Data Science in Real Life

开始时间: 08/08/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew



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Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: Course cover image by Jonathan Gross. Creative Commons BY-ND

现实生活中的数据科学:您曾经有过完美的数据科学经验吗?数据提取非常顺利。没有合并错误或数据丢失。在分析之前先明确假设。进行随机化以治疗目标患者。在分析之前先概述分析计划,然后严格执行。结论很明确,可行的决定也很明显。这件事发生在你身上吗?当然不是。现实生活中的数据分析是混乱的。如何管理一个面对真实数据分析的团队?在这一为期一周的课程中,我们将理想与现实生活中发生的事情进行了对比。通过对比理想,您将学习关键概念,这些概念将帮助您管理现实生活中的分析。 这是一门重点课程,旨在帮助您快速掌握现实生活中的数据科学。我们的目标是在不牺牲任何基本内容的情况下,为您提供尽可能方便的选择。我们将技术信息放在一边,这样您就可以集中精力管理团队并向前发展。 完成本课程后,您将知道如何: 1,描述“完美”的数据科学经验 2.确定实验设计的优缺点 3.描述提取/组合数据时可能遇到的陷阱,并了解管理数据提取的解决方案。 4.挑战统计建模假设并推动向数据分析师的反馈 5.描述交流数据分析中的常见陷阱 6.瞥见数据分析经理生活中的一天。 该课程将在概念层面上为数据科学家和统计学家的积极管理者进行授课。讨论的一些关键概念包括: 1.实验设计,随机化,A / B测试 2.因果推论,反事实, 3.管理数据质量的策略。 4.偏见和困惑 5.对比机器学习与经典统计推断 课程促销: 课程封面图片由乔纳森·格罗斯(Jonathan Gross)提供。知识共享BY-ND


This course is one module, intended to be taken in one week. Please do the course roughly in the order presented. Each lecture has reading and videos. Except for the introductory lecture, every lecture has a 5 question quiz; get 4 out of 5 or better on the quiz.





Have you ever had the perfect data science experience? The data pull went perfectly. There were no m