Course Description

Survey research is a powerful tool for informing decisions and projections, and increasingly, the lines demarcating data science and survey research are becoming blurred. A variety of decision-makers try to leverage survey data routinely: from marketing and communications officers at major corporations, to government officials and agencies, to news outlets and the general public. Over the past few years, it has become extremely easy for just about anyone to design and field an online survey. However, that democratization of access to survey research raises questions about reliability; can we trust results from survey data? Who did the analysis, and how? Are the methodologies valid? Can we make decisions based on the results?

This course is an overview of the broad spectrum of tasks involving survey data: from designing and fielding surveys to making sense of the data when it emerges from the field, and finally, using the data to build products and inform decisions. In this course we will explore a series of case studies and workshops designed not only to address each of these tasks but also to develop practical techniques for successfully deriving value from survey research. Students taking this course will learn to effectively design surveys, manage and wrangle survey data, and leverage it to make models and applications more powerful.


This course is part of the Data Engineering and Data Science & Machine Learning tracks of the Advanced Data Science Certificate.

Course Objectives

Upon successful completion of the course, students will:

  • Explore the tradeoffs between different types of survey data collection, including various methods of sampling, weighting, and stratification

  • Understand the basics of survey science: designing surveys, survey methodology, and how to tell a good survey from a bad survey

  • Evaluate survey data quality: learn how to conduct thorough quality assurance and investigate possible sources of bias or error in survey results

  • Understand common data engineering patterns associated with survey data

  • Understand common workflows data scientists use when working with survey data, including creating scalable data products from survey data and conducting ad hoc custom research using survey data

  • Evaluate ethical and legal considerations and academic conventions such as institutional review boards that are relevant to working with survey data

  • Compare and contrast popular R and Python libraries for the entire spectrum of working with survey data.

Applies Towards the Following Certificates

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