Explore Raw Data

本课程是 Google Data Analysis with Python 专项课程的一部分

位教师：Google Career Certificates

访问权限由 New York State Department of Labor 提供

1,557 人已注册

4个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

7 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

需要一些相关经验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Identify ethical issues that may come up during the data “discovering” practice of EDA
Using the PACE workflow to understand whether given data is adequate and applicable to a data science project
Recognize when and how to communicate status updates and questions to key stakeholders

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

4 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Google Data Analysis with Python 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

Finding stories in data using exploratory data analysis (EDA) is all about organizing and interpreting raw data. Python can help you do this quickly and effectively. In this course, you’ll learn how to use Python to perform the EDA practices of discovering and structuring.

By the end of this course, you will be able to: • Identify ethical issues that may come up during the data “discovering” practice of EDA • Use Python to merge or join data based on defined criteria • Use Python to sort and/or filter data • Use relevant Python libraries for cleaning raw data • Recognize opportunities for creating hypotheses based on raw data • Recognize when and how to communicate status updates and questions to key stakeholders • Apply Python tools to examine raw data structure and format. • Use the PACE workflow to understand whether given data is adequate and applicable to a data science project • Differentiate between the common formats of raw data sources (json, tabular, etc.) and data types

Data professionals must understand data sources, file formats, and responsible parties during exploratory analysis. In this module, you will learn when to contact data owners for questions or issues, how to import data using Python and perform EDA using basic functions in Python.

涵盖的内容

5个视频3篇阅读材料1个作业3个非评分实验室

5个视频总计34分钟

Introduction to data exploration 3分钟
Yaser: Understand data to drive value 2分钟
Where the data comes from 9分钟
Find stories using the six exploratory data analysis practices 10分钟
EDA using basic data functions with Python 10分钟

3篇阅读材料总计24分钟

Reference guide: The EDA process 8分钟
Reference guide: Import datasets with Python 8分钟
Reference guide: Pandas methods for the discovery of a dataset 8分钟

1个作业总计8分钟

Test your knowledge: Discovering is the beginning of an investigation 8分钟

3个非评分实验室总计100分钟

Annotated follow-along resource: EDA using basic functions with Python 20分钟
Activity: Discover what is in your dataset 60分钟
Exemplar: Discover what is in your dataset 20分钟

EDA discovery uses targeted questioning to identify data gaps and missing information. In this module, you will learn how to formulate hypotheses, manipulate datetime strings and create bar graph visualizations.

涵盖的内容

2个视频1篇阅读材料1个作业1个非评分实验室

2个视频总计20分钟

Discover what is missing from your dataset 6分钟
Date string manipulations with Python 14分钟

1篇阅读材料总计8分钟

Reference guide: Datetime manipulation 8分钟

1个作业总计6分钟

Test your knowledge: Understand data format 6分钟

1个非评分实验室总计20分钟

Annotated follow-along guide: Date string manipulations with Python 20分钟

Structuring is an EDA practice for organizing data to learn more about it. In this module, you will learn different types of structuring methods, pandas tools for structuring datasets, and interpret histograms to understand data distributions.