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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-wrangling-analysis-abtesting
课程评论：没有评论
课程名称：使用SQL进行数据清洗、分析和AB测试 课程概述：本课程旨在让学员将“数据科学中的SQL”所教授的技能应用于四个逐渐复杂和真实的数据科学案例研究中。学员将学习如何将各种类型的时间戳转换为统一格式并进行日期/时间计算。课程将涵盖如何选择并执行最优JOIN以满足数据科学查询需求，以及如何在分析数据集中清洗数据，包括去重、质量检查、回填和处理空值。学员还将学习如何使用窗口函数对数据进行分段和分析，并运用CASE语句执行条件逻辑以应对数据科学查询。此外，课程将介绍如何将查询转换为定时任务，以及如何将数据插入日期分区。最后，针对预测分析需求，学员将利用在课程中获得的工具和技能，从原始数据中构建特征。通过这些技能的实际应用，学员将获得进行AB测试分析的框架。 课程大纲： 1. **未知质量的数据** - 学员将能够创建可信赖的分析，并识别不可靠的数据和缺失数据的原因，通过定义新指标解决模糊的问题。 2. **创建干净的数据集** - 学员将能够命名主要的数据类型，解释如何将未过滤数据转换为可以进行数据分析的表，并讨论数据仓库与生产数据库的区别，使用工具创建可靠的表格。 3. **SQL问题解决** - 学员将能够映射JOIN的过程，突出不同问题所需的详细程度，练习回答数据问题，发展解决模糊或不清晰问题的策略。 4. **案例研究：AB测试** - 学员将利用SQL技能建立基本的AB测试系统，应用假设检验来证明或否定关于用户行为变化的假设，以及测试和解读与业务指标直接相关的结果，以获得未来更复杂的AB测试经验的基础。
Name:Data of Unknown Quality
Description:In this module, you will be able to create trustworthy analysis from a new set of data. You will be able to coalesce some nulls and identify unreliable data and discover reasons why data might be missing. You will also be able to answer ambiguous questions by defining new metrics.
Name:Creating Clean Datasets
Description:In this module, you will be able to name the main the categories of data types. You will be able to explain how the unfiltered data can be manipulated into a table where you can conduct data analysis. You will be able to discuss why a data warehouse is separate from a production database, and you will be able to use the tools you learned to create your own trustworthy tables.
Name:SQL Problem Solving
Description:In this module, you will be able to map out your joins and be able to highlight the level of detail needed for different kinds of questions. You will be able to practice answering data questions, which should help you feel ready to get asked a whole slough of questions, vague questions, ambiguous questions, or even poorly worded questions. Finally, you will develop a strategy for answering all those questions using data.
Name:Case Study: AB Testing
Description:In this module, you will be able to use your SQL skills to set up a basic AB testing system. You will be able to apply hypothesis testing to prove or disprove a hypothesis about how user behavior changed. You will be able to test and interpret the results using a metric or metrics that are tied directly to some business metrics. You will be able to test your SQL skills and give you the base experience you need to learn anything more complicated in terms of AB testing in the future.
This course allows you to apply the SQL skills taught in “SQL for Data Science” to four increasingly complex and authentic data science inquiry case studies. We'll learn how to convert timestamps of all types to common formats and perform date/time calculations. We'll select and perform the optimal JOIN for a data science inquiry and clean data within an analysis dataset by deduping, running quality checks, backfilling, and handling nulls. We'll learn how to segment and analyze data per segment using windowing functions and use case statements to execute conditional logic to address a data science inquiry. We'll also describe how to convert a query into a scheduled job and how to insert data into a date partition. Finally, given a predictive analysis need, we'll engineer a feature from raw data using the tools and skills we've built over the course. The real-world application of these skills will give you the framework for performing the analysis of an AB test.
