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Frauke Kreuter, Ph.D.,
Frederick Conrad, Ph.D.,
James M Lepkowski,
and
Richard Valliant, Ph.D.
The Capstone Project offers qualified learners to the opportunity to apply their knowledge by analyzing and comparing multiple data sources on the same topic. Students will develop a research question, access and analyze relevant data, and critically examine the quality of each data source.
At the completion of this capstone, students will have demonstrated hands-on data analysis capability, evaluated the quality of different data sources using the Total Survey Error approach, involving at least some of the following: comparing weighted non-probability samples to data collected from probability samples, using sampling techniques...
Read more
The Capstone Project offers qualified learners to the opportunity to apply their knowledge by analyzing and comparing multiple data sources on the same topic. Students will develop a research question, access and analyze relevant data, and critically examine the quality of each data source.
At the completion of this capstone, students will have demonstrated hands-on data analysis capability, evaluated the quality of different data sources using the Total Survey Error approach, involving at least some of the following: comparing weighted non-probability samples to data collected from probability samples, using sampling techniques...
Read more
The Capstone Project offers qualified learners to the opportunity to apply their knowledge by analyzing and comparing multiple data sources on the same topic. Students will develop a research question, access and analyze relevant data, and critically examine the quality of each data source.
At the completion of this capstone, students will have demonstrated hands-on data analysis capability, evaluated the quality of different data sources using the Total Survey Error approach, involving at least some of the following: comparing weighted non-probability samples to data collected from probability samples, using sampling techniques to correct for coverage errors, and tracking and assess the ease of using an online questionnaire that you implement.
Register for this course and see more details by visiting:
OpenCourser.com/course/35a5gx/survey
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches learners how to analyze and compare multiple data sources to evaluate their quality
Prepares learners to apply their research methods to data
Students develop hands-on data analysis capabilities
Taught by a faculty of experts in data science and survey methodology
Students evaluate data quality using the Total Survey Error approach, developing professional skills
Learners gain a foundation in accessing and analyzing relevant data while comparing weighted non-probability samples to probability samples
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Disengaged capstone
This capstone project has been heavily criticized for its lack of professor engagement, leaving students to fend for themselves during an intensive course.
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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 Survey Data Collection and Analytics Project (Capstone) with these
activities:
Compile a glossary of data analysis terms
Show steps
Strengthen understanding of data analysis terminology for better comprehension.
Show steps
-
Review lecture notes and readings to identify key terms.
-
Create a document or spreadsheet to organize the terms.
-
Define each term clearly and provide examples of its usage.
Review basic data analysis concepts
Show steps
Review foundational data analysis concepts to strengthen understanding of course materials.
Browse courses on
Data Analysis
Show steps
-
Review lecture notes and textbook materials on concepts like data types, distributions, and measures of central tendency.
-
Complete practice problems and exercises to reinforce understanding.
Solve practice data analysis problems
Show steps
Practice applying data analysis techniques to improve problem-solving skills.
Browse courses on
Data Analysis
Show steps
-
Access online resources or textbooks for practice problems.
-
Work through problems, checking solutions against answer keys for accuracy.
-
Identify areas of weakness and focus on improving those skills.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Connect with data analysis professionals
Show steps
Gain insights and guidance from experienced professionals in the field.
Show steps
-
Attend industry events or online forums to meet professionals.
-
Reach out to potential mentors via LinkedIn or email.
-
Build relationships and seek advice on career development.
Analyze a real-world dataset
Show steps
Apply data analysis skills to solve a practical problem and enhance understanding.
Browse courses on
Data Analysis
Show steps
-
Choose a dataset relevant to your field of interest.
-
Clean and prepare the dataset for analysis.
-
Perform data analysis techniques to identify patterns and insights.
-
Visualize the results to communicate findings effectively.
Write a blog post on a data analysis topic
Show steps
Enhance communication and understanding of data analysis concepts.
Browse courses on
Data Analysis
Show steps
-
Choose a specific topic related to the course material.
-
Research and gather information to support your writing.
-
Write a clear and engaging blog post explaining the topic.
-
Publish the blog post on a relevant platform.
