Course Summary
Learn how to prepare and clean data for analysis using a variety of tools and techniques with this comprehensive course on data preparation.Key Learning Points
- Understand the importance of data preparation in the analysis process
- Learn how to clean and transform data using various tools and techniques
- Master the art of data visualization and communication
Related Topics for further study
Learning Outcomes
- Understand the importance of data preparation in the analysis process
- Learn how to clean and transform data using various tools and techniques
- Master the art of data visualization and communication
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and data analysis
- Familiarity with data analysis tools like Excel or R
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Assignments
- Quizzes
Similar Courses
- Data Analysis and Interpretation
- Applied Data Science with Python
- Statistics with R
Related Education Paths
Notable People in This Field
- Nate Silver
- Hans Rosling
Related Books
Description
This is the third course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. As you continue to build on your understanding of the topics from the first two courses, you’ll also 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.
Knowledge
- Explain 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
Outline
- Data types and structures
- Introduction to data exploration
- Hallie: Fascinating data insights
- Data collection in our world
- Determining what data to collect
- Discover data formats
- Understanding structured data
- Know the type of data you're working with
- Data table components
- Meet wide and long data
- Course syllabus
- Deciding if you should take the speed track
- Optional: Your diagnostic quiz score and what it means
- Selecting the right data
- Data formats in practice
- The structure of data
- Data modeling levels and techniques
- Understanding Boolean logic
- Transforming data
- Glossary: Terms and definitions
- Optional: Familiar with data analytics? Take our diagnostic quiz
- Test your knowledge on collecting data
- Self-Reflection: Unstructured data
- Test your knowledge on data formats and structures
- Hands-On Activity: Applying a function
- Hands-on Activity: Introduction to Kaggle
- Test your knowledge on data types, fields, and values
- *Weekly challenge 1*
- Bias, credibility, privacy, ethics, and access
- Ensuring data integrity
- Bias: From questions to conclusions
- Biased and unbiased data
- Understanding bias in data
- Identifying good data sources
- What is "bad" data?
- Introduction to data ethics
- Optional Refresher: Alex: The importance of data ethics
- Introduction to data privacy
- Andrew: The ethical use of data
- Features of open data
- Andrew: Steps for ethical data use
- Data anonymization
- The open-data debate
- Sites and resources for open data
- Glossary: Terms and definitions
- Test your knowledge on unbiased and objective data
- Test your knowledge on data credibility
- Test your knowledge on data ethics and privacy
- Hands-On Activity: Kaggle datasets
- Test your knowledge on open data
- *Weekly challenge 2*
- Databases: Where data lives
- All about databases
- Database features
- Exploring metadata
- Using metadata as an analyst
- Metadata management
- Megan: Fun with metadata
- Working with more data sources
- Importing data from spreadsheets and databases
- Sorting and filtering
- Setting up BigQuery, including sandbox and billing options
- How to use BigQuery
- BigQuery in action
- Databases in data analytics
- Inspecting a dataset: A guided, hands-on tour
- Metadata is as important as the data itself
- From external source to a spreadsheet
- Exploring public datasets
- Using BigQuery
- In-depth guide: SQL best practices
- Glossary: Terms and definitions
- Test your knowledge on working with databases
- Test your knowledge on metadata
- Test your knowledge on accessing data sources
- Hands-On Activity: Clean data in spreadsheets with sorting and filtering
- Self-Reflection: Considering databases and spreadsheets for sorting and filtering
- Test your knowledge on sorting and filtering
- Hands-On Activity: Introduction to BigQuery
- Hands-On Activity: Create a custom table in BigQuery
- Hands-On Activity: Applying SQL
- Test your knowledge on using SQL with large datasets
- *Weekly challenge 3*
- Organizing and protecting your data
- Feel confident in your data
- Let's get organized
- All about file naming
- Security features in spreadsheets
- Organization guidelines
- Learning Log: Review file structure and naming conventions
- Balancing security and analytics
- Glossary: Terms and definitions
- Test your knowledge on how to organize data
- Self-Reflection: Protecting your resources
- Test your knowledge on securing your data
- *Weekly challenge 4*
- Optional: Engaging in the data community
- Managing your presence as a data analyst
- Why an online presence is important
- Tips for enhancing your online presence
- Networking know-how
- Benefits of mentorship
- Rachel: Mentors are key
- Getting started with LinkedIn
- Building connections on LinkedIn
- Developing a network
- Self-Reflection: Adding Kaggle to your online presence
- *Course challenge*
- Congrats! Course wrap-up
- Glossary: Terms and definitions
- *Course challenge*
Summary of User Reviews
Discover the art of Data Preparation with this comprehensive course on Coursera. Students rave about the dynamic presentations and practical exercises that make learning easy and fun. The course has received high praise overall from users.Key Aspect Users Liked About This Course
The hands-on exercises were particularly helpful for many students, allowing them to apply the concepts learned in real-world situations.Pros from User Reviews
- The course is well-structured and easy to follow, even for beginners.
- The instructors are knowledgeable and engaging, making the material accessible and interesting.
- The practical exercises are relevant and useful, helping students to apply the concepts learned in real-world situations.
- The course covers a wide range of topics and is comprehensive in its approach.
- The course is self-paced, allowing students to learn at their own pace and on their own schedule.
Cons from User Reviews
- Some users found the course material to be too basic and not challenging enough.
- The course could benefit from more advanced topics and exercises for experienced users.
- Some users found the assessments to be too easy and not reflective of the material covered in the course.
- The course could benefit from more interaction with instructors and other students.
- Some users found the course to be too expensive compared to other online courses available.
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