You need to visualize data for a project. What tools can help you do it quickly and effectively?

When thinking of data visualization tools, the first that comes to mind are Python libraries like Matplotlib and Seaborn. They offer a range of options such as bar graphs, scatter plots, histograms, and more. Power BI and Tableau are also excellent choices for data visualization. For beginners, Excel is a great starting point with plenty of options to become familiar with data and visualizations. R provides a high level of customization and excels in creating complex visualizations.

Understanding data is one of the important activity and by doing that we can then realise the insights we can built on top of it. It's similar to how we evaluate the land, soil and perform structural analysis for building a state of the art edifice. While the end user may be interested in facade the building going to have, the core strength lies based on the outcome of the structural analysis performed.

Algo que puede ser útil, incluso antes de arrancar con el análisis exploratorio de datos, es tener claro cuál es la pregunta que quisieras resolver con la visualización de datos. Esto te ayudará a identificar qué tipo de datos necesitas para responder a esa pregunta. Numéricos, Categóricos, Booleanos, etc. Esto es algo en lo que vas a mejorar a medida que desarrolles más gráficos y que vas a hacer de manera intuitiva.

- Tableau and Google Data Studio are popular tools for visualizing data quickly and efficiently. - Python libraries like Matplotlib and Seaborn provide more advanced options for visualizing and customizing data. - Excel has built-in charting and graphing capabilities. - It's important to understand your data and determine the best way to present it visually. - With the right tools and a solid understanding of your data, you can create compelling visualizations that effectively communicate your insights to others.

Identifying the problem, you intend to address and establishing the sources of your data are critical initial steps. After collecting the data, it's essential to clean it by eliminating any duplicates, filling in missing values, or correcting mistakes. Subsequently, the data should undergo a quality assurance process to ensure its applicability and accuracy. Necessary transformations should be applied to the data to prepare it for analysis. Once these steps are completed, the data can then be analyzed and visualized effectively.

Something to consider when choosing your tool for data visualisation is how your user will be engaging with it, not just in terms of the specific visualisation but also in terms of the wider data pipeline. Some tools will connect with certain databases better than others - Power Bi is well inter-grated with Microsoft products, whilst Tableau is better suited than Power Bi to work alongside Google Cloud Platforms. And - does your user/client want to track engagement with reports? Tableau server offers easily accessible data on how users interact with reports which is a great feature to think about. These are some of the things to think about and discuss with your audience.

Es muy importante hacerse preguntas sobre la audiencia de tu visualización de datos. ?Cuáles son sus intereses? ?Cuál es su contexto social, económico y cultural? ?Qué tanta familiaridad tienen con los tipos de gráfico que puedes usar? ?Qué sensibilidades puntuales tienen frente al uso de colores, formas, lenguaje? Después de todo, no hay que olvidar que uno de los objetivos de la creación de visualizaciones de datos es otorgar información útil para la toma de decisiones. Este tipo de preguntas te acercarán a gráficos que cumplan este propósito.

When visualizing data, it is important to know who your target audience is. Creating visualizations specifically for each group - from specialists to decision-makers, guarantees insightful information. While specialists look for specific performance enhancements, decision-makers prefer outcomes to be displayed in simple, straightforward graphics without the use of technical jargon.

Before choosing any it is very import to understand t which kind of the data & also the data volume like the data that you are dealing with requires minimal cleaning & also the data volume is small then in that "Tableau" & "Tableau Prep builder" can be used but say if the data requires extensive amount of cleaning then in that case "Alteryx" & "Tableau" just to name a few, selecting up a tool is also highly governed by your target audience & also what they are accustom to like it is not advisable to ask your target audience to switch from say "Tableau" to "Power BI" just because the later offer more a variety of data cleaning features we have to consider user adaptability & also the business case for which the tool is used.

Just use Chat-gpt Or any other genAI tool to get a quick understanding of data types and applicable graphs Without any Gen AI, here is how you can excel: 1. Know your data - what type is your data? Numeric, boolean etc etc 2. Study different graphs and charts available 3. Know your target audience - Are they well versed with the domain acumen, if not, do not be afraid to use a simple language 4. Use a powerful BI tool to represent your data 5. Story boarding : communicating your findings and insights is extremely needed to convey what does your data say?

