|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/real-life-data-science
课程评论：没有评论
课程名称：现实生活中的数据科学 概述：你是否曾经历过完美的数据科学体验？数据提取顺利完成，没有合并错误或缺失数据，假设在分析之前清晰界定，进行了随机化处理，分析计划在分析之前制定并严格遵循，结论明确且行动决策显而易见。然而，现实生活中的数据分析往往是混乱的。如何管理面对真实数据分析的团队？在为期一周的课程中，我们将理想与现实进行对比，通过这个过程，你将学习到管理真实数据分析的关键概念。 本课程旨在帮助你快速掌握现实生活中的数据科学。我们的目标是尽可能方便你，同时不牺牲任何重要内容。我们省略了技术细节，以便你能够专注于管理团队并推动其发展。 完成本课程后，你将能够： 1. 描述“完美”的数据科学体验 2. 识别实验设计中的优缺点 3. 描述提取/整理数据时可能遇到的问题，并学习管理数据提取的解决方案 4. 挑战统计建模的假设，并向数据分析师提供反馈 5. 描述在传达数据分析时的常见陷阱 6. 了解数据分析经理的日常工作 课程的授课层面为概念性，面向数据科学家和统计学家的管理者。课程讨论的一些关键概念包括： 1. 实验设计、随机化、A/B 测试 2. 因果推断、反事实 3. 管理数据质量的策略 4. 偏倚和混淆 5. 对比机器学习与经典统计推断 课程宣传链接：[课程视频](https://www.youtube.com/watch?v=9BIYmw5wnBI) 课程封面图片由 Jonathan Gross 提供，使用 Creative Commons BY-ND 授权，链接：[图片来源](https://flic.kr/p/q1vudb) 课程大纲： - 部分：1 - 标题：介绍，完美的数据科学体验 - 描述：本课程为一个模块，计划在一周内完成。请按照所提供的顺序进行学习。每节课都有阅读材料和视频，除了介绍性讲座，每节课都有5道题的测验；在测验中获得4分或更好的成绩。
Part: 1
Title:Introduction, the perfect data science experience
Description: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 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: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb
