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.

Why start a career in data analytics?

The amount of data created each day is tremendous. Any time you use your phone, look up something online, stream music, shop with a credit card, post on social media, or use GPS to map a route, you’re creating data. Companies must continually adjust their products, services, tools, and business strategies to meet consumer demand and react to emerging trends. Because of this, data analyst roles are in demand and competitively paid. Data analysts make sense of data and numbers to help organizations make better business decisions. They prepare, process, analyze, and visualize data, discovering patterns and trends and answering key questions along the way. Their work empowers their wider team to make better business decisions.

Why enroll in the Google Data Analytics Certificate?

You will learn the skill set required for becoming a junior or associate data analyst in the Google Data Analytics Certificate. Data analysts know how to ask the right question; prepare, process, and analyze data for key insights; effectively share their findings with stakeholders; and provide data-driven recommendations for thoughtful action. You’ll learn these job-ready skills in our certificate program through interactive content (discussion prompts, quizzes, and activities) in under six months, with under 10 hours of flexible study a week. Along the way, you'll work through a curriculum designed with input from top employers and industry leaders, like Tableau, Accenture, and Deloitte. You’ll even have the opportunity to complete a case study that you can share with potential employers to showcase your new skill set. 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 analytics.

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.

What tools and platforms are taught in the curriculum?

You'll learn to use analysis tools and platforms such as spreadsheets (Google Sheets or Microsoft Excel), SQL, presentation tools (Powerpoint or Google Slides), Tableau, Python, and Kaggle.

Which “spreadsheet” platform is being taught?

Learners can self-select which platform they want to use throughout the program: Google Sheets or Microsoft Excel. It’s up to the learner’s preference. All activities throughout the syllabus can be performed on either platform.

Do you need to take each course in order?

We highly recommend completing the courses in the order presented because the content in each course builds on information from earlier lessons.

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 Certificate?

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.

Prepare Data for Exploration

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Prepare Data for Exploration

This course is part of Google Data Analytics Professional Certificate

Instructor: Google Career Certificates

1,112,193 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

23,232 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

23,232 reviews

Beginner level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

What you'll learn

Explain what factors to consider when making decisions about data collection.
Discuss the difference between biased and unbiased data.
Describe databases with references to their functions and components.
Describe best practices for organizing data.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

29 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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Build your Data Analysis expertise

This course is part of the Google Data Analytics Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Google

There are 5 modules in this course

This is the third course in the Google Data Analytics Certificate. As you continue to build on your understanding of the topics from the first two courses, you’ll be introduced to new topics that will help you gain practical data analytics skills. You’ll learn how to use tools like spreadsheets and SQL to extract and make use of the right data for your objectives, and how to organize and protect your data. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Find out how analysts decide what data to collect for analysis. - Learn about structured and unstructured data, data types, and data formats. - Discover how to identify different types of bias in data to help ensure data credibility. - Explore how analysts use spreadsheets and SQL within databases and data sets. - Examine open data and the relationship between, and importance of, data ethics and data privacy. - Gain an understanding of how to access databases and extract, filter, and sort the data they contain. - Learn best practices for organizing data and keeping it secure.

Module details

A massive amount of data is generated every single day. In this part of the course, you will discover how this data is generated and how analysts decide which data to use for analysis. You’ll also learn about structured and unstructured data, data types, and data formats as you start thinking about how to prepare your data for analysis.

What's included

9 videos10 readings6 assignments1 plugin

9 videosTotal 31 minutes

Introduction to data exploration4 minutes
Hallie: Fascinating data insights 3 minutes
Data collection in our world4 minutes
Determine what data to collect4 minutes
Discover data formats5 minutes
Continue exploring structured data2 minutes
Know the type of data you're working with4 minutes
Data table components2 minutes
Meet wide and long data4 minutes

10 readingsTotal 56 minutes

Course 3 overview8 minutes
Helpful resources and tips4 minutes
Select the right data4 minutes
Data formats in practice4 minutes
The effects of different structures4 minutes
Data modeling levels and techniques8 minutes
Use Boolean logic8 minutes
Step-by-Step: Meet wide and long data 8 minutes
Transforming data4 minutes
Glossary terms from module 14 minutes

6 assignmentsTotal 149 minutes

Test your knowledge on collecting data 8 minutes
Optional Self-Reflection: Unstructured data20 minutes
Test your knowledge on data formats and structures8 minutes
Hands-On Activity: Introduction to Kaggle60 minutes
Test your knowledge on data types, fields, and values 8 minutes
Module 1 challenge45 minutes

1 pluginTotal 10 minutes

Differentiate data types10 minutes

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 part of the 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.

