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Tricia Bagley
Survey data sets are often deceptively complex because surveys collect a wide variety of data covering a wide variety of topics and experiences. To further the complexity of survey data, the respondents answering the questions come from a wide variety of backgrounds and stages in their customer journey. It is reasonable that it would be a challenge to boil down survey data into actionable insights because it can be deceptively complex.
With large sets of data, Principal Component Analysis or PCA is a useful tool that reduces and transforms variables to a leaner form that allows for a speedier analysis. In this project you will...
Read more
Survey data sets are often deceptively complex because surveys collect a wide variety of data covering a wide variety of topics and experiences. To further the complexity of survey data, the respondents answering the questions come from a wide variety of backgrounds and stages in their customer journey. It is reasonable that it would be a challenge to boil down survey data into actionable insights because it can be deceptively complex.
With large sets of data, Principal Component Analysis or PCA is a useful tool that reduces and transforms variables to a leaner form that allows for a speedier analysis. In this project you will...
Read more
Survey data sets are often deceptively complex because surveys collect a wide variety of data covering a wide variety of topics and experiences. To further the complexity of survey data, the respondents answering the questions come from a wide variety of backgrounds and stages in their customer journey. It is reasonable that it would be a challenge to boil down survey data into actionable insights because it can be deceptively complex.
With large sets of data, Principal Component Analysis or PCA is a useful tool that reduces and transforms variables to a leaner form that allows for a speedier analysis. In this project you will gain hands-on experience with the principles of Principal Component Analysis using survey data. To do this you will work in the free-to-use spreadsheet software Google Sheets.
By the end of this project, you will be able to confidently apply Principal Component Analysis concepts to transform large sets of variables into a leaner set of data that still contains the most relevant information.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/94pv4s/analyze
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Uses Principal Component Analysis to simplify and analyze survey data, making it relevant for professionals working with customer feedback
Employs Google Sheets, a familiar tool, for hands-on practice, making the course accessible to a wide range of learners
Focuses on practical applications, providing valuable skills for those seeking to extract insights from survey data
Designed for learners based in North America, limiting its accessibility for those in other regions
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Reviews summary
Pca analysis using google sheets
While the earlier lessons of this course are very basic and may not be suitable for experienced professionals, this course does a good job of introducing how to apply PCA concepts to survey data in Google Sheets.
This course is offered in a beginner-friendly manner.
"The course was simpler than I imagined."
"However, it was interesting to learn about the tool and its possible uses."
Course is very basic.
"I feel that this project was a little slow, it took I think 10 minutes to actually begin working."
"This is best suited for beginners, as it covers very basic materials."
Using Google Sheets is not the best way to learn PCA.
"Using Google Sheets, in my opinion, is not a good idea."
"Perhaps other students found this useful, but I was deeply disappointed."
This course does not use PCA correctly.
"This is NOT a PCA course, it doesn´t use PCA at all - it uses just an average!"
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 Analyze Survey Data using Principal Component Analysis with these
activities:
Review Survey Data Concepts
Show steps
Refresh your understanding of the fundamental concepts of survey data to better grasp the concepts that PCA will build on.
Browse courses on
Surveys
Show steps
-
Read through the course syllabus and identify key concepts in the Survey Data Concepts section.
-
Review your notes or study materials from previous courses or textbooks on survey data.
-
Complete any practice questions or exercises related to survey data concepts.
Practice PCA Calculations
Show steps
Reinforce your understanding of PCA by working through a series of calculations and simulations.
Show steps
-
Find a set of practice problems or simulations related to PCA.
-
Work through the problems or simulations, taking your time to understand the steps involved.
-
Check your answers against provided solutions or ask for help in discussion forums.
Explore PCA Applications
Show steps
Expand your knowledge of PCA by exploring its applications in various fields.
Show steps
-
Identify different industries or domains where PCA is commonly used.
-
Find articles, case studies, or tutorials that demonstrate how PCA has been applied in those fields.
-
Summarize your findings and discuss the benefits and challenges of using PCA in those contexts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a PCA Workshop
Show steps
Gain hands-on experience with PCA by attending a workshop led by experts.
Show steps
-
Research and identify PCA workshops that align with your learning goals.
-
Register for a workshop and prepare any necessary materials.
-
Attend the workshop and actively participate in the exercises and discussions.
Mentor Students in PCA
Show steps
Reinforce your understanding of PCA by sharing your knowledge and assisting other students.
Show steps
-
Offer your help as a mentor in PCA-related discussion forums or online communities.
-
Answer questions, provide guidance, and share resources with individuals seeking support in PCA.
-
Organize or participate in study groups to facilitate collaborative learning.
Analyze a Survey Dataset Using PCA
Show steps
Demonstrate your proficiency in PCA by applying it to a real-world survey dataset and interpreting the results.
Browse courses on
Survey Data Analysis
Show steps
-
Obtain a survey dataset that is relevant to your interests or career goals.
-
Clean and prepare the data for PCA analysis.
-
Apply PCA to the data and interpret the results, identifying the key components and their significance.
