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This is the third course in the Google Data Analytics Certificate. As you continue to build on your understanding of the topics from the first two courses, you’ll 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.
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This is the third course in the Google Data Analytics Certificate. As you continue to build on your understanding of the topics from the first two courses, you’ll 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.
Read more
This is the third course in the Google Data Analytics Certificate. As you continue to build on your understanding of the topics from the first two courses, you’ll 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.
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:
- Find out how analysts decide what data to collect for analysis.
- Learn about structured and unstructured data, data types, and data formats.
- Discover how to identify different types of bias in data to help ensure data credibility.
- Explore how analysts use spreadsheets and SQL within databases and data sets.
- Examine open data and the relationship between, and importance of, data ethics and data privacy.
- Gain an understanding of how to access databases and extract, filter, and sort the data they contain.
- Learn best practices for organizing data and keeping it secure.
Register for this course and see more details by visiting:
OpenCourser.com/course/40ik80/prepare
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What's inside
Syllabus
Data types and structures
A massive amount of data is generated every single day. In this part of the course, you will discover how this data is generated and how analysts decide which data to use for analysis. You’ll also learn about structured and unstructured data, data types, and data formats as you start thinking about how to prepare your data for analysis.
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Syllabus
Data types and structures
A massive amount of data is generated every single day. In this part of the course, you will discover how this data is generated and how analysts decide which data to use for analysis. You’ll also learn about structured and unstructured data, data types, and data formats as you start thinking about how to prepare your data for analysis.
Read more
Data responsibility
Before you work with data, you must confirm that it is unbiased and credible. After all, if you start your analysis with unreliable data, you won’t be able to trust your results. In this part of the course, you will learn to identify bias in data and to ensure your data is credible. You’ll also explore open data and the importance of data ethics and data privacy.
Database essentials
When you analyze large datasets, you’ll access much of the data from a database. In this part of the course, you will learn about databases, including how to access them and extract, filter, and sort the data they contain. You’ll also explore metadata to discover its many facets and how analysts use it to better understand their data.
Organize and protect data
Good organizational skills are a big part of most types of work, especially data analytics. In this part of the course, you will learn best practices for organizing data and keeping it secure. You’ll also understand how analysts use file naming conventions to help them keep their work organized.
Engage in the data community
Having a strong online presence can be a big help for job seekers of all kinds. In this part of the course, you will explore how to manage your online presence. You’ll also discover the benefits of networking with other data analytics professionals.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by experienced data analysts from Google, who are recognized for their work in this field
Develops practical data analytics skills, including data extraction, organization, and protection
Builds a strong foundation in data analysis for beginners, providing a clear starting point
Emphasizes data credibility and ethics, ensuring learners can work with reliable data
Includes hands-on exercises and real-world examples, providing practical experience
Requires no prior experience, making it accessible to learners from various backgrounds
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Reviews summary
Data wrangling: preparing a dataset
The course titled "Prepare Data for Exploration" is the third in a series of courses that form the Google Data Analytics Professional Certificate, offered through Coursera. This course focuses on the essential techniques and tools for preparing data for exploration and analysis. It covers a wide range of topics, including data wrangling, data cleaning, data transformation, feature engineering, and dealing with missing values. The course is designed for beginners with little to no prior experience in data analysis, and it provides a practical, hands-on approach to learning the material. Through interactive exercises, quizzes, and assignments, learners gain experience with real-world datasets and industry-standard tools such as Google Sheets, SQL, and Python. The course also emphasizes the importance of understanding the ethical implications of data collection and use, as well as the value of building a professional network and online presence. Upon completion of the course, learners will have a solid foundation in data preparation techniques and be well-equipped to pursue further studies or careers in data analysis.
The course extensively covers various tools and techniques commonly used in data preparation, including Google Sheets, SQL, and Python. I appreciated the practical guidance on how to leverage these tools effectively for data exploration purposes.
"The course extensively covered various tools and techniques commonly used in data preparation, including Google Sheets, SQL, and Python."
"I appreciated the practical guidance on how to leverage these tools effectively for data exploration purposes."
I appreciated the hands-on nature of the course. The assignments and quizzes were well-designed and allowed me to apply the concepts learned in a practical manner. The step-by-step instructions and the provided datasets were instrumental in helping me grasp the techniques effectively.
"I appreciated the hands-on nature of the course."
"The assignments and quizzes were well-designed and allowed me to apply the concepts learned in a practical manner."
