August 2022
Beginner to intermediate
463 pages
9h 22m
Chinese
Content preview from Tableau Prep即学即用
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｜
389
决定在何处准备数据
特定的可视化风格需要不同的数据结构），但大多数的数据集都有一个相对标准的
分析设置计划。
这就使得清理（包括计算）和汇总这两个过程可能适合在数据准备工具或可视化工
具中进行。对于小型数据集和简单的计算，哪种方法是“最好的”就不是那么明确了。
然而，随着数据集的规模或计算的复杂性的增加，你在这里的决策可能会决定你的
组织是否能成功地利用数据。
43.2
数据准备
vs
可视化分析
当你在评估使用哪种工具来完成每项任务时，一个关键的考虑因素是平衡敏捷性和
功能性。如果你采取在
Prep
这样的工具中单独准备数据的方式，就会牺牲其敏捷性，
并失去了让每个用户单独做这件事的选择。然而，这种灵活性的降低实际上可能是
有用的，因为它有可能防止错误、优化性能，而且使得某些难以完成的任务变得可能。
需要考虑在每个工具中应该完成哪些任务，以帮助你分配数据准备工作。要确定如
何分配工作，你得评估你的组织在某些因素上的复杂程度。
43.2.1
数据素养
数据素养（或者说一个人对图形、结果等数据产品的理解程度）是决定你将在哪里
进行数据清理和汇总的关键因素。让数据易于操作固然重要，但确保数据集得出的
答案的准确性更为重要。如果你的同行不具备承担这些任务的数据素养，则需要在
向他们提供数据之前完成这项工作。这意味着你需要了解和
/
或预测那些用户想要
回答的问题，并据此准备数据集。
43.2.2
组织规模
拥有一个能胜任并能完成任务的团队是一项重要的资产，同时还需要评估在整个组
织中多次重复这些任务所需的工作量。如果只要求一个人完成单次任务 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.