data is step one and is the most crucial. If you don't get it, you can't really tell a story with it. And trust me, making sure your data is solid, valid and accurate is crucial. You don't need to be a wizard with all the fancy tricks right off the bat. What's important is knowing your data and the story you're trying to tell. Make sure it's clear to your audience. If they can't follow or don't see the point, they'll just tune out. Keep it straightforward and engaging to really draw them in. Finally, make sure to have fun ?????

Familiarízate con las herramientas de visualización de datos más populares, como Tableau, Power BI o Python con librerías como Matplotlib y Seaborn. Además, estudia técnicas de dise?o visual y principios de percepción para crear visualizaciones claras y efectivas. Comparte tus visualizaciones con colegas, mentores o comunidades en línea y solicita retroalimentación constructiva. Esto te ayudará a identificar áreas de mejora y a perfeccionar tus habilidades. La tecnología y las tendencias en visualización de datos evolucionan constantemente. Mantente al día con los últimos avances y experimenta con nuevas técnicas y herramientas para seguir mejorando.