"The step-by-step instructions and the provided datasets were instrumental in helping me grasp the techniques effectively."
The course also emphasizes the importance of understanding the ethical implications of data collection and use, as well as the value of building a professional network and online presence.
"The course also emphasizes the importance of understanding the ethical implications of data collection and use, as well as the value of building a professional network and online presence."
The course covered a wide range of topics related to data preparation, including data cleaning, data transformation, feature engineering, and dealing with missing values. This comprehensive approach ensured that I gained a holistic understanding of the subject matter.
"The course covered a wide range of topics related to data preparation, including data cleaning, data transformation, feature engineering, and dealing with missing values."
"This comprehensive approach ensured that I gained a holistic understanding of the subject matter."
The course instructor did an excellent job of explaining complex concepts in a clear and concise manner. The videos were engaging, and the use of real-life examples helped me understand the concepts better.
"The course instructor did an excellent job of explaining complex concepts in a clear and concise manner."
"The videos were engaging, and the use of real-life examples helped me understand the concepts better."
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 Prepare Data for Exploration with these
activities:
Read "Data Analytics Made Accessible" by Anil Maheshwari
Show steps
This book provides a comprehensive overview of data analytics concepts and techniques, making it a valuable resource for anyone looking to build a foundation in this field.
View
Big Data: Second Edition
on Amazon
Show steps
-
Purchase or borrow a copy of the book.
-
Allocate time in your schedule to read the book.
-
Take notes and highlight important concepts as you read.
Review SQL
Show steps
Help you refresh your understanding of SQL syntax and concepts, making it easier to follow along with the course material.
Browse courses on
SQL
Show steps
-
Review basic SQL commands such as SELECT, INSERT, UPDATE, and DELETE.
-
Practice writing queries to retrieve data from a database.
-
Review how to use SQL to create and modify tables.
Refine database fundamentals
Show steps
Reviewing basic database concepts will help you build a better foundation for the more complex topics covered in this course.
Browse courses on
SQL
Show steps
-
Review your notes or a textbook on SQL syntax and commands.
-
Practice writing SQL queries to extract and filter data from simple tables.
-
Consider taking an online tutorial or completing some practice problems to reinforce your skills.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Form a study group
Show steps
Collaborating with peers can enhance your understanding of the course material and provide support.
Show steps
-
Find a group of classmates who are willing to meet regularly to discuss the course material.
-
Set up a regular meeting time and location.
-
Take turns leading the discussions and presenting your findings.
Data analysis practice problems
Show steps
Completing practice problems will help you develop your ability to apply data analysis techniques and algorithms to real-world scenarios.
Browse courses on
Data Analysis
Show steps
-
Find a set of practice problems online or in a textbook.
-
Work through the problems step-by-step, following the instructions carefully.
-
Check your answers against the provided solutions or consult with a tutor or instructor if you get stuck.
Learn advanced SQL techniques
Show steps
Expanding your knowledge of SQL will enable you to handle more complex data analysis tasks.
Browse courses on
SQL
Show steps
-
Find an online tutorial or course that covers advanced SQL topics.
-
Follow the tutorial step-by-step, practicing the techniques and completing any exercises provided.
-
Apply your new skills to a real-world data analysis project.
Attend a data analytics workshop
Show steps
Workshops provide an opportunity to learn from experts and network with other professionals in the field.
Browse courses on
Data Analytics
Show steps
-
Research upcoming data analytics workshops in your area or online.
-
Register for a workshop that aligns with your interests and learning goals.
-
Attend the workshop and actively participate in the activities and discussions.
Build a data visualization dashboard
Show steps
Creating a data visualization dashboard will allow you to practice your data analysis skills and present your findings in a clear and visually appealing way.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset that you are interested in analyzing.
-
Use a data visualization tool to create a dashboard that displays the key insights from your analysis.
-
Share your dashboard with others and get feedback on your work.
Contribute to an open-source data analytics project
Show steps
Contributing to an open-source project will give you hands-on experience with real-world data analytics problems and allow you to learn from others in the field.
Browse courses on
Data Analytics
Show steps
-
Find an open-source data analytics project that you are interested in contributing to.
-
Read the project's documentation and familiarize yourself with its codebase.
-
Make a contribution to the project, such as fixing a bug or adding a new feature.
Read "Data Analytics Made Accessible" by Anil Maheshwari
Show steps
This book provides a comprehensive overview of data analytics concepts and techniques, making it a valuable resource for anyone looking to build a foundation in this field.
