You're a data science consultant. How can you visualize data to make your clients' eyes light up?

I normally always 3 factors into consideration when developing a visualisation that can hook my audience. - Define the problem: Understanding the project that needs to be solved is one great way to start. It gives a good understanding of what needs to be done. - Know your Audience: knowing your audience by studying their needs, and how the problem identified influences their decisions. If you can the what, how and why. It will be a great step. - Incorporate a Tale: Writing a story with your visualisation starting from the problem to your audience's needs with your effective solution is an effective way to get them hooked, lighting up and craving to hear more. It's a of matter selling your ideas that come with evidence.

Understanding your data is indeed the cornerstone of effective visualization. Categorical data, like names or labels, finds clarity in bar charts, pie charts, or treemaps, while numerical data, such as quantities or ratios, best conveys insights through line charts, scatter plots, or histograms. Spatial data, including locations or areas, gains meaning from maps, choropleths, or heat maps. Prior to visualization, grasping the nature of your data, the questions it aims to answer, and the intended message ensures the selection of the most suitable visual representation, allowing for clearer, more impactful communication of insights and patterns to your audience.

know your purpose and audience Set the right expectation with customer Select Visualization tech in which you are the most comfortable and don't try to show too much, Keep it simple and focus on 2or 3 top most goals from customer end.

Storytelling .... Your clients brought to you their data because the couldn't figure it out themselves. And if you return back to them with data, no matter what it means they won't get impressesd with that. - Choose your charts wisely - Take care of the naming and labelling - Use supporting texts to give subtitles when the data starts speaking chinese to your clients ;-) - Let everything rest in a nice presentation or dashboard.

The first thing to consider is precisely defining the audience and their needs, and choosing the appropriate visualization tool/chart type for the data and the story in question.

No matter the tool, there are some visualizations that are important to us, data specialists, that should not be used in a business environment, as they only lead to confusion. Focus on scatter, bar and line charts as often as possible, and only bring other kinds if specifically requested. You should also avoid pie charts, and their derivatives, (check the book Storytelling with Data), and from my own experience, avoid "overly statistical" plots, like box plots and heatmaps, you will spend a lot of time trying to explain what they mean...

Selecting the right tool for data visualization is akin to choosing the right brush for a painting. Having worked on diverse projects, I've found Excel's simplicity beneficial for basic charts, while Tableau's interactive features elevate presentations. Python's versatility and rich libraries provide a coding approach, and R's ggplot2 offers expressive visualizations. Tailoring the choice to project needs and team preferences is crucial. Personally, I appreciate the flexibility of Python and Tableau, depending on the complexity and interactivity required, ensuring a seamless blend of functionality and user-friendliness in my visualizations.

In any technical field that involves itself with business-oriented individuals, it is very important to keep things simple and visually appealing. For many people in tech that are used to roles which are less client-facing, this is sometimes a difficult task. However, in the end of the day, you must learn to know your audience and adapt whatever message being conveyed to fit their lens comfortably.

I have good experience with the R programming language. You might check out the following packages: ggolot2 with plotly: Integrates ggplot2 visualizations with interactivity, offering features like zooming and tooltips. Shiny: A framework for building interactive web applications in R, allowing real-time data and visualization interaction. leaflet: Specializes in creating interactive maps, with zoom, pan, and various layers for geographical data. Happy to discuss!

There are plenty of visualisation tools available including Excel, python packages like pandas plotting, matplotlib, seaborn, plotly, cufflinks, R, Tableau, PowerBI, Qlikview. If your data is small and simple you can create fast and good visualization dashboard in Excel using pivot table. If you are working in jupyter notebook then python libraries like matplotlib, seaborn, pandas can be a best choice to rapidly create and decorate multiple plots in notebook itself. For big data and for advanced and dynamic dashboards we can use visualisation tools like Tableau, PowerBI, Qlikview. This are not free tools but allows us to create end to end data analysis and visualisation pipelines.

Best practices for effective data visualization: 1. Choose the right chart type 2. Keep it simple 3. Label clearly 4. Use color effectively 5. Prioritize readability 6. Tell a story 7. Use data ethically 8. Consider your audience

When you embrace the best practices of data visualization, you open the door to a world of possibilities. You can take complex information and turn it into a clear, compelling narrative that resonates with your clients. By using the right charts, graphs, and visual elements, you can breathe life into your data and make it easier to understand. When you focus on clarity, relevance, and aesthetics, you can create something truly engaging and insightful.

To create visually appealing and impactful data visualizations that resonate with clients, it's essential to follow best practices. This involves employing clear and concise design principles, including thoughtful color choices, proper use of labels and legends, and ensuring visual consistency. Prioritizing simplicity and avoiding unnecessary complexity helps prevent information overload and facilitates a more intuitive understanding of the data. Moreover, responsiveness to client feedback and iterative refinement based on their preferences contribute to a personalized and effective visualization strategy.

I"d be delighted to offer consultant in this matter 😀 Here are few points to notice while visualizing data: 1. Storytelling Plots: Use clear, concise plots that tell a compelling data story. 2. Heatmaps and Color Gradients: Highlight patterns and trends with color-coded visuals. 3. Interactive Dashboards: Engage clients with dynamic, user-friendly dashboards. 4. Before-and-After Comparisons: Showcase improvements or changes over time. 5. Impactful Infographics: Summarize key insights visually for quick understanding. 6. Predictive Visuals: Illustrate future trends or outcomes using predictive models. Tailor visuals to client goals, emphasizing clarity and actionable insights.

Adhering to best practices in data visualization is akin to speaking a universal language that resonates with clients. As a consultant, I've found that choosing the right chart type, ensuring clear labels, and using a discerning color palette significantly enhance communication. I prioritize simplicity, avoiding excessive colors or complex visuals that may confuse. Incorporating interactive elements, such as filters and tooltips, adds a dynamic layer to the visualization, fostering engagement and comprehension. Effective data visualization, rooted in these practices, not only conveys insights but also captivates clients, making the data more accessible and impactful.

Try visual representations of the data. It sounds corny but if you are measuring dollars, use stacks of coins or bills to emphasize the distinction. If you are using touchdowns, try footballs. Numbers don't always register with people as well as proportions.

When choosing visualization tools, start with those you're comfortable with, such as Microsoft Excel or Google Sheets for basic charts, or Tableau Public for more interactive visualizations. Consider Python with Matplotlib and Seaborn or R with ggplot2 if you prefer coding. The key is to begin with tools aligned with your needs, gradually expanding your skills through hands-on projects. Remember, practice is key, and as you work on real-world scenarios, you'll become more proficient and confident in data visualization.

For similar information use similar plots and don't unnecessarily use diverse graphs. While explaining data visualisation, use only contextual storytelling which can make impactful presentation. Make graphs which allow audiance to predict for future. Keep it simple, keep it real, keep it meaningful !

Here are key points to enhance data data visualization and storytelling: 1. Bar Charts: Effective for comparing categorical data and showing relative values. 2. Line Charts: Ideal for illustrating trends over time or continuous variables. 3. Scatter Plots: Showcase relationships between two variables. 4. Heatmaps: Visualize patterns, comparisons, or distributions across multiple variables. 5. Treemaps: Depict proportions of categories within a larger whole. 6. Geographic Maps: Highlight spatial data and regional variations. 7. Data Animations: Provide dynamic and engaging presentations of data over time. 8. Storytelling and Interactive Dashboards: Present multiple visualizations to tell a comprehensive data story.

It very important to explain the right context of the visualization. Apart from use simple and familiar labels with an corporative colours, with different type of graphs like bar, plot... Finally it is very important to print it user friendly, keeping it simple.