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Guided learning journeys that build knowledge step by step.
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A graduate-level learning path for space physicists to understand the policy landscape affecting research funding, satellite operations, international collaboration, and commercial space activities. This path introduces the physics context, policy analysis frameworks, and international relations concepts necessary to navigate and influence space policy.
This learning path equips graduate students in space physics with the software engineering skills necessary to develop robust, reproducible, and open-source data analysis pipelines. It covers version control, testing, and community software practices, tailored to space physics data analysis.
This graduate-level learning path equips space physics students with the knowledge and skills to apply data assimilation techniques to space physics models. It covers the mathematical foundations, core algorithms, and practical implementation strategies, with a focus on ionospheric assimilation.
This learning path equips graduate students in space physics with the knowledge and skills to apply machine learning to space physics data. It covers essential Python and data analysis skills, statistics, signal processing, and core ML techniques including classification, regression, clustering, anomaly detection, and feature extraction, with applications to space physics problems.
This path equips graduate students with the knowledge and skills to develop, run, and validate global MHD models of Earth's magnetosphere. It covers the underlying plasma physics, the numerical methods used in codes like BATS-R-US and LFM, the crucial role of boundary conditions and solar wind drivers, and the techniques for model validation against observations.
This advanced graduate-level path equips space physics students with the core knowledge and skills needed to operate a scientific space mission. It covers the full operations lifecycle—from mission planning and commanding through data acquisition and anomaly handling—while integrating essential systems engineering and project management perspectives. Learners will understand how spacecraft and instruments are operated to achieve scientific objectives, collaborate effectively in operations teams, and respond to real-world challenges.
This graduate-level path equips space physics students with the skills to analyze plasma wave data from space missions using Python and IDL. It covers essential signal processing techniques (FFT, wavelet, polarization), wave identification, and emission mechanisms, culminating in a comprehensive capstone project.
This advanced graduate-level learning path equips students with the skills to analyze energetic particle data from space missions. It covers the physics of particle measurements, data processing techniques, and advanced analysis methods including phase space density and composition analysis, with a strong foundation in Python and statistics.
A graduate-level learning path for analyzing magnetic field data from space missions, covering calibration, coordinate transformations, wave analysis, and field line tracing. Learners will build practical skills in Python for handling magnetometer data.
This learning path equips university students interested in outreach with foundational space physics knowledge and practical science communication skills. It covers aurorae, space weather, data visualization, and citizen science, culminating in a hands-on outreach project.