Massive geospatial data are generated every second from our smartphones, through our social media posts, or through many kinds of other means like tracked whale trajectories in the ocean, allowing us to trace the movements of entire societies. As these data keep growing, it becomes more important to extract meaningful insights from location, relation, and position, for applications as diverse as business analytics, epidemiology, or species protection. This course provides students core competences in Geospatial Data Science (GDS). A prerequisite for taking this course is solid know-how in Python programming and data analysis. There are 14 weeks of learning/teaching activities.

This course is about statistical analysis of vector data and machine learning using vector data. In this course I demonstrate open source python packages for the analysis of vector-based geospatial data. I use Jupyter Notebooks as an interactive Python environment. GeoPandas is used for reading and storing geospatial data, exploratory data analysis, preparing data for use in statistical models (feature engineering, dealing with outlier and missing data, etc.), and simple plotting. Statsmodels is used for statistical inference as it provides more detail on the explanatory power of individual explanatory variables and a framework for model selection.

It is simply looking at where things happen to understand why they happen there. Geospatial Data Science is the discipline that specifically focuses on the spatial component of data science. Spatial Analysis is considered as a core infrastructure of the modern tech industry and is heavily substantiated by the business transactions of world-leading companies such as Uber, Deliveroo, Apple, Google, Intel, and evidently by the motor companies such as Tesla, BMW, and Mercedes. So, these companies are bound to hire more and more Spatial Data Analysts and Geo-Spatial Scientists. Based on these business trends, we've compiled the spatial analysis courses designed by world-class educators to help beginners gain solid foundations of spatial data analysis.

Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. In this, we are going to perform spatial analysis and trying to find insights from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get hands-on Geopy, Plotly etc.. the workhorse of Geospatial data science Python libraries. The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science.

Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. In this, we are going to perform spatial analysis and trying to find insights from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get hands-on Geopy, Plotly etc.. the workhorse of Geospatial data science Python libraries. The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science.