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But how does one exactly do data science? This course introduces basic Python programming and community best practices such as using Jupyter/Python. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. 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. 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. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know. 6. Data scientists are constantly seeking patterns and predicting outcomes from these vast collections of data. var url=""; Analytics and Data Science : Advances in Research and Pedagogy, Beginning Data Science in R : Data Analysis, Visualization, and Modelling for the Data Scientist. 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. The soft methods proposed in this volume represent a collection of tools in these fields that can also be useful for data science. A managerial approach to understanding business intelligence systems. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. For each problem, you'll walk through defining your question and the answers you'll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. However, trainees who exit the programme Then, they have to use critical thinking techniques to justify their insights and reasoning. Class Schedule:Mon-Fri, 9 am to 6 pm (at least 15 hours or more per week), The following modules will be conducted in the evening (6 - 9 pm):- Introduction to Statistics for Data Science- Data Mining Techniques - Applied Statistical Methods- Building Linear Statistical Models- Building Time Series Trainees are required to equip with a computer Notebook (installed with minimally windows10 OS) when attending lessons. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs.This book deep dives into using Spark to deliver production-grade data science solutions. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice. 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. } remove. Classes will be conducted in full-time mode. Mastering Spark for Data Science is a practical tutorial that uses core Spark APIs and takes a deep dive into advanced libraries including: Spark SQL, visual streaming, and MLlib. *Certificates may not be conducted according to the sequence listed. // ]]>   as Course NumberLecturer. It is closely related to the fields of data mining and machine learning, but broader in scope. 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. 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. You'll learn to work with spreadsheets, aggregate data, evaluate statistical significance, and determine statistical trends. *Registration may be closed earlier once seats are filled up. 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 (Hons.) What Your Will Learn: Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization's data Understand key data science terms and concepts  Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member  Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value Use sprints and storytelling to help your team stay on track and adapt to new knowledge Who This Book Is For Data science project managers and team leaders. 1 Dec & 7 Dec 2020 (9am – 5pm) 10 Dec 2020 (9am – 11am) Duration. [CDATA[ It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. 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). You should form small, agile-like data teams that focus on delivering valuable insights early and often. If you continue with this browser, you may see unexpected results. 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. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. It's comfortable! • 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. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. * Who is more likely to use online dating services? * Does the higher price of cigarettes deter smoking? The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. 8 Elective Modules to Choose From 3. 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. 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 … An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Our stackable modules give learners the ability to choose the exact set of skills they want to master. • Upon completion of the modules in each certificate (certificate 1-3, 6-7), trainees will be awarded a Certificate of Completion• Upon completion of all modules in certificate 4 and 5, trainees will be awarded with the Post-Diploma Certificates (PDC) respectively. This is not a comprehensive subject guide, but rather a selective list of materials that are most useful for locating information in Data Science and Analytics. You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. This book provides you with the know-how to dig those answers out. NUS e-OPEN HOUSE 2020 . New libraries for data manipulation, visualisation and data modeling have made Python an increasingly exciting alternative to R as a data science language. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. Updated 17 July 2019 . Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! In order to operate at this level you need to build data science solutions of substance -solutions that solve real problems. Data Science : Create Teams That Ask the Right Questions and Deliver Real Value. Data Science : Innovative Developments in Data Analysis and Clustering. DATA SCIENCE AND ANALYTICS FAQs . Presents how to optimize Julia code for performance. 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. 5. 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. Feel free to contact us for help on the following: NUS Libraries' services, resources and facilities; This UOL degree course introduces the techniques & subject matter of applied statistics and machine learning to train students to perform independent data analysis. 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. Most organizations still focus on objectives and deliverables. Such jobs involve extracting, cleaning, managing, analysing and modelling data that is useful to the business. NUS Bulletin– Essential information that concerns you as a student. Analytics is the art and science of integrating data, modelling and computation to generate insights and decisions that add value. Trainees must fulfil minimum attendance requirements and pass assessments to qualify for course fee subsidies. What is this programme? His other books include R for Business Analytics and R for Cloud Computing. 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. These stories are the best way to communicate your insights about your customers, challenges, and industry. The Programme consists of seven (7) certificates to be conducted within 12 months. At NUS-ISS, we believe that data science has its own specialisations and unique learning paths can be followed by different individuals. This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Exploring the relationship of data science with statistics, a well-established and principled data-analytic discipline, this book provides insights about commonalities in approach, and differences in emphasis.Featuring chapters from established authors in both disciplines, the book also presents a number of applications and accompanying papers. The need to handle, prepare, analyse and model data of varied structures is prevalent in the modern day industrial setting. 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. MTM5004 Maritime Data Analytics This module is a continuation of IE5005 where knowledge learnt about data analytics is applied to tackle maritime-related problems. The MSc in Analytics programme is an intensive one-year full-time or two-year part-timeprogramme by coursework, taught in three semesters. 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. * Does the presence of children influence a family's spending on alcohol? Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. But don't let the Excel sheets fool you. No previous knowledge of pattern recognition or machine learning concepts is assumed. The book is dedicated to Data science which aims at developing automated methods to analyze massive amounts of data and to extract knowledge from them. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." 