Session Summary:

The course will cover some of the most commonly used methods of analysis and inference when working with data related to people, such as survey data and organizational network data. Among other things, you will learn about: Typical analyses conducted on people data Common people-related data types Common hypothesis testing methods Common explanatory modeling methods How to use graphs to model network relationships How to analyze and draw inferences about people networks You will learn by working through code and data examples with instructors and faculty, including a final data project where you will have a chance to put all your learning into practice.

Should I take the course?

This course will be valuable to you if:

  • You currently work or intend to work in a people-related discipline

  • You have a basic working knowledge of R and RStudio,

  • You are interested in learning applied statistical methods that are commonly used to understand and draw conclusions about the skills, behaviors and attitudes of people and groups.

The course is ideal for someone who is likely to study quantitative social science disciplines to an advanced level, as well as those working in quantitative Human Resources roles in organizations.

Session Details

July 25–26th

9:00 A.M. – 5:00 P.M.



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Keith McNulty

McKinsey & Company

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Keith McNulty is an applied mathematician/statistician, psychometrician and data scientist based in the UK. He started his career as a Pure Mathematician with a focus on Matrix Algebra and Group Representation Theory. He then transitioned into the private sector where he developed expertise in the application of mathematics and measurement theory to questions of people, talent, skills and organizational science. He is currently the Global Leader of Talent Science and Analytics at McKinsey & Company, the leading global professional services firm.

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Alex LoPilato


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Alex LoPilato is an organizational psychologist with extensive experience in the statistical models, methods, and theories developed to draw inferences from survey data and other psychometric data.

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Liz Romero


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Liz Romero is an applied statistician with extensive experience in the engineering and modeling of people-related data.