Process Data from Dirty to Clean
4.8 /5- Offered byCoursera
Process Data from Dirty to Clean at Coursera Overview
Duration | 22 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Process Data from Dirty to Clean at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 8 in the Google Data Analytics
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level No prior experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.
- Approx. 22 hours to complete
- English Subtitles: English
Process Data from Dirty to Clean at Coursera Course details
- This is the fourth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. 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, you will be able to do the following:
- - Learn how to check for data integrity.
- - Discover data cleaning techniques using spreadsheets.
- - Develop basic SQL queries for use on databases.
- - Apply basic SQL functions for cleaning and transforming data.
- - Gain an understanding of how to verify the results of cleaning data.
- - Explore the elements and importance of data cleaning reports.
Process Data from Dirty to Clean at Coursera Curriculum
Before you clean, check for integrity
Welcome to the course
Why data integrity is important
Balancing objectives with data integrity
Dealing with insufficient data
The importance of sample size
Using statistical power
Determine the best sample size
Evaluate the reliability of your data
Course syllabus
More about data integrity and compliance
Well-aligned objectives and data
What to do when you find an issue with your data
Calculating sample size
What to do when there is no data
Sample size calculator
All about margin of error
Glossary: Terms and definitions
Test your knowledge on data integrity and analytics objectives
Self-Reflection: Why pre-cleaning activities are important
Test your knowledge on insufficient data
Test your knowledge on testing your data
Test your knowledge on the margin of error
Weekly challenge 1
Sparkling-clean data
Clean it up!
Why data cleaning is important
Angie: Why I love cleaning data
Defining dirty data
Data-cleaning tools and techniques
Cleaning data from multiple sources
Data-cleaning features in spreadsheets
Optimize the data-cleaning process
Different data perspectives
Even more data-cleaning techniques
What is dirty data?
Common data-cleaning pitfalls
Workflow automation
Learning Log: Develop your approach to cleaning data
Glossary: Terms and definitions
Test your knowledge on clean versus dirty data
Hands-On Activity: Cleaning with spreadsheets
Test your knowledge on data-cleaning techniques
Test your knowledge on cleaning data in spreadsheets
Weekly challenge 2
Cleaning data with SQL
Using SQL to clean data
Sally: For the love of SQL
Understanding SQL capabilities
Spreadsheets versus SQL
Widely used SQL queries
Evan: Having fun with SQL
Cleaning string variables using SQL
Advanced data-cleaning functions, part 1
Advanced data-cleaning functions, part 2
Using SQL as a junior data analyst
SQL dialects and their uses
Prerequisite for next video: Uploading your dataset in BigQuery
Glossary: Terms and definitions
Hands-On Activity: Processing time with SQL
Test your knowledge on SQL
Test your knowledge on SQL queries
Self-Reflection: Challenges with SQL
Weekly challenge 3
Verify and report on your cleaning results
Verifying and reporting results
Cleaning and your data expectations
The final step in data cleaning
Capturing cleaning changes
Why documentation is important
Feedback and cleaning
Data-cleaning verification: A checklist
Embrace changelogs
Advanced formulas and functions for speedy data cleaning
Glossary: Terms and definitions
Test your knowledge on manual data cleaning
Self-Reflection: Creating a changelog
Test your knowledge on documenting the cleaning process
Weekly challenge 4
Optional: Adding data to your resume
About the data-analyst hiring process
The data analyst job-application process
Creating a resume
Making your resume unique
Joseph: Inclusion in the data industry
Translating past work experience
Kate: My career path as a data analyst
Where does your interest lie?
CareerCon resources on YouTube
Adding professional skills to your resume
Adding softs skills to your resume
Hands-On Activity: Build a resume
Hands-On Activity: Adding skills to a resume
Hands-On Activity: Adding experience to a resume
Course challenge
Get ready for the course challenge
Congratulations!
Glossary: Terms and definitions
Coming up next ...
Course challenge
Process Data from Dirty to Clean at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?