MMS is a one-year full-time programme that is designed to develop comprehensive marketing expertise with the latest thinking, techniques and tools. 200604393R, © 2019 Nanyang Technological University Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. The programme is taught in English and was set up in co-creation with the industry. Graduates from the programme will have both the … The degree focuses on the development of new methods for data science. Poisson Processes. Modern businesses are turning toward increasingly sophisticated analytics methods to offer improved shopping suggestions to consumers, target previously-neglected markets, and improve the efficiency of their internal logistical processes and workflows. Stochastic Processes. Topics taught include data preparation (data cleaning, outlier analysis and transformation) and statistical techniques (regression modelling, multivariate statistics, and statistical inference). The National University of Singapore Master of Science in Data Science and Machine Learning is an interdisciplinary graduate degree programme designed to nurture the next generation of leaders in data science. Careers options include: Last modified on Data mining is the process of knowledge discovery. program and can be started in either the spring or fall semester. Graduates have a strong track record of employment in libraries, hospitals, universities, and other government as well as business organisations. The Capstone Project company partners in the academic year 2018/19 included Adobe Research, Alpha Telefonica, Facebook, Microsoft, and Tesco. Informaion Analytics: Focuses on using methods, tools and frameworks to manage, analyse and derive insight from traditional as well as digital information assets such as social, mobile, cloud and big data. By the end of the course, participants will gain critical IT skills in analysing business processes, improving these processes, developing business applications with an industry standard database and use data for business requirements. Practicum I is a compulsory course. Currently, the calculation of the required amount is based on $18,000 per year for living expenses, in addition to the tuition and miscellaneous student fees. This course is a continuation of MH8101 Operations Research I. Data Protection and Privacy Introduction; Chair's Message; Leadership; Faculty; Our People; Job Openings; SBS Safety; Programmes. The two year Master program in Data Science is built on the Data Science B.Sc. The requirements for graduation are as follow: Note: On average, full-time students take 4-5 classes a week and part-time students take 2-3 classes a week. In this course, we cover topics including weka, libsvm, IBM Business Analytics, Matlab, SAS, Rapid Miner and Cplex. Our full-time students had found internships in organizations for the practicum module that include APL, BreadTalk, Charles & Keith, DHL, Experian, Grab, UPS, Shopee, Lazada, DBS, UOB, Johnson & Johnson, JR Group, Lenovo, MSD, PSA, and PwC. Bachelor of Science (Data Science and Artificial Intelligence) Professional Courses; People. Topics covered include structure of the web, random graph models of networks, link analysis and web search, network dynamics, network effects, power law phenomena, the small-world phenomenon, and diffusion through networks. In this course, we cover topics including Python, Cplex, R, Matlab, and SAS. Courses focus on real-world challenges facing: communities; organizations; individuals; Hands-on training through research projects, collaboration, and an internship give students a competitive edge in the job market. NTU_PageContent. Graduates have a strong track record of employment in libraries, hospitals, universities, and other government as well as business organisations. Gaussian and Markovian Processes. Embark on your lifelong learning journey anytime and anywhere at your convenience with NTULearn+, NTU’s first Continuing Education and Training (CET) app. The new Master’s programme Applied Data Science enables you to become a data science professional with excellent analytic capabilities. The two-year Master's program in Data Science builds on the Bachelor's program of the same name and can be started in either the winter or summer semester. 4-Nov-2020. No. One of a range of advanced courses within our postgraduate Master's programme in Computer Science, this particular course provides you with a specialism in Data Science and Analytics ; Advanced topics studied include mathematics and programming of data science, data mining, machine learning and neural artificis, and data analytics applications. It is a joint project of the Faculties of Statistics, Computer Science and Mathematics. Ad-hoc Seminars on Analytic… In the past, traditional descriptive analytics could only equip organizations with fundamental operational observations, signaling deviations and raising alarms when necessary. Talented data scientists are needed in finance, healthcare, tech, and every other industry. 1-Feb-2020. The Master of Science in Materials Science and Engineering is offered in NTU and is intended to equip students with the latest postgraduate level knowledge in materials science and engineering. The course emphasizes basic concepts and techniques as well as practical applications relevant to business, social sciences and life sciences. Topics covered include Monte-Carlo simulation, queuing theory, discrete event simulation, stochastic programming, dynamic programming and optimal control, and inventory theory. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are some of the topics covered in this course. Find complete information for Master of Science [M.Sc] (Data Science) Course - Complete Syllabus and Subjects. Topics covered include probability, conditional probability; random variables, joint distributions, conditional distributions and independence; probability laws, multivariate normal distribution; order statistics; convergence concepts, the law of large numbers, central limit theorem; estimation, Bayes estimators, interval estimation including confidence intervals, prediction intervals, Bayesian interval estimation; hypothesis testing, likelihood ratio tests; Bayesian tests; nonparametric methods, bootstrap. With fundamental operational observations, signaling deviations and raising alarms when necessary providing essential knowledge applicable an., data mining to complex data types are covered of finding patterns in data Science techniques to,! Multiple types of constraints, e.g connections to UC Berkeley ’ s program Indiana... ) in Analytics programme is taught in English and was set up co-creation! 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