View
Big Data: Second Edition
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of the book.
- Allocate time in your schedule to read the book.
- Take notes and highlight important concepts as you read.
Review SQL
Show steps
Help you refresh your understanding of SQL syntax and concepts, making it easier to follow along with the course material.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Review basic SQL commands such as SELECT, INSERT, UPDATE, and DELETE.
- Practice writing queries to retrieve data from a database.
- Review how to use SQL to create and modify tables.
Refine database fundamentals
Show steps
Reviewing basic database concepts will help you build a better foundation for the more complex topics covered in this course.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Review your notes or a textbook on SQL syntax and commands.
- Practice writing SQL queries to extract and filter data from simple tables.
- Consider taking an online tutorial or completing some practice problems to reinforce your skills.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Form a study group
Show steps
Collaborating with peers can enhance your understanding of the course material and provide support.
Show steps
Show steps
Show steps
- Find a group of classmates who are willing to meet regularly to discuss the course material.
- Set up a regular meeting time and location.
- Take turns leading the discussions and presenting your findings.
Data analysis practice problems
Show steps
Completing practice problems will help you develop your ability to apply data analysis techniques and algorithms to real-world scenarios.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a set of practice problems online or in a textbook.
- Work through the problems step-by-step, following the instructions carefully.
- Check your answers against the provided solutions or consult with a tutor or instructor if you get stuck.
Learn advanced SQL techniques
Show steps
Expanding your knowledge of SQL will enable you to handle more complex data analysis tasks.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Find an online tutorial or course that covers advanced SQL topics.
- Follow the tutorial step-by-step, practicing the techniques and completing any exercises provided.
- Apply your new skills to a real-world data analysis project.
Attend a data analytics workshop
Show steps
Workshops provide an opportunity to learn from experts and network with other professionals in the field.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Research upcoming data analytics workshops in your area or online.
- Register for a workshop that aligns with your interests and learning goals.
- Attend the workshop and actively participate in the activities and discussions.
Build a data visualization dashboard
Show steps
Creating a data visualization dashboard will allow you to practice your data analysis skills and present your findings in a clear and visually appealing way.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset that you are interested in analyzing.
- Use a data visualization tool to create a dashboard that displays the key insights from your analysis.
- Share your dashboard with others and get feedback on your work.
Contribute to an open-source data analytics project
Show steps
Contributing to an open-source project will give you hands-on experience with real-world data analytics problems and allow you to learn from others in the field.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Find an open-source data analytics project that you are interested in contributing to.
- Read the project's documentation and familiarize yourself with its codebase.
- Make a contribution to the project, such as fixing a bug or adding a new feature.
Career center
Learners who complete Prepare Data for Exploration will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is a professional who uses data to help organizations make better decisions. They collect, clean, and analyze data to identify trends and patterns that can be used to improve business operations. This course can help you develop the skills you need to be a successful Data Analyst by teaching you how to find, organize, and analyze data. You will also learn about data ethics and privacy, which are important considerations for anyone working with data.
Business Analyst
Business Analyst
A Business Analyst is a professional who helps organizations improve their business processes. They work with stakeholders to identify problems, analyze data, and develop solutions. This course can help you develop the skills you need to be a successful Business Analyst by teaching you how to find, organize, and analyze data. You will also learn about data ethics and privacy, which are important considerations for anyone working with data.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses data to solve complex problems. They use statistical and machine learning techniques to build models that can predict future outcomes. This course can help you develop the skills you need to be a successful Data Scientist by teaching you how to find, organize, and analyze data. You will also learn about data ethics and privacy, which are important considerations for anyone working with data.
Database Administrator
Database Administrator
A Database Administrator is a professional who manages and maintains databases. They ensure that data is stored and organized in a way that makes it easy to access and use. This course can help you develop the skills you need to be a successful Database Administrator by teaching you how to access databases and extract, filter, and sort the data they contain. You will also learn about data ethics and privacy, which are important considerations for anyone working with data.
Software Engineer
Software Engineer
A Software Engineer is a professional who designs, develops, and maintains software applications. They use their knowledge of programming languages and software development tools to create software that meets the needs of users. This course may be helpful for Software Engineers who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for developing software applications that use data.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze financial data. They use their knowledge of finance and mathematics to make investment decisions. This course may be helpful for Quantitative Analysts who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for developing financial models.
Market Researcher
Market Researcher
A Market Researcher is a professional who conducts research to understand consumer behavior. They use surveys, interviews, and other research methods to collect data about consumer needs and wants. This course may be helpful for Market Researchers who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for understanding consumer behavior.
