What are the most important tools for data visualization?

A big part of the job, maybe bigger than most people want to admit, is to communicate complex technical ideas to people on a limited time budget that are not at the same technical depth as you - management, POs, stakeholders, customers, junior colleagues, etc. That's where you abstract the hard data into visualizations to paint a picture so to speak. To sell ideas as digestible patterns without the need to explain complicated technical jargon. To demonstrate instead of describing.

Visualization is crucial in making data consumable for humans in the first place. In many applications such as medical imaging or engineering simulations, it not possible to gain any insight without visualizing the data.

L'analyse des données est très importante pour comprendre et connaitre son public. Ajouté de la mise en formes permet de rendre accessible et lisible les données

Machines need an alphanumerical symbols to interpret information. But in the reality, in the universe, on Earth and everywhere around us everything is visually represented. Humans, along with many life forms, comprehend, perceive and interact with the world based on it's visual aspects. Data visualization is the natural way of experiencing the reality.

Data visualization plays a crucial role for presenting complex data clearly and understandably, facilitating communication, identifying trends, aiding decision-making, and storytelling. It enhances memorability, encourages exploration, and aids collaboration among diverse teams. By transforming raw data into visually engaging representations, data visualization enables individuals and organizations to derive valuable insights into their businesses and help them make informed decisions and drive innovation effectively.

There are multiple tools available in the market like Tableau, Power BI , looker, Amazon Insights,cognos,Qlikview and even excel can be visualisation tool. It all depends on the volume of data and budget of the organisation. In my perspective Tableau and Power BI are the best all-rounder tools which can handle the huge volume of data set and give better user experience.

Good data visualization should?communicate a data set clearly and effectively by using graphics. The best visualizations make it easy to comprehend data at a glance. They take complex information and break it down in a way that makes it simple for the target audience to understand and on which to base their decisions.

Clear Purpose: Clearly define the purpose of the visualization. What message or insight are you trying to convey? Simplicity: Avoid unnecessary complexity. Eliminate clutter and present only the essential information. Relevance to Audience: Know Your Audience: Tailor the visualization to your target audience. Consider their level of expertise and familiarity with the subject matter. Appropriate Visualization Type: Choose the Right Chart: Select a visualization type that best represents the data. Bar charts, line charts, pie charts, and scatter plots are examples of common types. Color Meaningfully: Use color to highlight important information or group data. Avoid using too many colors that might confuse the audience.

One thing I've found helpful is to think of data visualization like storytelling. Just like a good story, your visualization should have a clear message, be easy to follow, and engage your audience. So, choose a chart that tells your data's 'story' in the simplest way possible. Use colors and sizes to highlight key points, and always remember that your audience needs to 'read' the visual story you are telling without getting overwhelmed by too much information.

A good data visualization is one that is clear, intuitive, and tailored to its audience. It should accurately represent the underlying data without distorting the truth. Effective visualizations are often simple and avoid unnecessary complications that could confuse the viewer. They use appropriate colors, scales, and labels to enhance readability and understanding, making complex information accessible at a glance.

In my experience as a data engineer, to learn data visualization effectively, start by grasping the fundamental principles and theories behind it. Familiarize yourself with various tools such as Tableau and Python libraries like Matplotlib. Practice by working with different datasets, experimenting with visualization types, and analyzing existing examples. Engage with online courses, tutorials, and communities to deepen your understanding and gain practical experience. As you progress, work on real projects to apply your skills and stay updated with the latest trends and techniques. Continuous skillful learning and experimentation are key to mastering data visualization and effectively communicating insights from data.

To effectively learn data visualization, consider it as learning a new language where practice and constructive feedback are your cornerstones. Engage in hands-on practice with accessible tools such as Google Sheets and Tableau, akin to practicing conversational skills in a new language. For theoretical grounding and advanced techniques, refer to resources like "Data Visualization for All," a free online course, or "Data Visualization: A Practical Introduction," a book that offers a structured approach to mastering data visualization using R and ggplot2. Moreover, "Storytelling with Data" is both a book and a blog that can act as your guide, teaching you how to communicate effectively through your charts and graphs.

To learn data visualization, dive into online resources and practice creating charts and graphs. Start with simple tools you're familiar with, like spreadsheets, and gradually work up to more advanced software. Books and blogs on the topic can offer valuable tips and examples. Practice regularly and seek out feedback to refine your skills. It's all about turning numbers into clear, visual stories that anyone can understand.

