. "Data Science, Data Analysis, Data Mining"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Advances in data mining"@en . . "6" . "The traditional data mining techniques are mainly focused on solving classification, regression and clustering problems. However, the recent developments in ICT led to the emergence of new sorts of massive data sets and related data mining problems. Consequently, the field of data mining has rapidly expanded to cover new areas of research, such as:\r\n\r\nprocessing huge (tera- or petabytes big) data sets\r\nreal-time analysis of data streams (internet traffic, sensor data, electronic transactions, etc.),\r\nsearching for similar pairs of objects such as texts, images, songs, etc., in huge collections of such objects,\r\nfinding anomalies in data,\r\nclustering of massive sets of records,\r\nrecommendation systems,\r\nreduction of data dimensionality\r\napplications of DeepLearning to data mining\r\n\r\nDuring the course you will learn several techniques, algorithms and tools for addressing these new and challenging data mining problems:\r\n\r\nRecommender Systems: Collaborative Filtering, MatrixFactorization\r\nAlgorithms for dimensionality reduction: LLE, t-SNE, UMAP\r\nRandomForest and XGBoost: the most popular algorithms for classification and regression trees\r\nAlgorithms for detecting anomalies in data\r\nLocality Sensitive Hashing (LSH): a general technique for finding similar items in huge collections of items\r\nAlgorithms for mining data streams: sampling, filtering (Bloom filters), probabilistic counting\r\nApplications of DeepLearning to data mining\r\nDistributed Processing of Massive Data: Hadoop, MapReduce, Spark\n\nOutcome:\nAfter completing the course, the students should:\r\n\r\nknow most successful algorithms and techniques used in Data Mining;\r\ngain some hands-on experience with several algorithms for mining complex data sets;\r\nbe able to apply the acquired knowledge and skills to new problems." . . "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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .