{"id":null,"code":"TKO_3103","name":{"valueFi":"Data Analysis and Knowledge Discovery","valueEn":"Data Analysis and Knowledge Discovery","valueSv":"Data Analysis and Knowledge Discovery"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534319652,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"This course enables to learn when and how to apply data analysis and knowledge discovery tools for data. Students will learn modern data analysis methods and algorithms to discover patterns and trends in large, complex and high-dimensional data sets, and turn data into information and knowledge.","valueEn":"This course enables to learn when and how to apply data analysis and knowledge discovery tools for data. Students will learn modern data analysis methods and algorithms to discover patterns and trends in large, complex and high-dimensional data sets, and turn data into information and knowledge.","valueSv":"This course enables to learn when and how to apply data analysis and knowledge discovery tools for data. Students will learn modern data analysis methods and algorithms to discover patterns and trends in large, complex and high-dimensional data sets, and turn data into information and knowledge."}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"The course introduces methods and algorithms for extracting information and knowledge from data sets. This includes techniques for data pre-processing, visualizing high-dimensional data, basic machine learning methods for supervised learning (classification, regression), unsupervised learning (clustering), model selection and validating how well a learned model predicts on new data (holdout, cross-validation). The CRISP-DM process model is introduced as a tool for analysing and implementing data science projects.","valueEn":"The course introduces methods and algorithms for extracting information and knowledge from data sets. This includes techniques for data pre-processing, visualizing high-dimensional data, basic machine learning methods for supervised learning (classification, regression), unsupervised learning (clustering), model selection and validating how well a learned model predicts on new data (holdout, cross-validation). The CRISP-DM process model is introduced as a tool for analysing and implementing data science projects.","valueSv":"The course introduces methods and algorithms for extracting information and knowledge from data sets. This includes techniques for data pre-processing, visualizing high-dimensional data, basic machine learning methods for supervised learning (classification, regression), unsupervised learning (clustering), model selection and validating how well a learned model predicts on new data (holdout, cross-validation). The CRISP-DM process model is introduced as a tool for analysing and implementing data science projects."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Written exam, mandatory practical data analysis programming exercises.","valueEn":"Written exam, mandatory practical data analysis programming exercises.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Lectures. Independent work on programming exercises. Independent work studying lecture materials, course book, online materials.","valueEn":"Lectures. Independent work on programming exercises. Independent work studying lecture materials, course book, online materials.","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"M.R. Berthold, C. Borgelt, F. Höppner, F. Klawonn: Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data. Springer, London (2010)\r\nA.Géron. Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. Concepts, Tools and Techniques to Build Intelligent Systems. 2nd edition. O’Reilly (2019).","valueEn":"M.R. Berthold, C. Borgelt, F. Höppner, F. Klawonn: Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data. Springer, London (2010)\r\nA.Géron. Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. Concepts, Tools and Techniques to Build Intelligent Systems. 2nd edition. O’Reilly (2019).","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"Python programming skills. Basic knowledge of probability, statistics and linear algebra is beneficial. Course TKO_7093 Statistical Data Analysis or equivalent skills.","valueEn":"Python programming skills. Basic knowledge of probability, statistics and linear algebra is beneficial. Course TKO_7093 Statistical Data Analysis or equivalent skills.","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietojenkäsittelytieteet","valueEn":"Computer Science","valueSv":"Computer Science"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Antti Airola, Jari Björne","valueEn":"Antti Airola, Jari Björne","valueSv":"Antti Airola, Jari Björne"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}