Course Summary
Learn how to process and analyze data using Python and its libraries. This course covers data cleaning, visualization, and statistical analysis using real-world datasets.Key Learning Points
- Learn to work with data using Python and its libraries
- Clean and preprocess data for analysis
- Visualize data using various techniques
- Perform statistical analysis on datasets
Job Positions & Salaries of people who have taken this course might have
- USA: $65,000 - $110,000
- India: ₹350,000 - ₹1,000,000
- Spain: €25,000 - €50,000
- USA: $65,000 - $110,000
- India: ₹350,000 - ₹1,000,000
- Spain: €25,000 - €50,000
- USA: $85,000 - $150,000
- India: ₹500,000 - ₹2,000,000
- Spain: €35,000 - €70,000
- USA: $65,000 - $110,000
- India: ₹350,000 - ₹1,000,000
- Spain: €25,000 - €50,000
- USA: $85,000 - $150,000
- India: ₹500,000 - ₹2,000,000
- Spain: €35,000 - €70,000
- USA: $70,000 - $125,000
- India: ₹400,000 - ₹1,500,000
- Spain: €30,000 - €60,000
Related Topics for further study
Learning Outcomes
- Understand the basics of data processing and analysis
- Learn to use Python and its libraries for data analysis
- Gain experience in cleaning, visualizing, and analyzing real-world datasets
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with data structures and algorithms
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Data Science Essentials
Related Education Paths
Related Books
Description
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.
Knowledge
- Define data integrity with reference to types of integrity and risk to data integrity
- Apply basic SQL functions for use in cleaning string variables in a database
- Develop basic SQL queries for use on databases
- Describe the process involved in verifying the results of cleaning data
Outline
- The importance of integrity
- Introduction to focus on integrity
- 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 margin of error
- Weekly challenge 1
- Sparkling-clean data
- Clean it up!
- Why data cleaning is important
- Angie: Why I love cleaning data
- Recognize and remedy 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 data with spreadsheets
- Test your knowledge on data-cleaning techniques
- Hands-On Activity: Clean data with spreadsheet functions
- 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
- Optional: Upload the customer dataset to BigQuery
- Optional: Upload the store transactions dataset to BigQuery
- Glossary: Terms and definitions
- Hands-On Activity: Processing time with SQL
- Test your knowledge on SQL
- Hands-On Activity: Clean data using 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 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: Black and African American 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
- Course challenge
Summary of User Reviews
Find out what users are saying about the Process Data course on Coursera. Overall, users have praised the course for its comprehensive approach to data processing. One key aspect that many users thought was good is the course's focus on real-world applications of data processing techniques.Pros from User Reviews
- The course provides a comprehensive overview of data processing techniques
- The course offers real-world applications of data processing techniques
- The course is taught by knowledgeable and experienced instructors
- The course provides hands-on experience with data processing tools and techniques
Cons from User Reviews
- Some users found the course content to be too basic
- Some users found the course to be too theoretical
- Some users found the assignments to be too difficult or time-consuming
Google Career CertificatesTop Instructor
4.8 Raiting
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