Learn from YT and try to download a public datasets like from kaggle.com. From there you can try code from the dataset given, example by using Python for data visualization. I've usually try different visualization while using Python libraries like seaborn and Matplotlib, there's also a documentation for both of libraries and if you ever feel stuck hey ChatGPT can help you solve it step by step!. Also take a free online course to level up your skills and show off your certificates.??

Learning data visualization involves a combination of studying principles of design, understanding data analysis, and practicing with visualization tools. Start by familiarizing yourself with the basics of graphic design and statistical analysis. Then, experiment with different data visualization software and tools, using real datasets to create visuals. Online courses, tutorials, and community forums are great resources to learn techniques and best practices in data visualization.

It depends on what kind of data we use for big data power bi and Tablue will be good, For medium and small data excel also google stude

This very much depends on the application. You have excel right there at your fingertips. You may want to deploy prometheus, grafana, or integrate datadog for more complex fully automated solutions. You may want to invest into developing stuff that suits your own special needs. There is a whole field of study around system observability and SCADA that you might want to explore.

For me that most important data visualization tool is Excel and PowerPoint and it really help us creating data visuals for our clients, and it doesn't need any kind of programming. For data visualization, from large data people might be going for other tools, but for us we really never find a need for such tools

The most popular would be Microsoft Excel or Google Sheets these are older apps in the data viz world but have very robust features

Tableau: Tableau is a popular and powerful data visualization tool that allows users to create interactive and shareable dashboards. It supports a wide range of data sources and offers intuitive drag-and-drop functionality. Microsoft Power BI: Power BI is a business analytics tool that enables users to visualize and share insights across an organization. It integrates with various data sources and provides features like data exploration, modeling, and interactive dashboards. Google Data Studio: This is a free tool from Google that allows users to create interactive reports and dashboards. It integrates with various Google services and other data sources.

Choosing the best tool for data visualization depends on understanding your objectives, data complexity, technical proficiency, and preferences. Begin with assessing your data characteristics and technical skills to determine whether you need a code-based solution like Python libraries or a user-friendly platform such as Tableau. Consider factors like interactivity, compatibility, cost and long-term viability when making your decision. Seek feedback from other users, test different tools through trials or demos and prioritize those that align closely with your needs and workflow. By evaluation, you can select a tool that helps you to create compelling visualizations and extract meaningful insights from your data.

ggplot2 (R): ggplot2 is a data visualization package in the R programming language. It follows the grammar of graphics concept and is highly customizable. QlikView/Qlik Sense: Qlik is a business intelligence platform that allows users to create interactive dashboards and reports. Qlik Sense is the newer version that focuses on self-service analytics. Excel: Microsoft Excel has robust charting and graphing capabilities, making it a widely used tool for basic data visualization needs. Plotly: Plotly is a Python graphing library that can be used for creating interactive plots. It supports various programming languages, including Python, R, and Julia.

It depends, you can start with Tableau or PowerBI first. Search more reference or watch tutorial from YT videos also helps you to decide what tools you want to make sure your data visualization fits your objectives, goals, etc

Choosing the best tool for your data visualization depends on several factors including the complexity of your data, the level of customization required, your technical proficiency, and your budget. Start by defining your objectives and the types of visualizations you need. Consider the data sources you'll be using and evaluate each tool's compatibility, ease of use, and scalability. Trying out free trials or demo versions can also help in making an informed decision.

Exercez-vous à décomposer mentalement les différentes catégories de données pour n'importe quel sujet, comme si vous deviez vous préparer à les mettre en forme. Posez vous aussi souvent des questions concernant la volonté, le message que vous voulez faire passer et sélectionner les données qui seraient les plus pertinentes, car toutes les données ne le sont pas forcément selon le message et vous risquerez de perdre le sens de ce que vous voulez mettre en avant en voulant en utiliser trop.

N'hésitez pas à être curieux et tenter des associations de données qui n'ont pas été exploré. Vous verrez que parfois cela ne mène pas à quelque chose de pertinent mais cela vous entraine tout de même à muscler votre esprit critique lorsque vous rencontrerez des visualisations de données sur les réseaux sociaux, dans des articles ou à la TV.