Process Data from Dirty to Clean

This course is part of Google Data Analytics Professional Certificate

Instructor: Google Career Certificates

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What you'll learn

Define different types of data integrity and identify risks to data integrity.
Apply basic SQL functions to clean string variables in a database.
Develop basic SQL queries for use on databases.
Describe the process of verifying data cleaning results.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

21 quizzes, 3 assignments

Taught in English

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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

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There are 6 modules in this course

This is the fourth course in the Google Data Analytics Certificate. In this course, you’ll continue to build your understanding of data analytics and the concepts and tools that data analysts use in their work. You’ll learn how to check and clean your data using spreadsheets and SQL, as well as how to verify and report your data cleaning results. 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: - Check for data integrity. - Apply data cleaning techniques using spreadsheets. - Develop basic SQL queries for use on databases. - Use basic SQL functions to clean and transform data. - Verify the results of cleaning data. - Write an effective data cleaning report

Data integrity is critical to successful analysis. In this part of the course, you’ll explore methods and steps that analysts take to check their data for integrity. This includes knowing what to do when you don’t have enough data. You’ll also learn about random samples and understand how to avoid sampling bias. All of these methods will also help you ensure your analysis is successful.

What's included

8 videos10 readings5 quizzes1 assignment1 plugin

8 videosTotal 32 minutes

Introduction to data integrity3 minutesPreview module
Why data integrity is important2 minutes
Balance objectives with data integrity3 minutes
Deal with insufficient data3 minutes
The importance of sample size3 minutes
Using statistical power4 minutes
Determine the best sample size 4 minutes
Evaluate data reliability5 minutes

10 readingsTotal 170 minutes

Course 4 overview: Set your expectations20 minutes
Helpful resources and tips10 minutes
More about data integrity and compliance20 minutes
Well-aligned objectives and data 20 minutes
When you find an issue with your data10 minutes
Calculate sample size20 minutes
When data isn't readily available20 minutes
Sample size calculator20 minutes
All about margin of error20 minutes
Glossary terms from module 110 minutes

5 quizzesTotal 84 minutes

Test your knowledge on data integrity and analytics objectives8 minutes
Self-Reflection: Pre-cleaning activities20 minutes
Test your knowledge on insufficient data8 minutes
Test your knowledge on margin of error8 minutes
Module 1 challenge40 minutes

1 assignmentTotal 8 minutes

Test your knowledge on testing your data8 minutes

1 pluginTotal 10 minutes

Refresher: Your Google Data Analytics Certificate roadmap10 minutes

Every data analyst wants to analyze clean data. In this part of the course, you’ll learn the difference between clean and dirty data. Then, you’ll practice cleaning data in spreadsheets and other tools.

What's included

10 videos10 readings5 quizzes1 assignment1 plugin

10 videosTotal 66 minutes

Clean it up!2 minutesPreview module
Why data cleaning is critical5 minutes
Angie: I love cleaning data1 minute
Recognize and remedy dirty data5 minutes
Data-cleaning tools and techniques6 minutes
Clean data from multiple sources5 minutes
Data-cleaning features in spreadsheets8 minutes
Optimize the data-cleaning process14 minutes
Different data perspectives10 minutes
Even more data-cleaning techniques6 minutes

10 readingsTotal 180 minutes

What is dirty data?20 minutes
Common data-cleaning pitfalls20 minutes
Step-by-Step guide: Data-cleaning features in spreadsheets20 minutes
Step-by-Step: Optimize the data-cleaning process 20 minutes
Workflow automation20 minutes
Step-by-Step: Different data perspectives20 minutes
Step-by-Step: Even more data-cleaning techniques20 minutes
Working with .csv files10 minutes
Develop your approach to cleaning data20 minutes
Glossary terms from module 210 minutes

5 quizzesTotal 176 minutes

Hands-On Activity: Cleaning data with spreadsheets60 minutes
Test your knowledge on the first steps toward clean data8 minutes
Hands-On Activity: Clean data with spreadsheet functions60 minutes
Test your knowledge on cleaning data in spreadsheets8 minutes
Module 2 challenge40 minutes

1 assignmentTotal 8 minutes

Test your knowledge on data cleaning8 minutes

1 pluginTotal 10 minutes

Principles of data integrity 10 minutes

Knowing a variety of ways to clean data can make a data analyst’s job much easier. In this part of the course, you’ll use SQL to clean data from databases. In particular, you’ll explore how SQL queries and functions can be used to clean and transform your data before an analysis.

What's included

9 videos7 readings4 quizzes1 assignment1 plugin

9 videosTotal 48 minutes

Use SQL to clean data0 minutesPreview module
Sally: For the love of SQL3 minutes
Understand SQL capabilities3 minutes
Spreadsheets versus SQL4 minutes
Widely used SQL queries6 minutes
Evan: Having fun with SQL 2 minutes
Clean string variables using SQL12 minutes
Advanced data-cleaning functions, part 16 minutes
Advanced data-cleaning functions, part 28 minutes

7 readingsTotal 105 minutes

How a junior data analyst uses SQL10 minutes
SQL dialects and their uses20 minutes
Review: Set up your BigQuery account20 minutes
Review: Get started with BigQuery20 minutes
Optional: Upload the customer dataset to BigQuery10 minutes
Optional: Upload the store transactions dataset to BigQuery20 minutes
Glossary terms from module 35 minutes

4 quizzesTotal 185 minutes

Hands-On Activity: Processing time with SQL60 minutes
Hands-On Activity: Clean data using SQL60 minutes
Self-Reflection: Challenges with SQL20 minutes
Module 3 challenge45 minutes

1 assignmentTotal 10 minutes

Test your knowledge on SQL queries10 minutes

1 pluginTotal 10 minutes

Data-cleaning with SQL functions10 minutes

When you clean data, you make changes to the original dataset. It’s important to verify the changes you make are accurate and to let your teammates know about the changes. In this part of the course, you’ll learn to verify that data is clean and report your data cleaning results. With verified clean data, you’re ready to begin analyzing!

What's included

6 videos5 readings4 quizzes

6 videosTotal 28 minutes

Verify and report results3 minutesPreview module
Confirm data-cleaning meets business expectations5 minutes
Verification of data cleaning8 minutes
Capture cleaning changes5 minutes
Why documentation is important3 minutes
Feedback and cleaning2 minutes

5 readingsTotal 65 minutes

Step-by-Step: Verification of data cleaning20 minutes
Data-cleaning verification checklist10 minutes
Embrace changelogs20 minutes
Advanced functions for speedy data cleaning10 minutes
Glossary terms from module 45 minutes

4 quizzesTotal 76 minutes

Test your knowledge on manual data cleaning8 minutes
Self-Reflection: Creating a changelog20 minutes
Test your knowledge on documenting the cleaning process8 minutes
Module 4 challenge40 minutes

Creating an effective resume will help you in your data analytics career. In this part of the course, you’ll learn all about the job application process. Your focus will be on building a resume that highlights your strengths and relevant experience.