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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-audit-methodology-for-analytics-practitioners/
课程评论：没有评论
**课程名称：** 面向分析从业者的数据审计方法 **课程概述：** 无论您是统计学、数据科学、机器学习还是其他领域的分析从业者，您都曾多次因数据质量问题感到沮丧。即使是未来的从业者，也将会面临同样的问题。世界上没有分析师可以完全避免处理数据问题。 然而，我们的非分析领域的客户和同事常常期望我们能够解决数据质量问题，仅仅因为我们是专业的数据工作者。这种期望的差异常常导致冲突，并且难以得到解决。 有时我们很幸运，但更多时候，我们在分析过程的后期才发现数据问题，这会导致项目延误甚至取消。这会加剧摩擦，并影响我们与客户和同事的工作关系。 本课程将为您提供一套实用的方法论，以应对这一持续的挑战。您将学会作为一名合格的分析从业者，能够负责和处理哪些与数据质量相关的问题。 **目标受众：** 本课程适合当前和未来的数据领域技术专业人士，包括但不限于分析、统计、数据科学、商业智能、数据工程、机器学习和人工智能等领域的从业者。
If you are an analytics practitioner, be it in statistics, data science, machine learning, and so on, you've experienced frustration with data quality, not just once, but multiple times. If you are an aspiring analytics practitioner, know that you will experience this frustration. There is not a single analytics practitioner in the world who can escape from dealing with data problems.We find so many problems with the quality of the data. However, our non-analytics clients and colleagues often expect us to be able to resolve data quality issues just because we work with data as professionals. This difference in expectations often leads to conflict with little resolution.Although sometimes we are lucky, we often find the data problems well into the analysis process, causing project delays or even project cancellations. This worsens the friction and impacts our working relationships with clients and colleagues.This course prepares you with a practical methodology to address this ever-challenging topic with what we can do as analytics practitioners with things we can and should be responsible for, as competent professionals.This course is intended for current and aspiring technical professionals in data, including analytics, statistics, data science, business intelligence, data engineering, machine learning, and artificial intelligence, among others.
