What do you do if your data science findings are too complex for a layperson to understand?

If data science findings are too complex for a layperson to understand, it's important to tailor the communication to the audience's level of expertise and familiarity with technical concepts. This may involve simplifying complex terminology, using visual aids such as charts or infographics to illustrate key points, and providing relatable examples or analogies to clarify abstract concepts. For instance, when presenting findings on customer segmentation to a non-technical audience in a retail company, focus on explaining the practical implications of the segments (e.g., high-spending customers versus occasional buyers) rather than delving into the technical details of the clustering algorithm used.

When data science findings are too complex for a layperson to grasp, I focus on simplifying the explanations, using analogies or visual aids ??, and emphasizing the practical implications to make the insights more accessible and actionable.

Many people understand through graphs or visuals... Sometimes the tech and math terms are abstract for non IT people, if we vulgarise them without a graph or a visual, they won't get it. An image synthetises a lot of wording, thus people will arrive to a better understanding of that problem and its complexity behind it.

Aquí podría llevar este punto a 4 ejes principales: Evita Jerga Excesiva: La sobreutilización de jerga y términos técnicos puede confundir a la audiencia. Simplifica tu lenguaje para hacerlo más accesible. Lenguaje Sencillo: Utiliza palabras y frases simples en inglés que la audiencia pueda entender fácilmente. Evita complicaciones innecesarias. Definiciones Claras: Define los términos esenciales para tu mensaje. Asegúrate de que tu audiencia comprenda los conceptos clave. Ejemplos y Analogías: Ilustra tus puntos con ejemplos, analogías y metáforas. Facilita la comprensión mediante comparaciones con situaciones cotidianas.

Clarity over Clutter: Avoid overloading your charts with too much data or using complex visualizations that require a data science degree to decipher. Keep it simple and highlight the key trends. Choose Wisely: There are many types of charts and graphs - bar charts for comparisons, line charts for trends, etc. Select the one that best conveys the specific insight you want to showcase. Label it Up: Don't assume your audience understands every element. Use clear labels, titles, legends, and annotations to guide them through the visualization.

Storytelling is essential for data scientists who want to make an impact. To engage your audience and drive action, turn complex findings into compelling narratives. Start with a strong hook, use relatable language and visuals, and finish with a clear takeaway. Imagine explaining a neural network with complex technical parameters (eyes glaze over!). Instead, craft a narrative! "Our deep learning model identified patterns in customer data, like hovering over a product image multiple times. This lets us predict buying habits with 40% higher accuracy, which means..." By connecting with your audience and emphasizing the value and implications of your findings, you can inspire and inform with your data science stories.

Así como cualquier buena historia tiene una introducción, un nudo o desarrollo y un desenlace, debemos utilizar este recurso para mostrar nuestros hallazgos. Partir de lo general para dar un marco, mostrando números globales para dar contexto y situar a la audiencia. A partir de allí empezar a mostrar detalles, segmentar la información utilizando nuestras principales variables puede servir para identificar grupos con comportamientos diferentes a los demás. Finalmente podemos volver a mencionar de forma resumida a los principales hallazgos y cómo tomar acción para obtener los mejores resultados.

Using storytelling simplifies complex data for non-experts. It engages them emotionally, provides context, simplifies information, makes it memorable, and persuades by connecting data to relatable narratives. It's a powerful way to ensure understanding and encourage action.

When it comes to seeking feedback on your data science findings, consider it a vital step in enhancing your communication strategy. Feedback serves a valuable tool for refining your presentation, uncovering potential gaps, and ensuring that your message resonates effectively with your audience. To gather constructive feedback, reach out to diverse set of experts. Their insights can offer varied perspectives and valuable input on different aspects of your presentation. Remember, feedback is not just about validation; it's dynamic process that contributes to your growth as a communicator. Embrace it as an opportunity to refine your findings and ensure that your message resonates effectively with your intended audience.

A perfect story telling would help. Communication skill is required in Data science to connect with your audience from Start to end. This will help you to make a project success. We cannot expect from our audience to understand technical complexity and standard data science terms. Knowing your audience would help you, however it is our task to ask right questions, and use visualization in an effective way.

You pay the layperson for his education/course so he too can spend few years in school, lose his ability to speak in plain terms and converse in complex terms like you :)