Explore advanced data analysis techniques
Show steps
Expand knowledge of data analysis beyond the course scope.
Browse courses on
Data Science
Show steps
-
Identify advanced techniques relevant to your career goals.
-
Find online tutorials or workshops covering those techniques.
-
Follow the tutorials, complete exercises, and apply the techniques to real-world problems.
Compile a glossary of data analysis terms
Show steps
Strengthen understanding of data analysis terminology for better comprehension.
Show steps
Show steps
Show steps
- Review lecture notes and readings to identify key terms.
- Create a document or spreadsheet to organize the terms.
- Define each term clearly and provide examples of its usage.
Review basic data analysis concepts
Show steps
Review foundational data analysis concepts to strengthen understanding of course materials.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Review lecture notes and textbook materials on concepts like data types, distributions, and measures of central tendency.
- Complete practice problems and exercises to reinforce understanding.
Solve practice data analysis problems
Show steps
Practice applying data analysis techniques to improve problem-solving skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Access online resources or textbooks for practice problems.
- Work through problems, checking solutions against answer keys for accuracy.
- Identify areas of weakness and focus on improving those skills.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Connect with data analysis professionals
Show steps
Gain insights and guidance from experienced professionals in the field.
Show steps
Show steps
Show steps
- Attend industry events or online forums to meet professionals.
- Reach out to potential mentors via LinkedIn or email.
- Build relationships and seek advice on career development.
Analyze a real-world dataset
Show steps
Apply data analysis skills to solve a practical problem and enhance understanding.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a dataset relevant to your field of interest.
- Clean and prepare the dataset for analysis.
- Perform data analysis techniques to identify patterns and insights.
- Visualize the results to communicate findings effectively.
Write a blog post on a data analysis topic
Show steps
Enhance communication and understanding of data analysis concepts.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a specific topic related to the course material.
- Research and gather information to support your writing.
- Write a clear and engaging blog post explaining the topic.
- Publish the blog post on a relevant platform.
Explore advanced data analysis techniques
Show steps
Expand knowledge of data analysis beyond the course scope.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify advanced techniques relevant to your career goals.
- Find online tutorials or workshops covering those techniques.
- Follow the tutorials, complete exercises, and apply the techniques to real-world problems.
Career center
Learners who complete Survey Data Collection and Analytics Project (Capstone) will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists often work with multiple data sources. The Survey Data Collection and Analytics Project (Capstone) course can help aspiring Data Scientists build the foundational skills needed for their role. Students of this course will learn to design questionnaires, analyze datasets, validate designs, and evaluate results.
Data Analyst
Data Analyst
Successful Data Analysts often have a deep background in statistical analysis. The Survey Data Collection and Analytics Project (Capstone) course can help learners build the essential skills needed to be a Data Analyst. This includes foundational knowledge in data collection, analysis, and presentation.
Statistician
Statistician
Statisticians need to understand how to design questionnaires and analyze data to draw meaningful conclusions. The Survey Data Collection and Analytics Project (Capstone) course can help individuals learn the fundamentals of data collection, analysis, and presentation that will aid in their pursuit of becoming a Statistician.
Market Research Analyst
Market Research Analyst
Market Research Analysts must know how to collect data, analyze data, and make recommendations based on their findings. The Survey Data Collection and Analytics Project (Capstone) provides foundational training in data collection and analysis that can accelerate learners in their efforts to enter the field of Market Research Analysis.
Survey Methodologist
Survey Methodologist
Survey Methodologists oversee the design and implementation of surveys. The Survey Data Collection and Analytics Project (Capstone) course provides foundational training in survey design and analysis that can help students pursue careers as Survey Methodologists. Students will learn how to design survey questionnaires, conduct data collection, and analyze data.
Research Analyst
Research Analyst
Research Analysts are responsible for collecting, analyzing, and interpreting data to provide insights into complex issues. The Survey Data Collection and Analytics Project (Capstone) course can help learners build the skills needed to become Research Analysts. This includes foundational knowledge in data collection, analysis, and presentation.
