. "Other Statistics (rather Than Geostatistics) Kas"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Statistics applied to science and engineering"@en . . "6.0" . "### Working language\n\nEnglish\n_Note: Note that the working language will be English, and students can always ask questions in Portuguese._\n\n### Goals\n\n1\\. Enable the student for regression analysis involving continuous or discrete responses (generalized linear models)\ntwo\\. Implement statistical analyzes in suitable software\n3\\. Promoting a critical spirit in a data analysis process (data collection, modelling, interpretation of results, ...)\n\n### Learning outcomes and skills\n\nAt the end of the curricular unit, it is intended that students:\na) acquire knowledge about the organized collection of information\nb) learn statistical techniques and models commonly used in data processing\nc) know how to correctly choose the statistical models learned for concrete problems\nd) know how to apply and implement the models studied in R\ne) acquire a critical spirit and ability to interpret the results obtained.\n\n### Working mode\n\nIn person\n\n### Prerequisites (prior knowledge) and co-requisites (concurrent knowledge)\n\nPrior knowledge of random variables and probability distributions, sample statistics, confidence intervals and hypothesis testing is required. These are the usual contents of an introductory curricular unit to Probability and Statistics in higher education. A brief review of this matter will be carried out.\n\n### Program\n\n0\\. Brief review of inference-based techniques. statistics - confidence intervals and hypothesis tests\n1- Introduction to programming language in software environment **R.**\ntwo\\. Pearson correlation and Spearman correlation.\n3\\. Simple linear regression.\n4\\. Multiple linear regression. Model, parameter estimation, hypothesis tests for coefficients, confidence intervals, prediction intervals, determination coefficient, multicollinearity, model selection methods, model comparison, diagnosis.\n5\\*. Analysis of variance - ANOVA: 1 and 2 factors.\n6\\*. Generalized linear models. Logistic regression.\n\\*Only one subject will be studied, from 5. to 6.\n\n### Mandatory Bibliography\n\nRita Gaio; Notes written by the teacher\n\n### Complementary Bibliography\n\nISBN: 1-58488-029-5\nISBN: 0-387-95475-9\nISBN: 978-0-521-86116-8\nISBN: 0-387-95187-3\nISBN: 0-387-95284-5\nISBN: 1-58488-325-1\nISBN: 0-387-98218-3\nJulian Faraway; Linear Models with R, Taylor and Francis, 2009. ISBN: 1584884258\nJulian Faraway; Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC Texts in Statistical Science, 2006. ISBN: 158488424X\n\n### Teaching methods and learning activities\n\nTheoretical-practical classes with different examples of application of techniques and statistical models presented in a computational laboratory. The software used is R.\n\n### Software\n\nR Project\n\n### Key words\n\nPhysical Sciences > Mathematics > Statistics\n\n### Type of evaluation\n\nDistributed evaluation with final exam\n\n### Assessment Components\n\nTest: 37.50%\nWritten work: 25.00%\nExam: 37.50%\n\n**Total:**: 100.00\n\n### Occupation Components\n\nSelf-study: 110.00 hours\nFrequency of classes: 42.00 hours\nWritten work: 10.00 hours\n\n**Total:**: 162.00\n\n### Get Frequency\n\nThere is no lack of frequency.\n\n### Final classification calculation formula\n\n1\\. The work consists of a written report and an oral presentation. Carrying out the work is optional.\n \ntwo\\. The grade of the work cannot be improved.\n \n3\\. The evaluation of the normal season will include the classification of two tests (T1 and T2), each with a quotation of 10 points. The T2 test will take place on the day designated for the exam of the normal season.\n \n4\\. The evaluation of the appeal period will only include a final exam, which will focus on all the contents of the curricular unit. The classifications of the T1 and T2 tests will not be considered here.\n \n5\\. Evaluation formula in the **regular season**: There are two evaluation formulas, depending on whether or not the curricular unit's work/project is delivered.\n \na) For students who **turn in work**:\na1) T1+T2: weight of 13 or 15 (in 20); work: weight of 7 or 5 (in 20)\nOf the two evaluation components, the one in which the student had the best rating (on a scale of 0-20) has, for that student, the maximum weight indicated above. The worst component has, for that student, the minimum weight indicated above.\na2) In order to pass, the student must obtain a classification greater than 20% in each of the components (tests and work).\n \nb) For students who **do not turn in the work**:\nIn this case, only the test scores count; however, the student's final classification will never be higher than 16, even with a higher grade in the tests.\n \n6\\. Evaluation formula in **appeal season**: There are two evaluation formulas, depending on the delivery or not of the work/curricular unit project.\n \na) For students who **turn in work**:\na1) resource exam: weight of 13 or 15 (out of 20); work: weight of 7 or 5 (in 20)\nOf the two evaluation components, the one in which the student had the best rating (on a scale of 0-20) has, for that student, the maximum weight indicated above. The worst component has, for that student, the minimum weight indicated above.\na2) In order to pass, the student must obtain a classification greater than 20% in each of the components (exam and work).\n \nb) For students who **do not turn in the work**:\nIn this case, only the exam score counts; however, the final classification of the student will never exceed 16 points, even if he/she has a higher grade in the exam.