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Google Career Certificates
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.
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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.
Read more
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
Register for this course and see more details by visiting:
OpenCourser.com/course/7dbc6m/process
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What's inside
Syllabus
The importance of integrity
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.
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Syllabus
The importance of integrity
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.
Read more
Clean data for more accurate insights
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.
Data cleaning with SQL
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.
Verify and report on cleaning results
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!
Optional: Add data to your resume
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.
Course wrap-up
Review the course glossary and prepare for the next course in the Google Data Analytics Certificate program.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores industry-standard methods and tools for data cleaning
Teaches SQL and spreadsheet data cleaning techniques used by current Google data analysts
Develops skills for checking data integrity, cleaning data, and reporting data cleaning results
Taught by expert Google data analysts, ensuring relevance and accuracy
Provides hands-on practice with real-world data cleaning tasks
Part of the Google Data Analytics Certificate program, indicating comprehensiveness and structure
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Reviews summary
Data clean and preparation
The "Process Data from Dirty to Clean" course by Google on Coursera is highly rated by learners for its comprehensive and engaging content on data cleaning techniques. The course is particularly lauded for its hands-on practice opportunities, clear explanations by the instructor, and emphasis on industry-relevant tools and techniques. However, some learners have expressed a desire for more hands-on exercises and a more comprehensive coverage of SQL.
**Key takeaways:**
* Data cleaning is a crucial step in data analysis, as it ensures the accuracy and reliability of insights derived from data.
* Spreadsheets and SQL are essential tools for data cleaning.
* Data analysts should be able to identify and address common data quality issues, such as missing values, duplicates, and inconsistencies.
* It is important to document data cleaning processes and results to ensure transparency and reproducibility.
* Data analysts should be aware of industry best practices for data cleaning and preparation.
**Noted strengths:**
* Clear and engaging video lessons
* Hands-on practice exercises
* Knowledgeable and experienced instructor
* Emphasis on industry-relevant tools and techniques
* Practical tips and real-world examples
**Suggestions for improvement:**
* More hands-on exercises
* More comprehensive coverage of SQL
* Separate module on resume preparation
There are a variety of tools that can be used for data cleaning, including:
* Spreadsheets
* SQL
* Python
* R
The best tool for data cleaning will depend on the size and complexity of your data, as well as your own level of expertise.
"There are a variety of tools that can be used for data cleaning, including:
* Spreadsheets
* SQL
* Python
* R"
"The best tool for data cleaning will depend on the size and complexity of your data, as well as your own level of expertise."
Data integrity refers to the accuracy, completeness, and consistency of data. Data integrity is important because it ensures that the data you are using for analysis is reliable and trustworthy.
There are a number of factors that can compromise data integrity, including:
* Human error
* Data entry errors
* Data transmission errors
* Data storage errors
* Data security breaches
It is important to have measures in place to protect data integrity and ensure that your data is accurate, complete, and consistent.
"Data integrity refers to the accuracy, completeness, and consistency of data."
"Data integrity is important because it ensures that the data you are using for analysis is reliable and trustworthy."
"There are a number of factors that can compromise data integrity, including:
* Human error
* Data entry errors
* Data transmission errors
* Data storage errors
* Data security breaches"
"It is important to have measures in place to protect data integrity and ensure that your data is accurate, complete, and consistent."
Data documentation is the process of creating and maintaining documentation that describes the structure and content of your data. Data documentation is important because it helps you to understand and use your data more effectively. Data documentation should include information such as:
* The data source
* The data structure
* The data fields
* The data quality
* The data usage
Data documentation can be created using a variety of tools, such as wikis, spreadsheets, and databases.
"Data documentation is the process of creating and maintaining documentation that describes the structure and content of your data."
"Data documentation is important because it helps you to understand and use your data more effectively."
"Data documentation should include information such as:
* The data source
* The data structure
* The data fields
* The data quality
* The data usage"
"Data documentation can be created using a variety of tools, such as wikis, spreadsheets, and databases."
Data cleaning is a process of identifying and correcting errors and inconsistencies in data so that it can be used for analysis. Some of the common data cleaning tasks include:
* Removing duplicate data
* Filling in missing values
* Correcting data errors
* Converting data to a consistent format
* Normalizing data
Data cleaning can be a time-consuming and challenging task, but it is essential for ensuring the accuracy and reliability of your data analysis results.
"Data cleaning is a process of identifying and correcting errors and inconsistencies in data so that it can be used for analysis."
