Visualizations can answer questions for you.ĭata visualizations will allow your audience to have a visual understanding of the steps you took and your findings. When presenting your findings, don’t limit yourself and put yourself in a rut where you have to answer 1000 questions. Some can read a textbook once and get it. ![]() You will do more harm by losing your audience's attention to technical words.ĭifferent people learn in different ways. If there is no way you can replace a specific data science term, there is no harm in explaining what it means. ![]() Translate your data science terminology in a language that everybody can understand. Think about how a teacher explains a topic to a student, and keep that at the front of your mind when you’re explaining to your audience. For example, F1 score or cross-validation. Therefore, some of the terminology used in your everyday team will be foreign to them. Therefore, once you have your findings, you will need to cater to a variety of people - and mastering how to do that can be difficult, but it can be achieved.Īs a data scientist myself, I understand that a lot of stakeholders or managers will not come from a technical background. ![]() Not only will you come across non-technical people, but you may be dealing with someone who prefers explanations through visualizations, or project run-throughs. If you are a technical person, it can be challenging to convey your message to non-technical people. As a data professional, it can be difficult for non-technical people to understand technical language. Once you have found your valuable insights, if it’s trends, patterns, or put into visualizations - you will need to be able to explain these.
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