What is data analytics?

Data is a group of facts that can take many different forms, such as numbers, pictures, words, videos, observations, and more. We use and create data everyday, like when we stream a show or song or post on social media. Data analytics is the collection, transformation, and organization of these facts to draw conclusions, make predictions, and drive informed decision-making.

What background is required?

No experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.

Do you need to be strong at math to succeed in this certificate?

You don't need to be a math all-star to succeed in this certificate. You need to be curious and open to learning with numbers (the language of data analysts). Being a strong data analyst is more than just math—it's about asking the right questions, finding the best sources to answer your questions effectively, and illustrating your findings clearly in visualizations.

Do you need to take each course in order?

We highly recommend taking the courses in the order presented, as the content builds on information from earlier courses. This is the fifth course in a series of nine courses that make up the Google Data-Driven Decision Making Specialization.

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, 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.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Data Responsibility

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Data Responsibility

本课程是 Google Data-Driven Decision Making 专项课程的一部分

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深入了解一个主题并学习基础知识。

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您将学到什么

Discuss the difference between biased and unbiased data.
Discuss characteristics of credible sources of data including reference to untidy data
Explain the relationship between data ethics and data privacy
Demonstrate an awareness of the accessibility issues associated with open data

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授课语言：英语（English）

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该课程共有4个模块

Before you work with data, you must confirm that it is unbiased and credible. After all, if you start your analysis with unreliable data, you won’t be able to trust your results. In this course, you will learn to identify bias in data and to ensure your data is credible. You’ll also explore open data and the importance of data ethics and data privacy.

By the end of this course, you will be able to: - Explain what is involved in reviewing data to identify bias - Discuss the difference between biased and unbiased data - Identify different types of bias including confirmation, interpretation, and observer bias - Discuss characteristics of credible sources of data including reference to untidy data - Explain the concept of open data with reference to the ongoing debate in data analytics - Define data ethics and data privacy - Explain the relationship between data ethics and data privacy - Demonstrate an understanding of the benefits of anonymizing data - Demonstrate an awareness of the accessibility issues associated with open data

单元详情

As a data analyst, it is important to understand why reaching conclusions on biased data can have real-world effects. In this part of the course, you'll learn what bias is, how it affects data, the types of bias, and how to spot it.