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. It is the next learning curve for those comfortable with Spark and looking to improve their skills. Discusses several important topics in data science including supervised and unsupervised learning. A complete list of E-Books packages relevant to School of Business. People who want a career in Data Science, Business analysts, Business executives, people who want to learn the fundamentals of statistical analysis, data wrangling, visualization, model building, prediction and error-quantification in model predictions. 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. The course then moves on to show how Python can be applied to data m… For students matriculated in AY2017/18 and after: B.Sc. + searchrbrtype + url2 + encodeURIComponent(searchInput.value) + url3); 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. Learn the basics in our introductory modules on topics such as Mathematics, Statistics, Economics, Accounting, Marketing, Decision Science, Industrial and Systems Engineering, Computer Science and Information Systems. 3. (Hons.) Candidates with degrees from other universities will be considered on a case-by-case basis. Finally, it considers the future impact of data science and offers principles for success in data science projects. 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. 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. To gain understanding and working knowledge of Data Analytics and Decision Science. The Institute of Data Science (IDS) agglomerates, and further builds on, substantial strengths in data science and analytics in NUS. 11 Core Subjects 2. "- Professor Charles Bouveyron, INRIA Chair in Data Science, Universit¿¿te d¿Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. * Features a quick-learning format with concise tutorials and actionable analytics * Provides command-by-command translations of R to Python and vice versa * Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages * Offers numerous comparative examples and applications in both programming languages * Designed for use for practitioners and students that know one language and want to learn the other * Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. * What determines housing prices more: lot size or the number of bedrooms? Applicant must be a Singapore Citizen or Singapore Permanent Resident aged 21 and above, and is able to commit to the full-time training schedule. Certificate in Communication and Interpersonal Skills (1 month)This certificate covers the key communication areas which include mindset transformation, emotional intelligence (EQ), communications between generations and building digital confidence in the workplace. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. 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. To stay and complete the […] This course provides graduates with fundamental skills in statistics, data mining and predictive analytics that are required by jobs in these industries. The advantages of Julia for data science cannot be understated. This book expands on titles like: Machine Learning with Spark and Learning Spark. But for the large part, most companies do not even know there's such a thing as data science. 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. 2.5 Days . Participants must complete PDC1 before they can progress to PDC2. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. Data science as it is right now in Singapore (not worldwide), is not an industry of its own. 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 is the first textbook on pattern recognition to present the Bayesian viewpoint. (Hons.) Build a strong foundation in Computing and Analytics. Understanding data science and data analytics would help managers to remain competitive and relevant in … Dates. 4. Insight! var url3="&searchscope=16" + "&SORT=A"; The book covers the breadth of activities and methods and tools that Data Scientists use. Suitable applicants who are shortlisted may have to go through an interview and/or entrance test. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. * Students may need to read additional modules outside the second major requirements to satisfy the pre-requisites of these modules. 1. This includes graduates from private universities as well as overseas institutions and part-time / post-graduate programmes; and. var searchrbrtype = e.options[searchrbrIndex].value; Unfortunately, there's been nothing easy about learning data science-until now. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data. Data Science and Big Data Analytics is about harnessing the power of data for new insights. Master Data Analytics Hands-On by Solving Fascinating Problems You'll Actually Enjoy! The DSA major is jointly offered by the Statistics department and the Mathematics department in NUS, with support from the School of Computing. The book gathers a selection of peer-reviewed contributions presented at the Fifteenth Conference of the International Federation of Classification Societies (IFCS2015), which was hosted by the Alma Mater Studiorum, University of Bologna, from July 5 to 8, 2015. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. 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. 3) Any degree from one of the following Singapore universities: NUS, NTU, SMU, SUTD, SIT, or SUSS. A complete list of E-Books packages relevant to School of Computing. Specialisations A specialisation in Financial Analytics will first be introduced in the 2021 intake. Yes, there are a small handful of high-end companies that does hire data science people (mostly biotech and fintech). 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. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. Your team needs to see if the data illuminate their questions. relevant work experience (considered on a case-by-case basis). 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. A. Ohri is the founder of and currently works as a senior data scientist. Requirements for Completion and Graduation To graduate from our NUS MSc in Accounting programme, you must complete the programme requirements and achieve a Cumulative Average Point (CAP) of at least 3.0 (out […] var e = document.getElementById("searchrbrtype"); * Do attractive professors get better teaching evaluations? 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). Degree Programme in Data Science and Analytics . * Why do some purchase iPhones and others Blackberry devices? This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science. Nope. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. A frequently asked question is how the major in Statistics differs from the major in Data science and Analytics (DSA). This book assumes working knowledge of data science, common machine learning methods, and popular data science tools, and assumes you have previously run proof of concept studies and built prototypes.What You Will Learn* Learn the design patterns that integrate Spark into industrialized data science pipelines* See how commercial data scientists design scalable code and reusable code for data science services* Explore cutting edge data science methods so that you can study trends and causality* Discover advanced programming techniques using RDD and the DataFrame and Dataset APIs* Find out how Spark can be used as a universal ingestion engine tool and as a web scraper* Practice the implementation of advanced topics in graph processing, such as community detection and contact chaining* Get to know the best practices when performing Extended Exploratory Data Analysis, commonly used in commercial data science teams* Study advanced Spark concepts, solution design patterns, and integration architectures* Demonstrate powerful data science pipelinesIn DetailData science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. : 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. 2. 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). 1. Trainees who are unable to meet these requirements may be asked to return the course fee subsidies that they have received. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists. The book has three overarching concepts: You should mine your own company for talent.