"Some of the common data cleaning tasks include:
* Removing duplicate data
* Filling in missing values
* Correcting data errors
* Converting data to a consistent format
* Normalizing data"
"Data cleaning can be a time-consuming and challenging task, but it is essential for ensuring the accuracy and reliability of your data analysis results."
The data cleaning process typically involves the following steps:
1. **Data profiling:** This step involves examining your data to identify errors and inconsistencies.
2. **Data cleansing:** This step involves correcting the errors and inconsistencies that you identified in step 1.
3. **Data validation:** This step involves verifying that the data cleaning process was successful.
4. **Data analysis:** This step involves using the cleaned data to perform analysis.
"The data cleaning process typically involves the following steps:
1. **Data profiling:** This step involves examining your data to identify errors and inconsistencies.
2. **Data cleansing:** This step involves correcting the errors and inconsistencies that you identified in step 1.
3. **Data validation:** This step involves verifying that the data cleaning process was successful.
4. **Data analysis:** This step involves using the cleaned data to perform analysis."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Process Data from Dirty to Clean with these
activities:
Review spreadsheet formulas
Show steps
Ensure you have a strong foundation in spreadsheet formulas before beginning the course.
Browse courses on
Spreadsheets
Show steps
-
Open a spreadsheet.
-
Review the basic formulas.
-
Practice using the formulas.
Watch tutorials on data cleaning techniques
Show steps
Gain a deeper understanding of data cleaning techniques by watching video tutorials.
Browse courses on
Data Cleaning
Show steps
-
Visit the YouTube website.
-
Search for "data cleaning tutorials".
-
Watch several tutorials.
-
Take notes on the techniques you learn.
Review Data Science for Business
Show steps
Reinforce your understanding of data science fundamentals by reviewing a classic text in the field.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the preface and introduction.
-
Read Chapter 1: Data Science in the Real World.
-
Read Chapter 2: Data Science Process Overview.
-
Read Chapter 3: Data.
-
Read Chapter 4: Modeling.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a data analytics study group
Show steps
Collaborate with peers to discuss course concepts and work on projects together.
Browse courses on
Data Analytics
Show steps
-
Post a message on the course discussion board.
-
Meet up with other students in person or online.
-
Set a regular meeting time.
-
Discuss course concepts and work on projects together.
-
Attend all study group meetings.
Solve SQL practice problems
Show steps
Sharpen your SQL skills by solving practice problems.
Browse courses on
SQL
Show steps
-
Visit the LeetCode website.
-
Create an account.
-
Select the "SQL" category.
-
Solve the "Easy" problems.
-
Move on to the "Medium" problems when you feel comfortable with the "Easy" problems.
Attend a data analytics workshop
Show steps
Network with other data analytics professionals and learn about the latest trends in the field.
Browse courses on
Data Analytics
Show steps
-
Find a data analytics workshop.
-
Register for the workshop.
-
Attend the workshop.
-
Take notes.
-
Ask questions.
Create a data cleaning report
Show steps
Demonstrate your understanding of data cleaning by creating a report that highlights the techniques you used and the results you achieved.
Browse courses on
Data Cleaning
Show steps
-
Choose a dataset to clean.
-
Apply data cleaning techniques to the dataset.
-
Write a report that describes the techniques you used and the results you achieved.
-
Share your report with others.
Design a data dashboard
Show steps
Apply your data analytics skills to create a visually appealing and informative dashboard.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset.
-
Design the dashboard.
-
Create the dashboard.
-
Share the dashboard with others.
Review spreadsheet formulas
Show steps
Ensure you have a strong foundation in spreadsheet formulas before beginning the course.
Browse courses on
Spreadsheets
Show steps
Show steps
Show steps
- Open a spreadsheet.
- Review the basic formulas.
- Practice using the formulas.
Watch tutorials on data cleaning techniques
Show steps
Gain a deeper understanding of data cleaning techniques by watching video tutorials.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Visit the YouTube website.
- Search for "data cleaning tutorials".
- Watch several tutorials.
- Take notes on the techniques you learn.
Review Data Science for Business
Show steps
Reinforce your understanding of data science fundamentals by reviewing a classic text in the field.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the preface and introduction.
- Read Chapter 1: Data Science in the Real World.
- Read Chapter 2: Data Science Process Overview.
- Read Chapter 3: Data.
- Read Chapter 4: Modeling.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a data analytics study group
Show steps
Collaborate with peers to discuss course concepts and work on projects together.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Post a message on the course discussion board.
- Meet up with other students in person or online.
- Set a regular meeting time.
- Discuss course concepts and work on projects together.
- Attend all study group meetings.
