. "Data Science, Data Analysis, Data Mining"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Physics data analysis (python)"@en . . "5.00" . "The aim is to provide students with a strong grounding in the analysis of experimental Physics data in the Python programming language. The contents will cover the basics of statistics, error analysis and propagation of errors, curve fitting and parameter estimation, chi-squared tests for goodness of fit, Monte Carlo simulations and maximum likelihood methods. Python topics will be intertwined with data analysis topics to build Python skills at the same time. Students will learn from doing examples themselves in-class in an Active Learning Room environment as well as assignments. The error analysis section of the course will pay close attention to the Guide to the expression of Uncertainty in Measurement (G.U.M.) reference document adopted by many scientific organisations and industries.\n\nLearning Outcomes:\nHave an understanding of experimental measurement and uncertainties, including statistical and systematic errors, and to use appropriate precision when quoting uncertainties.\n\nUnderstand the fundamental statistical distributions that apply to physical measurements.\n\nBe able to characterise data through parameters such as the mean, standard deviation, covariance, weighted mean and uncertainties on the weighted mean.\n\nBe able to propagate errors on measurements through functions of those measurements, both analytically and numerically.\n\nBe able to fit a function to a set of experimental data to derive best-fit parameters including the uncertainties on the parameters and to use the best-fit covariance matrix to calculate confidence intervals.\n\nBe able to apply a chi-squared test to assess goodness of fit and f-test to assess whether extra parameters for nested functions significantly improve the fit.\n\nBe able to apply Kolmogorov–Smirnov test and chi-square tests to compare two distributions.\n\nHave an understanding of and be able to apply the Permutation test and Bootstrap/Jackknife tests.\n\nBe apply to apply the Method of Maximum Likelihood, including the Likelihood Ratio Test, for parameter estimation and significance estimation.\n\nBe able to do all of the above in Python using appropriate libraries." . . "Presential"@en . "FALSE" . . "Master in Space Science and Technology"@en . . "https://hub.ucd.ie/usis/!W_HU_MENU.P_PUBLISH?p_tag=PROG&MAJR=F060 and https://www.ucd.ie/physics/spacescience/" . "90"^^ . "Presential"@en . "This programme is ideal for graduates of Physics, Engineering and closely related disciplines, who want to transfer their expertise to the fast-growing global space sector. Ireland is a member of the European Space Agency (ESA) and dozens of Irish companies and researchers are involved in major international space missions. UCD is building Ireland’s first satellite, EIRSAT-1.\n\nCourse highlights include a hands-on CubeSat lab, payload development and satellite systems engineering of a high-altitude balloon experiment and participation in an international mission design team project. A 3-month internship provides relevant training for industry or research and can lead to employment. Students have completed internships at the European Astronaut Centre (EAC), ESA, NASA-Ames, Cosine, ENBIO, InnaLabs, Skytek, Eblana Photonics and Réaltra.\nProgramme Outcomes:\nDescribe the state-of-the-art of knowledge in space science and technology\nApply acquired knowledge and technical skills in the space industry, or in graduate research\nDraw on a suite of relevant professional and transferable skills\nEngage actively in professional networking within the field \nParticipate constructively in multi-disciplinary, international teams"@en . . . . . . . . "1"@en . "FALSE" . . "Master"@en . "None" . "9560.00" . "Euro"@en . "27720.00" . "Mandatory" . "Our MEng Aerospace Engineering degree will equip you with industry knowledge and an in-depth understanding of the aerospace design and build process. Study materials and manufacturing, stress and dynamics, energy and thermodynamics to gain a solid grounding in aerospace engineering principles.\n\nGraduate ready to take up your place within the exciting, fast-paced aerospace industry. You'll develop core skills that you'll take with you through your career, such as innovation, teamwork and creativity.\n\nBy the end of the course, you'll be prepared for employment in leading aerospace companies such as Airbus UK, BAE Systems, Rolls-Royce, Leonardo, MBDA, Boeing and GE Systems.\n\nThere's an increasing demand for qualified aerospace engineers in the industry, so you'll have strong employability prospects. Past graduates have gone into careers in the design and manufacture of civil and military aircraft, helicopters and jet engines.\n\nIn your second year, you'll have the chance to specialise through the Systems, Design and Manufacturing pathways, allowing you to follow your career aspirations.\n\nThroughout your course, you'll benefit from a range of professional opportunities. Get an inside track on the industry through regular factory tours and professional briefings from leading aerospace organisations and work on placements to build up valuable experience and professional skills."@en . "1"^^ . "TRUE" . "Upstream"@en . . . . . . . . . . . . . . . . .