Students are required to fulfill the remaining 8 MCs of Faculty requirements from any two of the following subject groups: Chemical Sciences, Life Sciences, Physical Sciences or Multidisciplinary & Interdisciplinary Sciences; but not from the following subject groups: Computing Sciences and Mathematical & Statistical Sciences. Build a data-driven organisation. An ability to function effectively in teams to accomplish a common goal. NUS Graduate School for Integrative Sciences and Engineering The Bachelor of Science (Business Analytics) degree programme is an inter-disciplinary undergraduate degree programme offered by the School of Computing with participation from the Business School, Faculty of Engineering, Faculty of Science, and Faculty of Arts and Social Sciences. The NUS-ISS Stackable Certificate Programme in Data Science, leading to the NUS Master of Technology in Enterprise Business Analytics is designed to meet the industry demand for data scientists who can help organisations achieve improved business outcomes through data insights. (Hons.) Chua Bee Luan is a Senior Lecturer in Data Analytics and Visualization with the School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS). The NUS-ISS Stackable Certificate Programme in Data Science, leading to the NUS Master of Technology in Enterprise Business Analytics is designed to meet the industry demand for data scientists who can help organisations achieve improved business outcomes through data insights. Academic Information / Policies Dates 1 Dec & 7 Dec 2020 (9am - 5pm) 10 Dec 2020 (9am - 11am) Duration 2.5 Days Course Overview To gain understanding and working knowledge of Data Analytics and Decision Science. Analytics is the art and science of integrating data, modelling and computation to generate insights and decisions that add value. He is a Certified Information Systems Security Professional (CISSP) and has a dozen other IT certifications. Students in the DSA programme have the option to participate in co-op education which comprises the following study/internship sequence: The first three internship segments ride on the Undergraduate Professional Internship Programme (UPIP) of the Faculty of Science. – DSA2102 Essential Data Analytics Tools: Numerical Data analytics empowered by Python programming skills will provide you, as a professional, as well as the organization you work for, a competitive edge in the market. The NUS Co-Operative (Co-Op) Education Programme formally integrates academic studies with relevant work experience, where students complete multiple internship stints alternating with regular academic semesters over their candidature at NUS thus forming an integral part of the student’s learning experience. From beginner courses that introduce basic concepts to more technical programming and visualisation techniques, you will acquire data analytics skills and learn the real-life applications of data science. DSC2008 Business Analytics–Data and Decisions “Most companies today have plenty of data. CS4224   Distributed Databases The Master of Science in Business Analytics (MSBA) can be taken either as a 1-year (13-month) intensive full-time programme or a 2-year part-time programme. In addition to learning knowledge in data science, students will also have opportunities to explore the integration of machine learning and data analysis in sectors such as financial industry, healthcare and government. CS4243   Computer Vision and Pattern Recognition AY2016/17 CS5340   Uncertainty Modelling in AI Data Analytics Academic/Training Unit: SCALE Department Module/Course Title Module/Course Code Course Type INSTITUTE OF SYSTEMS SCIENCE NICF - Predictive Analytics - Insights of Trends IDS coordinates and supports data science research initiatives across NUS.The value of the solutions that IDS can provide is through combining the expertise from multiple disciplines to tackle a problem well-roundedly from an all angles. His research interests are in the domain of data-driven decision-making and optimization under uncertainty. The Institute of Data Science (IDS) agglomerates, and further builds on, substantial strengths in data science and analytics in NUS. Note that the BT4101 project selection process takes place one semester ahead of the semester in which the students commence BT4101. 96 MCs Total Data literacy skills are relevant to everyone – from managers and CXOs to technical professionals. The four-year direct Honours programme in Data Science and Analytics (DSA) is designed to prepare graduates who are ready to acquire, manage and explore data that will inspire changes around the world. School of Computing Singapore is a financial hub, with key industries focusing on biomedical sciences, health care, manufacturing, e-commerce and sustainable energy, among others. The DSA programme will equip its graduates with the skills to contribute to the activities of these industries. APPLICATION PERIOD #1. NUS Bulletin AY2020/21 Learn big data and data … Programme Structure and Curriculum Rationale. Faculty of Law On a daily basis, large data sets are routinely generated by activities in the sciences, administration, leisure and commerce. – Six additional modules from List A and List B subject to the About NUS ST3233    Applied Time Series Analysis 160 MCs To be awarded a B.Sc. Year 2 ST3247    Simulation Faculty of Arts & Social Sciences   20 MCs   Business Analytics is the perfect degree for anyone interested in Statistics, Business, Computing, and of course, using data to create change. It also combines data analytics with machine learning. The DSA programme will equip its graduates with the skills to contribute to the activities of these industries. NUS Business Analytics Centre (BAC) NUS BAC offers the Master of Science in Business Analytics programme, develops business analytics professionals and collaborate with industry partners to meet the needs of companies across various industries actively seeking for talent who excel in the analytics … Business decisions are … – DSA2101 Essential Data Analytics Tools: Data Visualisation Mr Chan holds a Bachelor Degree (First-Class Honours) and a Master’s Degree in Engineering from the National University of Singapore (NUS). Centre for English Language and Communication, S. Other Multidisciplinary/Special Programmes, Pre-University Students from Local Institutions, Graduate Continuation and Graduation Requirements, Non-Graduating Programme Application & Eligibility, Responsibility for Notices/Circulars and Updates of Personal Particulars, Notes on Leave Matters for Research Scholars (RS), Open Researcher and Contributor ID (ORCID), Social Visit Pass for Spouse and Child of Full-Time Graduate Research Students, Important Information to Note Upon Cessation of Studies at NUS, Satisfactory / Unsatisfactory (S/U) Option, School of Continuing and Lifelong Education, NUS Graduate School for Integrative Sciences and Engineering, Centre for English Language and Communication, Other Multidisciplinary/ Special Programmes. with a primary major in Data Science and Analytics, candidates must satisfy the following: – CS1010/CS1010S/CS1010X Programming Methodology, – DSA2101 Essential Data Analytics Tools: Data Visualisation, – DSA2102 Essential Data Analytics Tools: Numerical, – MA2311 Techniques in Advanced Calculus or MA2104 Multivariable Calculus, – DSA3102 Essential Data Analytics Tools: Convex Optimisation, – DSA4199 Honours Project in Data Science or, – Six additional modules from List A and List B subject to the, + There must be at least two modules each from List A and, + A maximum of two DSA426x series modules can be used to fulfil this requirement, + There must be at least four modules at level 4000, DSA4211  High-Dimensional Statistical Analysis, DSA4212  Optimisation for Large-Scale Data-Driven Inference, DSA426x Sense-making Case Analysis: YY and ZZ, List B1 — DSA-recognised modules (no hidden pre-requisites), ST3232    Design and Analysis of Experiments, ST3240    Multivariate Statistical Analysis, ST4231    Computer Intensive Statistical Methods, List B2 — DSA-recognised modules (with hidden pre-requisites) *, CS3230   Design and Analysis of Algorithms, CS3243 Introduction to Artificial Intelligence, CS4225  Big Data Systems for Data Science or Massive Data Processing Techniques in Data Science, CS4231   Parallel and Distributed Algorithms, CS4243   Computer Vision and Pattern Recognition. 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