Solve SQL practice problems
Show steps
Sharpen your SQL skills by solving practice problems.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Visit the LeetCode website.
- Create an account.
- Select the "SQL" category.
- Solve the "Easy" problems.
- Move on to the "Medium" problems when you feel comfortable with the "Easy" problems.
Attend a data analytics workshop
Show steps
Network with other data analytics professionals and learn about the latest trends in the field.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Find a data analytics workshop.
- Register for the workshop.
- Attend the workshop.
- Take notes.
- Ask questions.
Create a data cleaning report
Show steps
Demonstrate your understanding of data cleaning by creating a report that highlights the techniques you used and the results you achieved.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Choose a dataset to clean.
- Apply data cleaning techniques to the dataset.
- Write a report that describes the techniques you used and the results you achieved.
- Share your report with others.
Design a data dashboard
Show steps
Apply your data analytics skills to create a visually appealing and informative dashboard.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset.
- Design the dashboard.
- Create the dashboard.
- Share the dashboard with others.
Career center
Learners who complete Process Data from Dirty to Clean will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is a professional who analyzes data, finds trends, and generates insights that can be used to make better decisions in business.
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, and programming to extract insights from data that can help businesses make better decisions.
Business Analyst
Business Analyst
Business Analysts identify opportunities to improve business processes and recommend solutions.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect, analyze, and interpret data to help businesses understand their customers and make informed decisions about products, services, and marketing strategies.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and economics to analyze financial data and make recommendations for investments.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and statistics to solve complex problems in a variety of industries, including manufacturing, healthcare, and transportation.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to help businesses make informed decisions.
Database Administrator
Database Administrator
Database Administrators are responsible for the design, implementation, and maintenance of databases.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that supports data analysis.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models.
Actuary
Actuary
Actuaries use their knowledge of mathematics and statistics to assess risk and financial implications.
Auditor
Auditor
Auditors examine financial records to ensure that they are accurate and compliant with regulations.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks that could impact an organization.
Compliance Officer
Compliance Officer
Compliance Officers ensure that an organization complies with laws and regulations.
For more career information including salaries, visit:
OpenCourser.com/course/7dbc6m/process
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Process Data from Dirty to Clean.
Provides a comprehensive overview of data science, including data cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about data science and its applications in business.
Provides a comprehensive overview of data science, including data cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about data science and its applications in various fields.
Save
Provides a practical guide to data cleaning, including techniques for identifying and correcting errors in data. It valuable resource for anyone who wants to learn more about data cleaning and its importance in data analysis.
Provides a comprehensive overview of machine learning, including data cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about machine learning and its applications in various fields.
Provides a comprehensive overview of PyTorch, including data cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about PyTorch and its applications in various fields.
Save
Provides a comprehensive overview of SQL, including how to use SQL to clean and analyze data. It valuable resource for anyone who wants to learn more about SQL and its applications in data analysis.
Provides a comprehensive overview of data manipulation with R, including how to use R to clean and analyze data. It valuable resource for anyone who wants to learn more about R and its applications in data analysis.
Provides a comprehensive overview of data analysis with Python, including how to use Python to clean and analyze data. It valuable resource for anyone who wants to learn more about Python and its applications in data analysis.
Provides a comprehensive overview of deep learning, including data cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about deep learning and its applications in various fields.
Provides a comprehensive overview of machine learning for data science, including how to use machine learning to clean and analyze data. It valuable resource for anyone who wants to learn more about machine learning and its applications in data analysis.
Save
Provides a comprehensive overview of data visualization, including how to use data visualization to communicate insights from data. It valuable resource for anyone who wants to learn more about data visualization and its applications in data analysis.
Save
Provides a comprehensive overview of deep learning for data science, including how to use deep learning to clean and analyze data. It valuable resource for anyone who wants to learn more about deep learning and its applications in data analysis.
Save
Provides a comprehensive overview of storytelling with data, including how to use data to create compelling and persuasive stories. It valuable resource for anyone who wants to learn more about storytelling with data and its applications in data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7dbc6m/process
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Level
Introductory
Via
Coursera
Institution
Google
Instructor
Google Career Certificates
Language
English
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Good to know
Explores industry-standard methods and tools for data cleaning
Teaches SQL and spreadsheet data cleaning techniques used by current Google data analysts
Develops skills for checking data integrity, cleaning data, and reporting data cleaning results
Taught by expert Google data analysts, ensuring relevance and accuracy
Provides hands-on practice with real-world data cleaning tasks
Part of the Google Data Analytics Certificate program, indicating comprehensiveness and structure
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