Product Manager
Product Manager
A Product Manager is a professional who is responsible for the development and marketing of a product. They work with engineers, designers, and marketers to create a product that meets the needs of users. This course may be helpful for Product Managers who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for understanding user needs and developing successful products.
Data Engineer
Data Engineer
A Data Engineer is a professional who designs and builds data pipelines. They work with data scientists and other data professionals to ensure that data is available and accessible for analysis. This course may be helpful for Data Engineers who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for building data pipelines.
Statistician
Statistician
A Statistician is a professional who uses statistical methods to collect, analyze, and interpret data. They work with researchers and other professionals to solve problems and make informed decisions. This course may be helpful for Statisticians who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for solving problems and making informed decisions.
Epidemiologist
Epidemiologist
An Epidemiologist is a professional who studies the distribution and determinants of disease in populations. They use statistical methods to analyze data and identify risk factors for disease. This course may be helpful for Epidemiologists who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for studying the distribution and determinants of disease.
Financial Analyst
Financial Analyst
A Financial Analyst is a professional who analyzes financial data to make investment decisions. They use their knowledge of finance and economics to make recommendations on which stocks, bonds, and other investments to buy or sell. This course may be helpful for Financial Analysts who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for making investment decisions.
Actuary
Actuary
An Actuary is a professional who uses mathematical and statistical methods to assess risk. They work with insurance companies and other financial institutions to develop and manage insurance policies. This course may be helpful for Actuaries who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for assessing risk.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is a professional who uses mathematical and statistical methods to improve the efficiency of operations. They work with businesses and organizations to solve problems and make decisions. This course may be helpful for Operations Research Analysts who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for solving problems and making decisions.
Data Mining Analyst
Data Mining Analyst
A Data Mining Analyst is a professional who uses data mining techniques to find patterns and trends in data. They work with businesses and organizations to identify opportunities and solve problems. This course may be helpful for Data Mining Analysts who want to learn more about data analysis. The course will teach you how to find, organize, and analyze data, which can be useful for finding patterns and trends in data.
For more career information including salaries, visit:
OpenCourser.com/course/40ik80/prepare
Reading list
We've selected 11 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
Prepare Data for Exploration.
Provides a comprehensive overview of data science concepts and techniques. It covers topics such as data mining, machine learning, and statistical modeling. It valuable resource for learners who want to gain a deeper understanding of the field of data science.
Provides a comprehensive guide to the principles and practices of open data. It covers topics such as data sharing, data licensing, and data governance. It valuable resource for learners who want to gain a deeper understanding of the open data ecosystem.
Provides a comprehensive overview of database systems. It covers topics such as data models, query processing, and transaction management. It valuable resource for learners who want to gain a deeper understanding of the underlying principles of database systems.
Save
Provides a practical introduction to data visualization. It covers topics such as data visualization principles, chart types, and data visualization tools. It valuable resource for learners who want to develop their skills in data visualization.
Provides a comprehensive introduction to Python for data analysis. It covers topics such as data manipulation, data visualization, and machine learning. It valuable resource for learners who want to develop their skills in Python for data analysis.
Save
Provides a comprehensive introduction to R for data science. It covers topics such as data manipulation, data visualization, and machine learning. It valuable resource for learners who want to develop their skills in R for data science.
Save
Provides a comprehensive overview of data mining techniques. It covers topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for learners who want to gain a deeper understanding of the field of data mining.
Provides a comprehensive introduction to machine learning for data science. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for learners who want to develop their skills in machine learning for data science.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for learners who want to gain a deeper understanding of the field of deep learning.
Provides a practical introduction to data science from scratch. It covers topics such as data cleaning, data analysis, and data visualization. It valuable resource for learners who want to gain a hands-on understanding of the field of data science.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for learners who want to develop their skills in machine learning using these popular tools.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/40ik80/prepare
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Level
Introductory
Via
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Institution
Google
Instructor
Google Career Certificates
Language
English
Transcripts
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Deutsch
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Good to know
Taught by experienced data analysts from Google, who are recognized for their work in this field
Develops practical data analytics skills, including data extraction, organization, and protection
Builds a strong foundation in data analysis for beginners, providing a clear starting point
Emphasizes data credibility and ethics, ensuring learners can work with reliable data
Includes hands-on exercises and real-world examples, providing practical experience
Requires no prior experience, making it accessible to learners from various backgrounds
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