Data science and spatial statistics help you identify patterns, relationships, and trends in geodata. In this course, you’ll learn how to use Python to perform geographic analyses, investigate networks, and analyze the movements of objects. You’ll work with powerful libraries for spatial statistics, network analysis, and mobility data. This will help you develop practical skills for turning data into well-founded insights and predictions.
Statistics and data science play a key role in discovering patterns, trends, and relationships in geospatial data. By analyzing data in a structured way, you can make more informed decisions and gain valuable insights from large amounts of information.
In this blended learning course, you’ll learn how to apply statistical analyses and data science techniques using Python. You’ll work with geospatial datasets, networks, movement data, and spatial patterns, and discover how to analyze, model, and visualize this data.
The course is suitable for GIS professionals, data analysts, and anyone who wants to gain a deeper understanding of geographic data. Thanks to the hands-on approach, you’ll not only learn the theory but also apply statistical methods directly to realistic datasets and real-world problems.
In this blended learning course, you’ll learn the fundamentals of statistics and data science within a geospatial context. You’ll start by analyzing datasets and learn how to identify patterns, clusters, and spatial relationships.
Among other things, you’ll learn to work with libraries such as PySAL, ESDA, SPREG, and MGWR for spatial statistics and regression analysis. These tools will enable you to investigate geographic patterns and find explanations for spatial differences within datasets.
In addition, you’ll discover how to analyze networks and mobility data using tools such as NetworkX, OSMnx, MovingPandas, and Trackintel. You’ll learn to investigate and visualize routes, trips, and spatial networks. You’ll also gain insight into applying statistical models to geographic problems.
In short: this course is ideal for anyone who wants to combine statistical analysis and data science with GIS and geospatial data to gain more well-founded insights.
Blended learning combines independent online learning with hands-on guidance. You’ll have access to online course materials that allow you to learn at your own pace how to work with statistical techniques, data analysis, and geospatial models. The theory is supported by practical assignments, so you can immediately practice with realistic datasets.
During the guided sessions, you can ask questions, get additional explanations, and work on assignments that align with real-world applications. You’ll learn how to set up statistical models, interpret results, and perform geospatial analyses on real-world datasets.
Upon completion of this course, you’ll have a solid foundation in statistics and data science with Python. You’ll be able to independently analyze datasets, investigate spatial patterns, and apply statistical techniques in GIS, mobility analysis, and geodata projects.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic knowledge of Python is required for this course. Experience with statistics or data science is not necessary. The course covers both the fundamentals of statistics and their practical application to geospatial datasets, providing you with a step-by-step understanding of data analysis and spatial statistics.
Statistics focuses on analyzing, explaining, and interpreting data using mathematical methods. Data science builds on this foundation and combines statistics with programming, data processing, and modeling to provide insights, predictions, and decision support. In this course, you will learn how these two disciplines reinforce each other in geospatial analysis.
Among other things, you will learn how to perform spatial statistics, regression analysis, pattern recognition, clustering, network analysis, and mobility analysis. You will work with libraries such as PySAL, ESDA, SPREG, MGWR, NetworkX, and OSMnx to examine and interpret geographic datasets.
This course is relevant for GIS specialists, geodata analysts, data scientists, researchers, and policy advisors who want to analyze geographic data and support their findings with statistical methods. The knowledge gained can be applied in the fields of mobility, land-use planning, infrastructure, environmental research, smart cities, and many other data-driven disciplines.