Data Journalism
Data Journalism
Data Journalists use data to tell stories and inform the public. The Survey Data Collection and Analytics Project (Capstone) course can help learners build the skills needed to become Data Journalists by teaching them foundational knowledge of data collection, analysis, and presentation.
Public Policy Analyst
Public Policy Analyst
Public Policy Analysts use data to inform policy decisions. The Survey Data Collection and Analytics Project (Capstone) course can aid learners interested in Public Policy Analysis by teaching them foundational knowledge in data collection, analysis, and presentation.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. The Survey Data Collection and Analytics Project (Capstone) course will be useful for Business Analysts by teaching foundational knowledge in data collection, analysis, and presentation.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to solve complex problems in a variety of industries. The Survey Data Collection and Analytics Project (Capstone) course will be useful to Operations Research Analysts by teaching foundational knowledge in data collection, analysis, and presentation.
Actuary
Actuary
Actuaries use data to assess risk and make financial decisions. The Survey Data Collection and Analytics Project (Capstone) may be useful for Actuaries by teaching foundational knowledge in data collection, analysis, and presentation.
Epidemiologist
Epidemiologist
Epidemiologists use data to study the causes and spread of diseases. The Survey Data Collection and Analytics Project (Capstone) may be useful for Epidemiologists by teaching foundational knowledge in data collection, analysis, and presentation.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. The Survey Data Collection and Analytics Project (Capstone) may be useful for Financial Analysts by teaching foundational knowledge in data collection, analysis, and presentation.
Biostatistician
Biostatistician
Biostatisticians use data to design and analyze clinical trials. The Survey Data Collection and Analytics Project (Capstone) may be useful for Biostatisticians by teaching foundational knowledge in data collection, analysis, and presentation.
Computer Scientist
Computer Scientist
Computer Scientists use data to develop software and algorithms. The Survey Data Collection and Analytics Project (Capstone) may be useful for Computer Scientists by teaching foundational knowledge in data collection, analysis, and presentation.
For more career information including salaries, visit:
OpenCourser.com/course/35a5gx/survey
Reading list
We've selected seven 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
Survey Data Collection and Analytics Project (Capstone).
Comprehensive textbook commonly used in academic institutions.
Provides background on statistical methods used in data analysis.
This classic textbook provides a comprehensive overview of the methods used in survey research, with a focus on the practical aspects of conducting surveys. It covers a wide range of topics, including sampling, data collection, and analysis.
This textbook provides a comprehensive overview of the methods used in survey sampling, with a focus on the mathematical foundations of sampling theory. It covers a wide range of topics, including sampling designs, estimation methods, and variance estimation.
This textbook provides a comprehensive overview of the methods used to analyze survey data, with a focus on the use of statistical software packages such as SAS and R. It covers a wide range of topics, including descriptive statistics, inferential statistics, and regression analysis.
Save
Provides a comprehensive overview of the issue of nonresponse in surveys, with a focus on the methods used to reduce nonresponse bias. It covers a wide range of topics, including sampling methods, data collection methods, and weighting methods.
Provides a practical guide to the development and use of questionnaires in quantitative research. It covers a wide range of topics, including item construction, scale development, and data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/35a5gx/survey
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Time
20 hours
Via
Coursera
Institutions
University of Maryland, College Park
University of Michigan
Instructors
Frauke Kreuter, Ph.D.
Frederick Conrad, Ph.D.
James M Lepkowski
Richard Valliant, Ph.D.
This course belongs to a
collection
called
Survey Data Collection and Analytics . It's comprised of seven courses:
- Dealing With Missing Data
- Framework for Data Collection and Analysis
- Combining and Analyzing Complex Data
- Questionnaire Design for Social Surveys
- Sampling People, Networks and Records
- Survey Data Collection and Analytics Project (Capstone) (viewing)
- Data Collection: Online, Telephone and Face-to-face
Good to know
Teaches learners how to analyze and compare multiple data sources to evaluate their quality
Prepares learners to apply their research methods to data
Students develop hands-on data analysis capabilities
Taught by a faculty of experts in data science and survey methodology
Students evaluate data quality using the Total Survey Error approach, developing professional skills
Learners gain a foundation in accessing and analyzing relevant data while comparing weighted non-probability samples to probability samples
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