When selecting a data visualization tool, it's important to consider your target audience. Remember that effective data visualization should be able to convey complex information in a simple and understandable manner, regardless of the viewer's technical proficiency. Depending on your needs and the technical skills of your users, you may choose to work with Python or R for data preprocessing and visualization. Alternatively, Power BI or Tableau may be more suitable for your needs. In some cases, Excel may also be a viable option. Ultimately, the tool you choose should align with your objectives and the needs of your audience.

Todas las herramientas de visualización de datos tienen ventajas y desventajas en varias dimensiones: precio –gratis o paga–, facilidad de uso –interfaz gráfica o código–, escala –1 gráfico rápido pero muchos lento–, etc. En este sentido, no hay una única herramienta que funcione mejor que todas las demás. Debes ajustarla a tu necesidad puntual, así como a los requerimientos de tu cliente y tus stakeholders.

Check if the visualization tool seamlessly integrates with other tools or platforms you are using for data analysis, storage, or presentation. Integrations can streamline your workflow and enhance the overall efficiency of the project.

To effectively communicate insights to an audience, selecting the appropriate data visualisation tool is crucial. Because of its smooth interaction with Excel, Python, SQL, Powerpoint, and Azure (deployment), Power BI is my preferred option. Along with supporting a wide variety of data types, the tool should be interactive and user friendly. Make sure that everyone in your organization can access and share your report.

Before building visualisation in the tool, what I would suggest is to create a wireframe of the theme and design as a concept and get it approved by the stakeholders as it will reduce a lot of re-work and also sets the expectation. There are lot of tools available for the same like Figma and Canva where the wireframes and components can be built and viewed.

Para esta tarea puede ser útil revisar los mapeos que propone el concepto de la gramática de los gráficos. Si conoces ggplot2, seguro tienes familiaridad con este concepto. Estos mapeos son: - Capas: La manera en la que vas a pintar la información en el gráfico (p.e. puntos, líneas, rectángulos). - Escalas: La especificación de cómo se muestran esas capas (p.e. color, forma, tama?o). - Coordenadas: Las reglas con las que se ubican las capas en el gráfico. - Facetas: Si quieres hacer más de un gráfico en tu producto final bajo la idea de los peque?os múltiplos. - Tema: El conjunto de características estéticas que acompa?an el gráfico como el color de fondo, el tipo de letra de los títulos, etc.

La retroalimentación frente a tu gráfico es fundamental, porque a veces puedes quedarte en el detalle de la elaboración del gráfico y olvidar la audiencia a la que se lo quieres presentar. Una idea es mostrárselo a algunas personas antes de publicarlo, de tal manera que puedas identificar si hay ambigüedades en el mensaje que quieres enviar, problemas para su lectura en distintos dispositivos, errores de ortografía, etc.

Testing can be done through two main ways: getting user feedback and empathy. User feedback is an obvious way, but it is also a back and forth process which can be time consuming. Empathizing with the final visualization user helps preemptively understand what they need and how they need it, thus also helping the creator arrange the visualization a way which will naturally be impactful to the user.

Selecting the visuals is a major part of the Business intelligence process and as data grows and story we might have to say changes, the visuals also should be refined accordingly. The column chart and pie chart created with the same data can create different impacts with the users based on the story and arrangement of visuals. So it's good to review the report on a recurrent basis and refine it whenever the current visual is not solving the purpose.

While visualizing data is crucial, assembling it into a storyboard or dashboard is equally vital. Presenting a narrative within the visualized data enhances the analysis's value.

trying to deliver a finished product quickly will most likely create more confusion. if you want to something quick, start with a minimum required dataset. as an example one month worth of data, or data focusing on one location/category. this is important as it will help validating your data for consistancy. then i would do everything in excel. i will revalidate the visuals are showing what they are supposed to show. if there is time, i will attempt to create in my preferred application. if it is presented in excel, i would mention this is in the early stages of development and something more robust is being worked on. regardless of how quickly a report/visual is required, validation of the data source and its output is very important.