Data Analytical Thinking and Methods I: How To Define a Research Question and Introduction to Statistical Approaches to Draw Inference


  • How to define a research question (what are we measuring?)
  • To think about what data is available and the concept of measurement error (how are we measuring it?)
  • An introduction to the main statistical approaches (how can we draw inference?)
Taught by
  • Julia Lane
42 mins


During this session you will learn about quantitative data—including so-called “big data”— and some of the statistical techniques researchers and policy officials use to derive value from it. The lecture emphasizes the broad structure and necessary steps needed in any data-related inquiry: define a research question, formulate a testable hypothesis, think about what data is available, what data is missing, issues of measurement error, and other applied concerns, such as how to link datasets. At the end, we will discuss some of the statistical paradigms that can be used to draw inferences, as well as some of the key ideas when addressing the privacy and confidentiality issues.


Big Data and Social Science: A Practical Guide to Methods and Tools by Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, and Julia Lane

For an in-depth training, check out the Applied Data Analytics Training Program, the first-of-its-kind program to give working professionals an opportunity to develop the key computer science and data science skill sets necessary to harness the wealth of newly-available data and to creatively address real civic problems.

Questions? Need help with a project?
Ask the public data science community at The Network of Innovators (NoI). NoI is a peer learning platform for finding practitioners with expertise and experience.