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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-synthesis-in-ux-research/
课程评论：没有评论
课程名称：如何理解用户研究数据？ 课程概述：尽管同理心和以用户为中心是设计思维和用户体验设计的核心，但用户研究或用户体验研究的必要性常常因各种原因受到争议，例如时间紧迫或急于推动某个“想法”。如果我们决定在产品或服务开发之前进行用户体验研究，如何确保该活动的有效性？我们如何知道它能够产生最佳结果和可操作的见解？一些需要思考的要点包括：我们是否与合适的用户进行了沟通？所有信息和数据是否与研究目标相关？我们如何理解从访谈中获得的大量原始数据？哪些见解对利益相关者有用且相关？ 课程内容：在当今数据驱动的世界中，从多种来源综合数据已成为数据分析师、研究人员和决策者的一项基本技能。本在线课程旨在提供学习者对数据综合重要性的理解，以及在这一过程中使用的技术、最佳实践和工具。学生将学习进行数据综合的最佳实践和基本原则，以帮助提炼有效的可操作见解。学生将能够理解并应用不同的方法和框架，以帮助策划清晰的见解，并提供适当的验证和证据来支持发现。 课程结束时，学生将清楚理解数据综合的必要性和重要性，以及在这一过程中使用的最佳实践。他们将掌握进行严格和全面数据综合的技能，成为数据驱动世界中的有价值资产。
Even though empathy and user centricity has been the crux of Design Thinking and UX Design, the need for user research or UX research has often been debated upon for a variety of reasons, like lack of time or just wanting to push through an ‘idea' quickly.And let us say, we do decide to conduct UX research before product/ service development. How do we ensure the effectiveness of such activity? How do we know if it will yield the best results and actionable insights?Some pointers to think about-Have we spoken to the right set of users?Are all information and data relevant to the research goals?How do we make sense of a large pool of raw data from the interviews?What insights are useful and relevant to the stakeholdersAbout course:In today's data-driven world, synthesizing data from diverse sources has become an essential skill for data analysts, researchers, and decision-makers. This online course on Data Synthesis aims to provide learners with an understanding of the importance of data synthesis and the techniques, best practices, and tools used in the process.Students will learn the best practices and thumb rules in conducting data synthesis, to help derive effective actionable insights. Students will be able to understand and apply different methods and frameworks to help curate clear insights with appropriate validations and evidence to support the findings.By the end of the course, students will have a clear understanding of the need and importance of data synthesis, best practices used in the process. They will be equipped with the skills to conduct rigorous and comprehensive data synthesis, making them valuable assets in the data-driven world.