What's included

12 videos4 readings6 assignments

12 videosTotal 36 minutes

Introduction to bias, credibility, privacy, and ethics1 minute
Bias: From questions to conclusions3 minutes
Biased and unbiased data2 minutes
Understand bias in data4 minutes
Identify good data sources3 minutes
What is "bad" data?3 minutes
Essential data ethics5 minutes
Optional refresher: Alex and the importance of data ethics3 minutes
Prioritize data privacy2 minutes
Andrew: The ethical use of data3 minutes
Features of open data4 minutes
Andrew: Steps for ethical data use3 minutes

4 readingsTotal 20 minutes

Data anonymization4 minutes
The open data debate4 minutes
Resources for open data8 minutes
Glossary terms from module 24 minutes

6 assignmentsTotal 132 minutes

Test your knowledge on unbiased and objective data 8 minutes
Test your knowledge on data credibility8 minutes
Test your knowledge on data ethics and privacy 8 minutes
Hands-On Activity: Kaggle datasets60 minutes
Test your knowledge on open data8 minutes
Module 2 challenge40 minutes

When you analyze large datasets, you’ll access much of the data from a database. In this part of the course, you will learn about databases, including how to access them and extract, filter, and sort the data they contain. You’ll also explore metadata to discover its many facets and how analysts use it to better understand their data.

What's included

10 videos13 readings12 assignments

10 videosTotal 38 minutes

All about databases2 minutes
Database features and components4 minutes
Demystify metadata 4 minutes
Manage data with metadata3 minutes
Megan: Fun with metadata3 minutes
So many places to find data3 minutes
Import data from spreadsheets and databases 4 minutes
Sort and filter to focus on relevant data6 minutes
Get to know BigQuery, including sandbox and billing options3 minutes
BigQuery in action6 minutes

13 readingsTotal 92 minutes

Maximize databases in data analytics4 minutes
Inspect a dataset: A guided, hands-on tour8 minutes
Metadata is as important as the data itself8 minutes
Metadata and metadata repositories8 minutes
Working with .csv files4 minutes
Step-by-Step: Import data from spreadsheets and databases8 minutes
Import data dynamically8 minutes
Explore public datasets8 minutes
Set up your BigQuery account8 minutes
Get started with BigQuery8 minutes
Step-by-Step: BigQuery in action8 minutes
In-depth guide: SQL best practices8 minutes
Glossary terms from module 34 minutes

12 assignmentsTotal 400 minutes

Test your knowledge on working with databases8 minutes
Test your knowledge on metadata8 minutes
Test your knowledge on accessing data sources8 minutes
Hands-On Activity: Clean data in spreadsheets with sorting and filtering60 minutes
Self-Reflection: Compare databases and spreadsheets 20 minutes
Test your knowledge on sorting and filtering8 minutes
Hands-On Activity: Introduction to BigQuery60 minutes
Hands-On Activity: Create a custom table in BigQuery60 minutes
Hands-On Activity: Choose the right tool for the job60 minutes
Hands-On Activity: More practice with SQL60 minutes
Test your knowledge on using SQL with large datasets8 minutes
Module 3 challenge40 minutes

Good organizational skills are a big part of most types of work, especially data analytics. In this part of the course, you will learn best practices for organizing data and keeping it secure. You’ll also understand how analysts use file naming conventions to help them keep their work organized.

What's included

3 videos3 readings4 assignments1 plugin

3 videosTotal 9 minutes

Feel confident in your data1 minute
Let's get organized5 minutes
Security features in spreadsheets3 minutes

3 readingsTotal 16 minutes

File organization guidelines8 minutes
Balance security and analytics4 minutes
Glossary terms from module 44 minutes

4 assignmentsTotal 76 minutes

Test your knowledge on bringing data to order8 minutes
Self-Reflection: Protect your resources20 minutes
Test your knowledge on securing data8 minutes
Module 4 challenge40 minutes

1 pluginTotal 10 minutes

Effective file-naming and organization methods10 minutes

Having a strong online presence can be a big help for job seekers of all kinds. In this part of the course, you will explore how to manage your online presence. You’ll also discover the benefits of networking with other data analytics professionals.

What's included

2 videos4 readings1 assignment

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Google Career Certificates

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Reviewed on Aug 8, 2023

I love how the course it's not limited to teach about the technical skills of a data analyst professional, but it also helps you discover some soft skills that make you a better human resource.

Reviewed on Dec 31, 2021

I liked that some assignments had me use SQL and get more comfortable using it. However I would have liked more assignments using SQL and Sheets to get more practice. Otherwise was pretty fun.

Reviewed on Jul 5, 2023

There is a lot of useful information about how to manage data, considerations for data security, and organization of data. I felt as though I learned a lot and advanced my skills with data analysis.

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