\n\nMore information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479406" . . "Presential"@en . "TRUE" . . "Master in Remote Sensing"@en . . "https://sigarra.up.pt/fcup/en/cur_geral.cur_view?pv_ano_lectivo=2021&pv_curso_id=16781" . "120"^^ . "Presential"@en . "The FCUP Master’s degree in Remote Sensing offers advanced training in Earth observation techniques, analysis and use of geospatial data acquired by sensors deployed on space-borne or aerial platforms. This Programme aims to equip students with skills on the fundamentals of Remote Sensing (RS), the application potential of the various sensors, acquisition techniques, data processing and analysis, and familiarization with various RS and Geographical Information Systems (GIS) data processing tools. Students are expected to be able to use this knowledge in the study of the planet and in solving problems in various fields of application. \nTo this end, the Programme was organised with a common core of curricular units addressing fundamental subjects and a set of optional units that aim to teach specific skills in applications for the study of the ocean, agriculture and forests, coastal zones, climate change, etc. \n\nVídeo de Apresentação: https://youtu.be/_JlMlgmp81U\n\n### Programme structure\n\nThe FCUP MSc in Remote Sensing is a two-year programme organised as follows \n\n* a first year corresponding to a study programme of a set of curricular units (60 ECTS);\n* a second year corresponding to a scientific dissertation or a professional internship with a final report (54 ECTS) plus a supporting curricular unit of 6 ECTS on scientific writing.\n\n### Schedule and teaching language\n\nThe Master's degree runs on a concentrated schedule predominantly on Friday afternoons and on Saturday mornings, attracting students from all over the country, including working students. It is also intended to attract students from other countries, and it is anticipated that classes may be taught in English. \n\n### Employment Prospects\n\nAs this is the only MSc of this type in Portugal, it will respond to the needs of specialized staff in Remote Sensing. The Masters in Remote Sensing will have unique competences to join public institutions or private companies of a technological nature, including space agencies (ESA, EUMETSAT, etc.), with activity in in areas related to the exploitation of Remote Sensing or GIS data, location-based services, thematic mapping, and many others involving the use of georeferenced data. \nIt is also expected that the graduates of this MSc will promote the technological development of the country through the creation of companies of a technological nature and some of them continue their studies for a PhD. \n\n### Entry requirements\n\n1. Hold a higher academic degree in a study programme whose curricula provides students adequate scientific background for Remote Sensing: several branches of Engineering, Environmental Sciences, Informatics, Mathematics, Physics, Geophysics, Geology and related fields.\n2. Hold a foreign higher academic degree in one of the areas described in the previous paragraph.\n3. Hold an academic, scientific or professional curriculum recognized by the scientific committee of the MSc Programme as sufficient to prove capacity to carry out this study program.\n\n### Selection criteria\n\n1\\. Academic curriculum (60%)\n\n* Sub-criterion 1: Training area (20%) \n First degrees with study plans which include scientific background for Remote Sensing will be valued. Background in Mathematics (8%), Informatics / Programming (8%) and Physics (4%) will be considered.\n* Sub-criterion 2: First degree’s grade (40%) \n For candidates who do not yet hold a degree but can obtain it before the end of the registration period, as well as for those who satisfy the condition iii) of entry requirements, the first degree’s grade is replaced by the credit-weighted average of grades in the completed courses.\n\n2\\. Scientific curriculum and professional experience (40%)\n\n* Sub-criterion 1: Technical and/or scientific publications and communications (15%) \n The following aspects will be considered: peer-reviewed papers in scientific journals (10%); other scientific papers (5%);\n* Sub-criterion 2: Participation in research projects (5%)\n* Sub-criterion 3: Professional curriculum (20%) \n The duration and nature of professional activity in areas of relevance to the Master’s programme shall be considered, as follows: relevance of the professional activity (12%); duration of the professional activity (8%).\n\nIn case of equal ratings the following criteria will be adopted:\n\n* First’s degree mark\n* Number of years of professional activity on a relevant field.\n* Number of scientific papers\n* Participation in internships during the first degree, in Remote Sensing topics.\n \n\n### Contacts\n\nCourse Director: m.dr.diretor@fc.up.pt\n\nPostgraduate Section: pos.graduacao@fc.up.pt\n\nAcademic Degree: Master\n\nType of course/cycle of study: Masters Degree \n\nDuration: 4 Semesters\n\n### Study Plan\n\n* The study plan from 2018: https://sigarra.up.pt/fcup/en/cur_geral.cur_planos_estudos_view?pv_plano_id=22922&pv_ano_lectivo=2021&pv_tipo_cur_sigla=&pv_origem=CUR \n\n* All Courses of Study: https://sigarra.up.pt/fcup/en/cur_geral.cur_planos_estudos_list?pv_curso_id=16781&pv_ano_lectivo=2021&pv_tipo_cur_sigla=&pv_origem=CUR \n\n### Certificates\n\n* Master's degree in Remote Sensing (120 ECTS credits)\n* Specialization in Remote Sensing (60 ECTS credits)"@en . . . . "2"@en . "FALSE" . . . "Master"@en . "Thesis" . "1300.00" . "Euro"@en . "3750.00 (International) / 2250.00 (CPLP)" . "Recommended" . "As this is the only MSc of this type in Portugal, it will respond to the needs of specialized staff in Remote Sensing. The Masters in Remote Sensing will have unique competences to join public institutions or private companies of a technological nature, including space agencies (ESA, EUMETSAT, etc.), with activity in in areas related to the exploitation of Remote Sensing or GIS data, location-based services, thematic mapping, and many others involving the use of georeferenced data.\nIt is also expected that the graduates of this MSc will promote the technological development of the country through the creation of companies of a technological nature and some of them continue their studies for a PhD."@en . "1"^^ . "TRUE" . "Downstream"@en . . . . . . . . . . . . . . . . . . . . .