This is the second course in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations.
Go Beyond the Numbers: Translate Data into Insights
Go Beyond the Numbers: Translate Data into Insights
本课程是 Google Advanced Data Analytics 专业证书 的一部分
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您将学到什么
Apply the exploratory data analysis (EDA) process
Explore the benefits of structuring and cleaning data
Investigate raw data using Python
Create data visualizations using Tableau
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要了解的详细信息
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作业
21 项作业
授课语言：英语（English）
积累 Data Analysis 领域的专业知识
本课程是 Google Advanced Data Analytics 专业证书 专项课程的一部分
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该课程共有5个模块
You’ll learn how to find the stories within data and share them with your audience. You’ll learn about the methods and benefits of data cleaning and how it can help you discover those stories. You’ll also go over the steps of the EDA process and learn how EDA can help you quickly understand data. Finally, you'll explore different ways to visualize data to communicate key insights.
涵盖的内容
8个视频5篇阅读材料3个作业2个插件
8个视频•总计37分钟
- Introduction to Course 2•5分钟
- Robb: Obstacles and achievements•4分钟
- Welcome to module 1•1分钟
- Find stories using the six exploratory data analysis practices •10分钟
- Benj: Data science and storytelling•3分钟
- Combine PACE and EDA practices•7分钟
- PACE with data visualizations•5分钟
- Wrap-up•3分钟
5篇阅读材料•总计34分钟
- Course 2 overview•8分钟
- Helpful resources and tips•8分钟
- Case study: Deloitte •8分钟
- Reference guide: The EDA process•8分钟
- Glossary terms from module 1•2分钟
3个作业•总计62分钟
- Module 1 challenge•50分钟
- Test your knowledge: Tell stories with data•6分钟
- Test your knowledge: How PACE informs EDA and data visualizations•6分钟
2个插件•总计20分钟
- Categorize: EDA best practices•10分钟
- [Turkish learners ONLY] Categorize: EDA best practices - Türkçe•10分钟
Finding stories in data using EDA is all about organizing and interpreting raw data. Python can help you do this quickly and effectively. You’ll learn how to use Python to perform the EDA practices of discovering and sculpting.
涵盖的内容
9个视频6篇阅读材料4个作业7个非评分实验室2个插件
9个视频•总计70分钟
- Welcome to module 2•3分钟
- Yaser: Understand data to drive value•2分钟
- Where the data comes from•11分钟
- EDA using basic data functions with Python•10分钟
- Discover what is missing from your dataset•6分钟
- Date string manipulations with Python•14分钟
- Use structuring methods to establish order in your dataset•5分钟
- EDA structuring with Python•16分钟
- Wrap-up•3分钟
6篇阅读材料•总计38分钟
- Reference guide: Import datasets with Python•8分钟
- Reference guide: Pandas methods for the discovery of a dataset•8分钟
- Reference guide: Datetime manipulation•8分钟
- Reference guide: Pandas tools for structuring a dataset•8分钟
- Histograms•2分钟
- Glossary terms from module 2•4分钟
4个作业•总计70分钟
- Module 2 challenge•50分钟
- Test your knowledge: Discovering is the beginning of an investigation•8分钟
- Test your knowledge: Understand data format•6分钟
- Test your knowledge: Create structure from raw data•6分钟
7个非评分实验室•总计220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
- Annotated follow-along guide: Date string manipulations with Python•20分钟
- Annotated follow-along guide: EDA structuring with Python•20分钟
- Activity: Structure your data•60分钟
- Exemplar: Structure your data•20分钟
2个插件•总计20分钟
- Categorize: Structuring methods•10分钟
- [Turkish learners ONLY] Categorize: Structuring methods - Türkçe•10分钟
You’ll explore three more EDA practices: cleaning, joining, and validating. You'll discover the importance of these practices for data analysis, and you’ll use Python to clean, validate, and join data.
涵盖的内容
11个视频6篇阅读材料5个作业5个非评分实验室2个插件
11个视频•总计78分钟
- Welcome to module 3•4分钟
- Methods for handling missing data •8分钟
- Work with missing data in a Python notebook•12分钟
- Remy: A day in the life of a data professional•3分钟
- Account for outliers•6分钟
- Identify and deal with outliers in Python•14分钟
- Sort numbers versus names•5分钟
- Label encoding in Python•9分钟
- The value of input validation•7分钟
- Input validation with Python•8分钟
- Wrap-up•2分钟
6篇阅读材料•总计44分钟
- Data deduplication with Python•8分钟
- Protect the people behind the data•8分钟
- Reference guide: How to handle outliers•8分钟
- Other approaches to data transformation•8分钟
- Reference guide: Data cleaning in Python •8分钟
- Glossary terms from module 3•4分钟
5个作业•总计76分钟
- Module 3 challenge•50分钟
- Test your knowledge: The challenge of missing or duplicate data•8分钟
- Test your knowledge: The ins and outs of data outliers•6分钟
- Test your knowledge: Changing categorical data to numerical data•6分钟
- Test your knowledge: Input validation•6分钟
5个非评分实验室•总计180分钟
- Annotated follow-along guide: Work with missing data in a Python notebook•20分钟
- Activity: Address missing data•60分钟
- Exemplar: Address missing data•20分钟
- Activity: Validate and clean your data•60分钟
- Exemplar: Validate and clean your data•20分钟
2个插件•总计20分钟
- Identify: Python functions for cleaning data•10分钟
- [Turkish learners ONLY] Identify: Python functions for cleaning data - Türkçe•10分钟
You’ll practice creating and presenting data stories in an ethical, accessible, and professional way. You'll also explore advanced data visualization techniques in Tableau.
