Course Contents

Section 1: Introduction to Spatial Data Science

In this section, you will explore how spatial data, tools, and analysis techniques enhance traditional data science methods. You will understand that "spatial" goes beyond X and Y coordinates and that spatial context reveals hidden patterns in data. You will start applying data engineering and visualization techniques using ArcGIS Pro and ArcGIS Notebooks.

Section 2: Spatial Approach to Predictive Analysis

Prediction is a key part of data science. In this section, you will learn how to integrate spatial attributes into predictive models to deepen your understanding of data and increase predictive power. You will learn to apply the Random Forest algorithm, a popular machine learning approach, to solve problems. You will train and evaluate a model and then use it to generate robust predictions.

Section 3: Finding Optimal Locations with Suitability Models

Here, you will learn how to apply common spatial analysis techniques to answer the general question: "Where is the best location for <fill in the blank>?" This includes performing a weighted overlay analysis that considers multiple suitability criteria and ranks them. You will also learn how to transform data using functions to more fully represent the impact of suitability.

Section 4: Discovering Patterns and Clusters

In this section, you will explore whether patterns in the data are meaningful or just the result of random chance. ArcGIS provides a suite of tools to help analysts identify patterns and clusters in data and determine their significance. You will learn to apply statistical clustering methods to analyze patterns in space and time. You will create a space-time cube and use space-time pattern mining tools to explore spatiotemporal trends and identify areas and times of high and low clustering.

Section 5: Object Detection with Deep Learning

This section delves into extracting information from big data using deep learning. You will learn how to automate object detection and feature identification from images. You will prepare training data and then use a neural network to train an object detection model.

Section 6: Communicating Results Effectively

Effective communication is vital for successful analysts and data scientists. In this section, you will learn how to present complex analysis in a way that suits your audience to simplify information. You will design and build interactive visual stories that convey the key insights you want decision-makers to understand and act upon.