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Guided learning journeys that build knowledge step by step.
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7813 Paths · page 443 / 782
This learning path guides graduate students in exoplanet science through the essential knowledge and skills needed to understand and interpret CHEOPS observations. It covers mission fundamentals, data reduction, precision photometry, and characterization of exoplanets from CHEOPS data.
This learning path guides graduate students through the process of analyzing TESS data, from understanding the mission and its data products to performing transit searches and validating planet candidates. It covers essential prerequisites in photometry, Python, and statistics, and culminates in hands-on projects using real TESS data.
A graduate-level learning path to analyze TESS data, covering time-series photometry, transit detection, candidate validation, and follow-up strategies. Learners will gain practical Python-based skills to reduce, analyze, and interpret TESS light curves for exoplanet discovery.
This learning path equips graduate students in exoplanet science with the data science skills needed to analyze large exoplanet datasets, apply machine learning, and manage databases. It covers Python programming, statistics, databases, and machine learning, all contextualized within exoplanet research.
This graduate-level learning path equips learners with the knowledge to understand, evaluate, and apply key exoplanet observation techniques and their underlying instruments. It covers the foundational physics of detection, the major instrument types (photometers, spectrographs, coronagraphs), and the precision requirements that drive modern exoplanet science.
This learning path guides graduate students through the essential statistical and domain knowledge needed to model exoplanet populations. It covers occurrence rate inference, detection bias correction, forward modeling, and Bayesian hierarchical models, culminating in the application of these methods to real exoplanet surveys.
This advanced graduate-level path equips learners with the skills to analyze exoplanet spectra, from raw data reduction to atmospheric retrieval. It covers the essential principles of spectroscopy, data reduction techniques, telluric correction, and spectral retrieval, with a focus on Python-based tools. The path is designed for graduate students aiming to build a career in exoplanet science.
This graduate-level learning path equips learners with the skills to analyze exoplanet transit light curves, from data reduction to model fitting and validation. It covers essential photometry, signal processing, and statistical concepts, culminating in hands-on transit fitting and vetting.
A comprehensive learning path for graduate students in exoplanet science to master radial velocity (RV) data analysis. It covers the physics of the RV method, data reduction, stellar activity correction, signal extraction, and orbital fitting, with a strong emphasis on statistical rigor and practical application.
This learning path explores how the discovery of exoplanets influences cultural, philosophical, and educational perspectives. It introduces the basics of exoplanet science, the search for life, and examines the broader societal implications for humanity's place in the cosmos.