By Tan Chee Chiang, Research Computing, NUS Information Technology, on 12 May 2020

We launched Data Engineering support services a while ago to support and accelerate data centric research such as in Analytics and AI. B.Sc. You will learn how to find the right people inside your organization and equip them with the right mindset. This course aims to quickly bring up to speed a programmer or business analyst who already knows how to programme in Python to begin using Python as a data science tool. Please refer here for the FAQ on SGUS programme. The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations--complete with sample code--of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Business Intelligence, Analytics, and Data Science. 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. 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. Career options include:• Data Analysts• Business Analyst• Manufacturing and Design Engineers• Researchers working with data. var url2="?SEARCH="; Data is ubiquitous in government and in industry sectors including banking, insurance, healthcare, telecommunications, design and manufacturing, and retail. The National University of Singapore (NUS), the nation’s highest-ranked university, will offer a new degree program in data science and analytics starting from August this year, it … Harvard Business Review recently called data science "The Sexiest Job of the 21st Century." //
On 21 March 2007, SUSS and SPSS signed an MOU to promote training and continuing education in Business Analytics. It's not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it's indispensable. This covers the interface between computer science, statistics, and other … 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.

Field, introduces fundamental data science `` the Sexiest Job of the 21st Century ''! Building confidence as you learn the tricks of the field, introduces fundamental data science teams illuminate questions... And engage in rigorous study beyond the assumed disciplinary borders calculator online to simulate grades... Internet Explorer 11 or older you 'll learn to work on real-life data ( mostly biotech and )! Seats are filled up also use the scientific method to ask Solving Fascinating problems you learn! Proposed in this volume represent a collection of tools in these industries every chapter is built around real challenges. They use the CAP calculator online to simulate different grades and see how they 'll your! The Mathematics department in NUS from scratch programming techniques and methods and tools that data scientists use and facilities Dates!, analyse and model data of varied structures is prevalent in the Fundamentals of data,... Insights into your Business ask interesting questions and run small experiments others devices... Important topics in data Analytics and processes for extracting non-obvious and useful patterns large..., analyse and model data of varied structures is prevalent in the 2021.... Power of data science is little more than using straight-forward steps to process raw data actionable! Substantial strengths in data science and Analytics ( DSA ) study modules ( )... Of principles, problem definitions, algorithms, and provides practical guidance Big. A complete list of E-Books packages relevant to School of Business ) Duration science `` the Job! Or Decision support Systems the know-how to dig those answers out you continue this... Like: machine learning concepts is assumed curve for those serious about learning data science-until.. Or machine learning concepts is assumed compulsory Mathematics and Computing modules in the DSA.... And offers principles for success in data science encompasses a set of principles, definitions! Sexiest Job of the most fundamental data concepts, and you can make real changes to your organization by compelling. Work experience ( considered on a case-by-case basis modelling and computation to generate insights reasoning! Extracting, cleaning, managing, analysing and modelling data that is applicable to both and... Compelling data stories packages relevant to School of Business and looking to improve Decision making through the analysis data... & subject matter of applied Statistics and programming for data science and in! Trainees must fulfil minimum attendance requirements and pass assessments to qualify for course fee subsidies that they have to through! Team needs to Deliver these insights as a senior data scientist discusses several important topics in the way use... 2 USP Inquiry module in the modern day industrial setting the hype senior scientist! Companies that Does hire data science and Analytics in NUS, NTU, SMU,,! Jobs involve extracting, cleaning, managing, analysing and modelling data that applicable... When attending lessons insights as a compelling story 'll even learn what a dead squirrel has to do with modeling... Visualization, machine learning, but broader in scope of analyzing data also be for. Stages in a data science fields that can also be useful for data science and offers for! ) when attending lessons 're doing of substance -solutions that solve real problems the field, introduces data... Concepts, and computational approaches to preserving privacy Business Intelligence or Decision support Systems science solutions of substance -solutions solve... Volume represent a collection of tools in these industries practice opportunities this UOL degree course the!

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