涵盖的内容
8个视频11篇阅读材料5个作业2个插件
8个视频•总计41分钟
- Welcome to module 4•3分钟
- The visualization life cycle•5分钟
- Work with Tableau, Part 1•7分钟
- Work with Tableau, Part 2•7分钟
- Drew: Explore the possibilities of data•3分钟
- Craft compelling stories with Tableau•9分钟
- Present like a pro with Tableau•6分钟
- Wrap-up•1分钟
11篇阅读材料•总计64分钟
- Tableau Public overview•8分钟
- How to sign on to Tableau Public •8分钟
- Download your datasets and begin presenting with Tableau •4分钟
- Follow-along guide: Work with Tableau, Part 1•4分钟
- Follow-along guide: Work with Tableau, Part 2•8分钟
- Activity Exemplar: Design a bar graph that tells a story in Tableau Public•4分钟
- Follow-along guide: Craft compelling stories with Tableau•8分钟
- The top five data visualization resources•8分钟
- Follow-along guide: Present like a pro with Tableau•4分钟
- Activity Exemplar: Build an interactive dashboard in Tableau Public•4分钟
- Glossary terms from module 4•4分钟
5个作业•总计120分钟
- Module 4 challenge•50分钟
- Test your knowledge: Present a story•4分钟
- Activity: Design a bar graph that tells a story in Tableau Public•30分钟
- Activity: Build an interactive dashboard in Tableau Public•30分钟
- Test your knowledge: Advanced Tableau•6分钟
2个插件•总计20分钟
- Identify: Compelling visualizations•10分钟
- [Turkish learners ONLY] Identify: Compelling visualizations - Türkçe•10分钟
In this end-of-course project, you’ll practice using Python to perform EDA on a workplace scenario dataset. Then, you'll use Python and Tableau to visualize the data.
涵盖的内容
4个视频10篇阅读材料4个作业6个非评分实验室
4个视频•总计9分钟
- Welcome to module 5•3分钟
- Introduction to your Course 2 end-of-course portfolio project•1分钟
- End-of-course project wrap-up and tips for ongoing career success•2分钟
- Course wrap-up•3分钟
10篇阅读材料•总计52分钟
- Explore your Course 2 workplace scenarios•8分钟
- Course 2 end-of-course portfolio project overview: Automatidata•8分钟
- Activity Exemplar: Create your Course 2 Automatidata project•4分钟
- Course 2 end-of-course portfolio project overview: TikTok•8分钟
- Activity Exemplar: Create your Course 2 TikTok project•4分钟
- Course 2 end-of-course portfolio project overview: Waze•8分钟
- Activity Exemplar: Create your Course 2 Waze project•4分钟
- Course 2 glossary•2分钟
- Reflect and connect with peers•2分钟
- Get started on the next course•4分钟
4个作业•总计135分钟
- Assess your Course 2 end-of-course project•45分钟
- Activity: Create your Course 2 Automatidata project•30分钟
- Activity: Create your Course 2 TikTok project•30分钟
- Activity: Create your Course 2 Waze project•30分钟
6个非评分实验室•总计240分钟
- Activity: Course 2 Automatidata project lab•60分钟
- Exemplar: Course 2 Automatidata project lab•20分钟
- Activity: Course 2 TikTok project lab•60分钟
- Exemplar: Course 2 TikTok project lab•20分钟
- Activity: Course 2 Waze project lab•60分钟
- Exemplar: Course 2 Waze project lab•20分钟
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自 2018开始学习的学生
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自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
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学生评论
4.7
913 条评论
- 5 stars
82.80%
- 4 stars
12.48%
- 3 stars
2.08%
- 2 stars
1.31%
- 1 star
1.31%
显示 3/913 个
M
MB
5·
已于 Feb 11, 2024审阅
Very well designed course for anyone having experience of any field willing to dive into data analytics.
M
MH
5·
已于 Dec 19, 2023审阅
The Course was very effective which increased my skills, knowledge and confidence level.
J
JM
5·
已于 Aug 22, 2023审阅
Very Helpful Course! The storytell methods described are really helpful to me. I have always had an issue with getting my point across but now I know where my problem was and have corrected it.
常见问题
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.
The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data analysts.
During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.
This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools. To succeed in this certificate program, you should already know about key foundational aspects of data analysis, such as the data analysis process and data life cycle, databases and general database elements, programming language basics, and project stakeholders.
The content in this certificate program builds upon data analytics concepts taught in the Google Data Analytics Certificate. These include key foundational aspects of data analysis such as the data analysis process and data life cycle, databases and general database elements such as primary and foreign keys, SQL and programming language basics, and project stakeholders. If you haven’t completed that program or if you’re unsure whether you have the necessary prerequisites, you can take an ungraded assessment in Course 1 Module 1 of this certificate to evaluate your readiness.
You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analytics.
We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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