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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-tableau-level-of-detail-expressions-tableau-lod/
课程评论：没有评论
**Coursera课程总结：Tableau高级应用 - LOD表达式** 本课程专为有一定Tableau使用基础，但在处理涉及不同数据聚合层级、需要跨层级比较，或希望在特定维度上应用过滤条件的分析师设计。课程旨在帮助学习者掌握Tableau中强大的Level of Detail (LOD)表达式，解决日常分析中遇到的常见难题。 **课程内容概要：** * **解决痛点：** 课程指出，传统的Tableau分析方法在处理诸如“将单个类别与整个表进行比较”、“指定字段过滤”、“追踪自定义用户群组行为”等复杂分析场景时会遇到瓶颈，学习者常会遇到“不能混合聚合和非聚合值”的错误。 * **LOD表达式的重要性：** LOD表达式是Tableau中一个关键的工具集，能够帮助分析师突破视图层级的限制，解决诸如用户分组分析（Cohort Analysis）、留存分析（Retention Analysis）、按维度分组聚合（Binning Aggregates by Dimensions）等经典分析问题，以及执行特殊过滤（如比例刷选 Proportional Brushing、相对比较 Relative Comparisons）。 * **核心技术：** 课程将深入讲解FIXED, INCLUDE, EXCLUDE这三种LOD表达式的理论基础和实际应用。学习者将从基础概念入手，循序渐进地学习更高级的技巧。 * **学习目标：** 完成课程后，学习者将能够熟练运用LOD表达式解决上述各类复杂的分析挑战，并能根据具体需求创造性地提出解决方案。课程旨在提升学习者的Tableau分析能力，拓宽其工具箱，使其能够在工作中提供更具洞察力的分析和创意。 **适合人群：** * 希望在Tableau中处理跨层级数据分析的Tableau用户。 * 在数据分析过程中经常遇到聚合与非聚合值混合问题的学习者。 * 对FIXED, INCLUDE, EXCLUDE表达式感到困惑，希望明确其用法和适用场景的学习者。 * 希望提升Tableau分析技能，成为数据分析领域更有价值贡献者的人。
Have you ever had analytical questions that are easy to ask, but surprisingly hard to answer with regular analytical tools? Do you often find yourself asking questions involving different data layers? Like comparing a single category to a whole table; or applying filters on particular fields; or tracking the behaviour of custom cohorts over time - just to mention a few classic examples. Do you want to know how to compare data aggregated at different levels of granularity? Do you often bump into the error message: 'Cannot mix aggregate and non-aggregate values'? Do Tableau terms FIXED, INCLUDE or EXCLUDE confuse you? Are you struggling choosing the right one for particular tasks? Do you want to step up your daily analytical game and gain new, useful skills? If you are a passionate Tableau user and you can associate yourself with one or more of the questions above, then this course is for you. Scenarios like the ones mentioned above occur on a daily basis, and they can cause quite a bit of headache for the analyst. Tableau has many great tools and functions including table calculations that make everyday life easier for the data scientist. One strong point of the software is its responsiveness. Plotting measures against variables has never been easier: each change to the shelves is instantly and automatically applied on the view - a great environment to interact with the data. This strong point, however, can easily be turned into a weakness, if you want your analysis to step out of the borders of the view level of detail. In Tableau, to solve classic analytical problems (such as cohort analysis, retention analysis or binning aggregates by dimensions), or to proceed with special filtering scenarios (like proportional brushing or relative comparisons) you need to be familiar with a special tool set called the level of detail (LOD) expressions. In this course, you will learn about the general mechanics of LOD expressions both in theory and practice. We start from the very basics and then we proceed to more advanced techniques in a stepwise manner. If you are not familiar with the concept of LOD expressions yet, but you are already a Tableau user, then taking this course will most probably improve your analytical skills and broaden your tool set. After completing this course, you will be able to solve all above mentioned analytical challenges and even more, because LOD expressions let the analyst come up with creative solutions for custom scenarios. Instead of asking the questions you can be the one in the office who always has a practical answer or a constructive idea. Take a look at the content of this course, and I bet you won't regret it.
