B.Sc. A complete list of E-Books packages relevant to School of Business. Suitable applicants who are shortlisted may have to go through an interview and/or entrance test. (Hons.) It's not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it's indispensable. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science. You should form small, agile-like data teams that focus on delivering valuable insights early and often. Students will read modules in mathematics, statistics and computer science and will be exposed to the interplay of these three areas in the practice of data science. It delivers the most complete range of analytical solutions, using simple to advanced statistical techniques, predictive analytics and data mining technology to help organisations turn data into business insights.

On 21 March 2007, SUSS and SPSS signed an MOU to promote training and continuing education in Business Analytics. His other books include R for Business Analytics and R for Cloud Computing. This subject guide covers resources available at the NUS Libraries. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Trainees are required to equip with a computer Notebook (installed with minimally windows10 OS) when attending lessons. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. This includes graduates from private universities as well as overseas institutions and part-time / post-graduate programmes; and. MTM5004 Maritime Data Analytics This module is a continuation of IE5005 where knowledge learnt about data analytics is applied to tackle maritime-related problems. Everything's software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). They should pivot their efforts to keep their insights aligned with business value. * Students may need to read additional modules outside the second major requirements to satisfy the pre-requisites of these modules. 8 Elective Modules to Choose From 3. The Data Science and Analytics (DSA) programme is a multidisciplinary undergraduate degree offered by the Department of Practicum (3 months)This project module provides opportunities for learners embark on an industry project either in-house or in an attachment to a company or at the Digital Building Innovation Centre (DBIC). Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. They will read CP3209 Undergraduate Research Project in Computing (8 MCs) in place of the Business Analytics Capstone Project module (BT4103) in the Core modules requirement. Data Science gets thrown around in the press like it's magic. with a major in Statistics [160 MCs] B.Sc. * Do attractive professors get better teaching evaluations? Data Science and Big Data Analytics is about harnessing the power of data for new insights. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Instead, a data science team is exploratory. Business Intelligence, Analytics, and Data Science. Understanding data science and data analytics would help managers to remain competitive and relevant in … Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.Style and approachThis is an advanced guide for those with beginner-level familiarity with the Spark architecture and working with Data Science applications. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more.You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Candidates with degrees from other universities will be considered on a case-by-case basis. without a valid reason may be asked to return the course fee subsidy that they have received.• Refund of paid course fees will be pro-rated based on certificates that have not commenced• Trainees who wish to exit from the programme Today's messy glut of data holds answers to questions no one's even thought to ask. The MSc in Analytics programme is an intensive one-year full-time or two-year part-timeprogramme by coursework, taught in three semesters. The book has three overarching concepts: You should mine your own company for talent. You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Nope. The specific objectives of the SGUS Specialist Diploma in Data Science and Analytics are to provide training in the fundamentals of statistics and programming for data science, as well as in specialised skills in the areas of data mining, applied statistical methods, statistical modelling and prediction. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. 11 Core Subjects 2. It looks like you're using Internet Explorer 11 or older. The formula here is quite simple, and you can find more about the CAP system on the NUS website. must write to ptenquiry@sp.edu.sg, Eligible for SkillsFuture CreditTerms and Conditions. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know. All core and elective modules are worth 4 MCs each, unless stated otherwise. Introduction. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! (Hons.) For courses on Business Intelligence or Decision Support Systems. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. But for the large part, most companies do not even know there's such a thing as data science. NUS PhD (Data Science) The NUS PhD (Data Science) programme is jointly hosted by the NUS Graduate School for Integrative Sciences and Engineering (NGS) and the Institute of Data Science (IDS).It prepares students with a distinctive set of interdisciplinary skills for careers in academia or industry. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Graduates of the course will be trained in preparing data, summarising and presenting data, performing statistical analysis of univariate and multivariate data, using descriptive models to uncover patterns in data, developing, applying and deploying predictive models and quantifying risks associated with prediction. b) Graduated from the educational institutions mentioned in (a) and completed or completing their National Service (NS) in CY 2019 / 2020 and are entering the labour force. Data Science and Predictive Analytics : Biomedical and Health Applications using R, Data Science for Healthcare : Methodologies and Applications, Data Science Landscape : Towards Research Standards and Protocols, "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. For students matriculated in AY2017/18 and after: B.Sc. The Polytechnic reserves the right to shortlist and admit applicants. Students will complete 5 essential modules to build a cross-disciplinary foundation for Business Analytics and engage in rigorous study beyond the assumed disciplinary borders. Upon successfully acquiring these 2 PDCs, trainees will be conferred with a Specialist Diploma in Data Science (Data Analytics) from SP• Upon completion of all the certificates, trainees will be awarded with a Certificate in SGUS Specialist Diploma in Data Science & Analytics from SP. It has never been easier for organizations to gather, store, and process data. The Data Analytics and Consulting Centre is a consulting unit closely linked with the DSA programme. Build a strong foundation in Computing and Analytics. Subsidised Fees for Singaporeans & Singapore PR (inclusive of GST): $1,000.00. *Registration may be closed earlier once seats are filled up. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Career options include:• Data Analysts• Business Analyst• Manufacturing and Design Engineers• Researchers working with data. Data Science : Innovative Developments in Data Analysis and Clustering. Most organizations still focus on objectives and deliverables. It's comfortable! Classes will be conducted in full-time mode. var url="http://linc.nus.edu.sg/search/"; 3. Analytics and Data Science : Advances in Research and Pedagogy, Beginning Data Science in R : Data Analysis, Visualization, and Modelling for the Data Scientist. This covers the interface between computer science, statistics, and other … These stories are the best way to communicate your insights about your customers, challenges, and industry. The MSc in Data Science and Machine Learning programme is offered jointly by the Department of Mathematics, the Department of Statistics and Applied Probability and the Department of Computer Science with support from the Faculty of Engineering, … It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. Upon completion of study, students are awarded a Master of Science (MSc) in Analytics degree. Finally, your team needs to deliver these insights as a compelling story. A complete list of E-Books packages relevant to Faculty of Science. Section 1: Introduction to Course and Python Fundamentals – In this introduction, an overview of key Python concepts is covered as well as the motivating factors for building industry professionals to learn to code. Yes, there are a small handful of high-end companies that does hire data science people (mostly biotech and fintech). Updated 17 July 2019 . Degree Programme in Data Science and Analytics . Teach your team to ask the right questions to gain actionable insights into your business. Certificate in Data Analytics (2 months)To provide training on specialised skills in the area of data mining and applied statistical methods. 6. Finally, it considers the future impact of data science and offers principles for success in data science projects. Presents how to optimize Julia code for performance. Data scientists are constantly seeking patterns and predicting outcomes from these vast collections of data. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Your team needs to see if the data illuminate their questions. The big data evolution provides an opportunity to manage significantly large amounts of information and act on them with analytics for improved diagnostics and prognostics. To stay and complete the […] var searchrbrIndex = document.getElementById("searchrbrtype").selectedIndex; The soft methods proposed in this volume represent a collection of tools in these fields that can also be useful for data science. In addition, the target trainees for SGUS programmes will exclude the following: a) Graduated or graduating in Calendar Year (CY) 2019 / 2020 from ITE, Polytechnics, Universities, and other educational institutions. Participants must complete PDC1 before they can progress to PDC2. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. Certificate in Introduction to Analytics, Cyber Security, AI and Blockchain (1 month)This certificate introduces key concepts in the areas of data and visual analytics using Power BI, statistics, cyber security, blockchain, AI and machine learning. : How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Discusses several important topics in data science including supervised and unsupervised learning. Trainees must fulfil minimum attendance requirements and pass assessments to qualify for course fee subsidies. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. The need to handle, prepare, analyse and model data of varied structures is prevalent in the modern day industrial setting. As a Business Analytics student, you will gain a solid foundation in the statistical and analytical methods that make up the backbone of data science. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. The programme consists of a total of 30 Academic Units (AU), with 24 AU stemming from core courses and 6 AU from elective courses: 1. Certificate in Essential and Emerging Skills for Employability (1 month)This certificate covers fundamentals in various essential areas within an organisation, which includes human resource, finance, design thinking, robotic process automation and digital marketing. It is closely related to the fields of data mining and machine learning, but broader in scope. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell's Outliers: It teaches through a powerful narrative packed with unforgettable stories. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. * Who is more likely to use online dating services? with a major in Statistics (specialisation in Data Science) [160 MCs] B.Sc. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. You'll master data science by answering fascinating questions, such as: * Are religious individuals more or less likely to have extramarital affairs? Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. The goal of data science is to improve decision making through the analysis of data. • The programmes allow trainees to exit without penalty if they are successfully placed into a job or have secured a job on their own accord while undergoing training. * Does the higher price of cigarettes deter smoking? As an emerging discipline, data science broadly means different things across different areas. REQUIREMENTS FOR COMPLETION AND GRADUATION To graduate from our NUS MSc in Marketing Analytics and Insights programme, you must complete the programme requirements and achieve a Cumulative Average Point (CAP) of at least 3.0 (out of 5.0). Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science productsAbout This Book* Develop and apply advanced analytical techniques with Spark* Learn how to tell a compelling story with data science using Spark's ecosystem* Explore data at scale and work with cutting edge data science methodsWho This Book Is ForThis book is for those who have beginner-level familiarity with the Spark architecture and data science applications, especially those who are looking for a challenge and want to learn cutting edge techniques. This proceedings volume is a collection of peer reviewed papers presented at the 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016) held in Rome (Italy). Data Science and Big Data Computing This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. 1. Every chapter is built around real research challenges, so you'll always know why you're doing what you're doing. Each chapter will cover a different technique in a spreadsheet so you can follow along: Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through each technique. with a Major in Statistics [120 MCs] B.Sc. A managerial approach to understanding business intelligence systems. This book describes, simply and in general terms, the process of analyzing data. CP3209 is an independent study modules (ISM) which will be counted as 2 USP Inquiry module in the Sciences and Technologies domain. It is the next learning curve for those comfortable with Spark and looking to improve their skills. remove. Feel free to contact us for help on the following: NUS Libraries' services, resources and facilities; function RBRSearchGo(){ 1 Dec & 7 Dec 2020 (9am – 5pm) 10 Dec 2020 (9am – 11am) Duration. Applicants applying to be enrolled in this programme must satisfy at least one of the following entry requirements: 1) An engineering diploma from a polytechnic in Singapore, 2) Any diploma from a polytechnic in Singapore and. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. It shows how Data science employs various programming techniques and methods of data wrangling, data visualization, machine learning, probability and statistics. Our stackable modules give learners the ability to choose the exact set of skills they want to master. var url2="?SEARCH="; PDC 1 Certificate in Fundamentals of Data Science Module 1 - Introduction to Statistics for Data Science Module 2 - Introduction to Programming for Data Science Semester Two PDC 2 Certificate in Data Analytics Module 3 - Data Mining Techniques Module 4 - Applied Statistical Methods. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. Other specialisations may be added in the future. Statistical modeling, machine learning, and data mining--including supervised and unsupervised data mining methods--are treated in detail, as are time series forecasting, text mining, and natural language processing. 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. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data. IDS is a research institute that will be the focal point for all data science research and translation, education and related activities at NUS, which aims to contribute to Singapore’s Smart Nation initiative by developing deep and strategic capabilities in data science, analytics and optimisation, and cybersecurity.

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