-
Create a presentation or report to showcase your analysis and insights.
Contribute to a PCA Open-Source Project
Show steps
Enhance your understanding of PCA and make a contribution to the field by participating in open-source development.
Show steps
-
Identify open-source projects related to PCA that align with your interests.
-
Review the project documentation and identify areas where you can contribute.
-
Make code contributions, report bugs, or provide documentation updates.
Participate in a PCA Challenge
Show steps
Test your PCA skills and knowledge by participating in a competition or challenge.
Show steps
-
Identify PCA challenges or competitions that are relevant to your interests.
-
Form a team or work individually to develop a solution to the challenge.
-
Submit your solution and participate in the evaluation process.
Review Survey Data Concepts
Show steps
Refresh your understanding of the fundamental concepts of survey data to better grasp the concepts that PCA will build on.
Browse courses on
Surveys
Show steps
Show steps
Show steps
- Read through the course syllabus and identify key concepts in the Survey Data Concepts section.
- Review your notes or study materials from previous courses or textbooks on survey data.
- Complete any practice questions or exercises related to survey data concepts.
Practice PCA Calculations
Show steps
Reinforce your understanding of PCA by working through a series of calculations and simulations.
Show steps
Show steps
Show steps
- Find a set of practice problems or simulations related to PCA.
- Work through the problems or simulations, taking your time to understand the steps involved.
- Check your answers against provided solutions or ask for help in discussion forums.
Explore PCA Applications
Show steps
Expand your knowledge of PCA by exploring its applications in various fields.
Show steps
Show steps
Show steps
- Identify different industries or domains where PCA is commonly used.
- Find articles, case studies, or tutorials that demonstrate how PCA has been applied in those fields.
- Summarize your findings and discuss the benefits and challenges of using PCA in those contexts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a PCA Workshop
Show steps
Gain hands-on experience with PCA by attending a workshop led by experts.
Show steps
Show steps
Show steps
- Research and identify PCA workshops that align with your learning goals.
- Register for a workshop and prepare any necessary materials.
- Attend the workshop and actively participate in the exercises and discussions.
Mentor Students in PCA
Show steps
Reinforce your understanding of PCA by sharing your knowledge and assisting other students.
Show steps
Show steps
Show steps
- Offer your help as a mentor in PCA-related discussion forums or online communities.
- Answer questions, provide guidance, and share resources with individuals seeking support in PCA.
- Organize or participate in study groups to facilitate collaborative learning.
Analyze a Survey Dataset Using PCA
Show steps
Demonstrate your proficiency in PCA by applying it to a real-world survey dataset and interpreting the results.
Browse courses on
Survey Data Analysis
Show steps
Show steps
Show steps
- Obtain a survey dataset that is relevant to your interests or career goals.
- Clean and prepare the data for PCA analysis.
- Apply PCA to the data and interpret the results, identifying the key components and their significance.
- Create a presentation or report to showcase your analysis and insights.
Contribute to a PCA Open-Source Project
Show steps
Enhance your understanding of PCA and make a contribution to the field by participating in open-source development.
Show steps
Show steps
Show steps
- Identify open-source projects related to PCA that align with your interests.
- Review the project documentation and identify areas where you can contribute.
- Make code contributions, report bugs, or provide documentation updates.
Participate in a PCA Challenge
Show steps
Test your PCA skills and knowledge by participating in a competition or challenge.
Show steps
Show steps
Show steps
- Identify PCA challenges or competitions that are relevant to your interests.
- Form a team or work individually to develop a solution to the challenge.
- Submit your solution and participate in the evaluation process.
Career center
Learners who complete Analyze Survey Data using Principal Component Analysis will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts play a crucial role in understanding customer behavior and making informed decisions. Principal Component Analysis (PCA) is a powerful tool that Data Analysts use to analyze large survey datasets, identify patterns, and extract meaningful insights. By taking this course, you'll gain hands-on experience with PCA, which will enhance your ability to analyze survey data effectively and contribute to data-driven decision-making.
Market Research Analyst
Market Research Analyst
Market Research Analysts use survey data to understand consumer preferences and trends. PCA is a valuable tool for Market Research Analysts as it helps them reduce complex survey data into more manageable and interpretable insights. This course will provide you with a solid foundation in PCA, enabling you to conduct comprehensive market research studies and make well-informed recommendations.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers apply statistical and mathematical techniques to analyze data. PCA is a key tool for Quantitative Researchers as it helps them identify patterns and relationships in large datasets. This course will equip you with the skills to use PCA effectively, giving you an edge in the field of quantitative research.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. PCA is a valuable tool for Business Analysts as it helps them understand the underlying factors driving business performance. By taking this course, you'll gain a deep understanding of PCA, enabling you to contribute to data-driven decision-making and improve business outcomes.
Data Scientist
Data Scientist
Data Scientists use a variety of techniques, including PCA, to analyze data and solve complex problems. This course will provide you with a strong foundation in PCA, enabling you to extract meaningful insights from large survey datasets. By combining PCA with other data science techniques, you'll be well-equipped to make significant contributions in the field.
