. "Other Statistics (rather Than Geostatistics) Kas"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Modern astrostatistics"@en . . "3" . "Randomness, uncertainties and deviations from the norm surround us in everyday life. A major asset of any scientist is to see beyond the complexity of noise, scatter and biases, and to find an underlying -often surprisingly simple- explanation for the noisy data. This course is specialized to astronomical data analysis, but the topics discussed will also foster an improved understanding of Google, Facebook and other free social media services.\n\nTopics that will be covered include:\n\nDescriptive statistics: Finding meaning in a huge data set.\nInference statistics: Constraining a physical model by data.\nFiltering, e.g. for gravitational wave detections and source detection.\nRandom fields: Sky surveys and structure formation in cosmology.\nSampling methods: Making huge data analyses numerically feasible.\nBayesian Hierarchical Models: How to disentangle a seemingly complex analysis.\nPrior Theory and Information Measures: How not to hide prejudices in an analyses.\nMissing data and elusive physics: What to do if your sought signal hides in the dark figures?\nMachine learning: Finding patterns which escape humans.\n\nOutcome:\nPrincipal course objective: After completion of this course, you will be able to correctly interpret noisy data. You will be able to design and apply statistical methods to answer scientific questions. You will be able to measure parameters, discover astronomical objects, or discover elusive signals in noisy data.\n\nUpon completion of this course, you will be able to:\n\nRecognize the most common distributions of noisy astronomical data\nIdentify signals in noisy data\nReject theories which are incompatible with data\nDesign own statistical methods to analyze complex data\nCategorize astronomical objects\nSolve simple Bayesian Hierarchical Models\nDiscover prejudices in analyses\nExplain basic machine learning algorithms" . . "Presential"@en . "TRUE" . . "Master of Astronomy and Data Science"@en . . "https://www.universiteitleiden.nl/en/education/study-programmes/master/astronomy/astronomy-and-data-science" . "120"^^ . "Presential"@en . "In the master’s specialisation Astronomy and Data Science you focus on development and application of new data-mining technologies, fully embracing modern astronomy as a data rich science. You combine the research curriculum in Astronomy with in-depth training in Computer Science.\n\nThe Astronomy and Data Science master’s programme is built on world-class computational astrophysics research as well as hightech industry expertise. It covers a wide range of research areas studying complex astronomical phenomena, including radiative transfer, computation of dynamical internal galaxy structures and hydrodynamical modeling of galaxy formation and evolution of the intergalactic medium.\n\nThis two-year Astronomy and Data Sicence programme uniquely combines advanced Astronomy courses of the Leiden Observatory and relevant courses from the Computer Science master’s programme of the Leiden Institute of Advanced Computer Science including advanced data mining and neural networks. To this end, the Leiden Observatory offers sophisticated computational facilities ranging from local computer clusters to high-performance systems at national and international computing centers.\n\nOutcome:\nDuring the programme, you learn to perform academically sound research and evaluate scientific information independently and critically. Without exception, you actively participate in current research within the institute and are individually supervised by our international scientific staff. Students with a Leiden degree in Astronomy become strong communicators and collaborators and can easily operate in an international setting. You will acquire extensive astronomical research experience and highly advanced analytical and problem solving skills."@en . . . . . . "2"@en . "FALSE" . . "Master"@en . "Thesis" . "2314.00" . "Euro"@en . "19600.00" . "Mandatory" . "Most graduates holding a MSc degree in Astronomy from Leiden University find work in many different capacities, including:\n\n1. Research: universities, observatories, research institutes\n2. Industry and consultancy: ICT, R&D, telecom, high technology, aerospace\n3. Finance: banking, insurance, pension funds\n4. Public sector: governments, policy makers, high schools\n5. Science communication: journalism, popular writing, museums\n6. Typical jobs for Astronomy graduates include:\n\nScientific researcher (postdoc, research fellow, professor)\n1. R&D engineer\n2. Consultant\n3. Data scientist, statistician\n4. Policy advisor, public information officer (e.g. Ministry of Foreign Affairs)\n5. High school physics teacher\n6. Scientific editor for magazines, newspapers and other media\n7. Research at Leiden Observatory\n\nIf you want to get more deeply involved in research after graduating in Astronomy, consider pursuing a PhD at Leiden Observatory. If you have completed the Leiden master’s degree programme in Astronomy, you are directly eligible for admission to our PhD programme"@en . "no data" . "TRUE" . "Upstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .