Michael is a hybrid thinker and doer--a byproduct of being a StrengthsFinder "Learner" over time. With 20 years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.

This is the third article of a three-part series of articles in Getting started Geographic Data Science with Python. You will learn about reading, manipulating and analysing Geographic data in Python. The third part, which is this article, covers a relevant and real-world project wrapping up to cement your learning. Learning Objectives for this case study are: 1. Apply spatial operations on real word dataset project 2. Spatial join and munging Geographic data. In this project, we will use two datasets: a population dataset disaggregated by age and preschools dataset from Statistics Sweden.