Statistical Analyst
Statistical Analyst
Statistical Analysts use statistical techniques to analyze data and provide insights. PCA is a key technique for Statistical Analysts, as it helps them identify patterns and trends in large datasets. This course will provide you with a solid understanding of PCA, enabling you to contribute to data-driven decision-making and improve statistical analysis outcomes.
Survey Researcher
Survey Researcher
Survey Researchers design and administer surveys to collect data from target populations. PCA is a valuable tool for Survey Researchers as it helps them analyze survey data effectively and extract meaningful insights. This course will provide you with a comprehensive understanding of PCA, enabling you to design and conduct surveys that yield valuable and actionable insights.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers apply machine learning techniques to solve real-world problems. PCA is a useful technique for Machine Learning Engineers as it helps them reduce dimensionality and improve the performance of machine learning models. This course will provide you with a solid foundation in PCA, enabling you to build and deploy machine learning models that leverage survey data effectively.
Product Manager
Product Manager
Product Managers use data to understand customer needs and develop successful products. PCA is a valuable tool for Product Managers as it helps them identify patterns and trends in survey data, which can inform product design and development decisions. This course will provide you with a practical understanding of PCA, enabling you to make data-driven decisions and improve product outcomes.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify and solve business problems. PCA is a useful technique for Business Intelligence Analysts as it helps them reduce dimensionality and extract meaningful insights from large datasets. This course will provide you with a foundation in PCA, enabling you to contribute to data-driven decision-making and improve business outcomes.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. PCA is a valuable tool for Quantitative Analysts as it helps them identify patterns and relationships in large datasets. This course will provide you with a strong foundation in PCA, enabling you to make informed investment decisions and contribute to financial modeling.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems. PCA is a valuable tool for Operations Research Analysts as it helps them identify patterns and relationships in large datasets. This course will provide you with a foundation in PCA, enabling you to contribute to data-driven decision-making and improve operational efficiency.
Econometrician
Econometrician
Econometricians use statistical and mathematical techniques to analyze economic data. PCA is a useful technique for Econometricians as it helps them reduce dimensionality and extract meaningful insights from large datasets. This course will provide you with a foundation in PCA, enabling you to contribute to economic modeling and forecasting.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines and infrastructure. PCA is a useful technique for Data Engineers as it helps them reduce dimensionality and improve the performance of data processing systems. This course will provide you with a foundation in PCA, enabling you to design and implement data pipelines that leverage survey data effectively.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. PCA is a useful technique for Software Engineers as it helps them reduce dimensionality and improve the performance of software systems. This course will provide you with a foundation in PCA, enabling you to design and develop software systems that leverage survey data effectively.
For more career information including salaries, visit:
OpenCourser.com/course/94pv4s/analyze
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
Analyze Survey Data using Principal Component Analysis.
Provides a comprehensive overview of the principles and applications of Principal Component Analysis (PCA). It valuable resource for anyone who wants to learn more about PCA and how to use it to analyze data.
Provides a comprehensive overview of multivariate data analysis techniques, including PCA. It valuable resource for anyone who wants to learn more about multivariate data analysis and how to use it to analyze data.
Provides a practical guide to multivariate statistical analysis techniques, including PCA. It valuable resource for anyone who wants to learn more about multivariate statistical analysis and how to use it to analyze data.
Provides a comprehensive overview of pattern recognition and machine learning techniques, including PCA. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning and how to use them to analyze data.
Save
Provides a comprehensive overview of data mining techniques, including PCA. It valuable resource for anyone who wants to learn more about data mining and how to use it to analyze data.
Save
Provides a comprehensive overview of deep learning techniques, including PCA. It valuable resource for anyone who wants to learn more about deep learning and how to use it to analyze data.
Provides a comprehensive overview of machine learning with R, including PCA. It valuable resource for anyone who wants to learn more about machine learning with R and how to use it to analyze data.
Provides a comprehensive overview of hands-on machine learning with R, including PCA. It valuable resource for anyone who wants to learn more about hands-on machine learning with R and how to use it to analyze data.
Provides a comprehensive overview of machine learning for absolute beginners, including PCA. It valuable resource for anyone who wants to learn more about machine learning for absolute beginners and how to use it to analyze data.
Provides a comprehensive overview of natural language processing with Python, including PCA. It valuable resource for anyone who wants to learn more about natural language processing with Python and how to use it to analyze data.
Provides a comprehensive overview of reinforcement learning, including PCA. It valuable resource for anyone who wants to learn more about reinforcement learning and how to use it to analyze data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/94pv4s/analyze
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Tricia Bagley
Good to know
Uses Principal Component Analysis to simplify and analyze survey data, making it relevant for professionals working with customer feedback
Employs Google Sheets, a familiar tool, for hands-on practice, making the course accessible to a wide range of learners
Focuses on practical applications, providing valuable skills for those seeking to extract insights from survey data
Designed for learners based in North America, limiting its accessibility for those in other regions
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