2014 SAS Business Knowledge Series A unique collaboration between SAS and a global network of industry experts who deliver the most current information on business practices, concepts, methodology and techniques to help you get the most value out of your SAS investment. Grow with us sas.com/hongkong/training +852 2105 3533 hkcrm@sas.com 17JUL2014
SAS Business Knowledge Series Looking for real-world solutions from experts you can trust? I encourage you to join thousands of fellow professionals worldwide who have profited from the dynamic training offered through our popular Business Knowledge Series. For 13 years, this series has addressed critical issues surrounding business analytics in a variety of fields, including finance, healthcare, insurance and retail. With more than 40 classes delivering valuable information on business practices, concepts, methodology and techniques, there s an expert available to help you in your industry. Larry Stewart Vice President of SAS Education SAS EDUCATION Grow with us sas.com/hongkong/training +852 2105 3533 hkcrm@sas.com 2 SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright SAS Institute Inc. All rights reserved.
SEPTEMBER Course Code - FDSNA71 Fraud Detection using Supervised, Unsupervised and Social Network Analytics Presented by Dr. Christophe Mues, Assistant Professor at the School of Management of the University of Southampton (UK) Course Overview Learn how analytics can be used to fight fraud by learning fraud patterns from historical data and discuss the use of supervised learning, unsupervised learning and social network learning. The techniques discussed can be applied across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, counterfeit, The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. The lecturer will also extensively report on his recent research insights about the topic. Various real-life case studies and examples will be used for further clarification. Course objectives Learn how to Preprocess data for fraud detection (sampling, missing values, outliers, categorization, ) Build fraud detection models using supervised analytics (logistic regression, decision trees, neural networks, ensemble models, ); Build fraud detection models using unsupervised analytics (hierarchical clustering, non-hierarchical clustering, k-means, selforganizing maps, ); Build fraud detection models using social network analytics (homophily, featurization, egonets, PageRank, ) Who should attend Job profiles: Fraud analysts, data miners, data scientists Consultants working in fraud detection Industries: Financial services, government, healthcare, insurance, 3
sas.com/hongkong/training +852 2105 3533 hkcrm@sas.com Course Schedule September 15-16, 2014 (2.0 days) Course Fee HKD14,500 / *ETP14,500 *Enterprise Training Points Formats Classroom Prerequisites Before attending this course, you should have a basic knowledge of statistics (e.g. descriptive statistics, confidence intervals, hypothesis testing). Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary. This course addresses SAS Enterprise Miner software and SAS Social Network Analytics. Course Outline Introduction Fraud detection Data preprocessing Supervised methods for fraud detection Unsupervised methods for fraud detection Social networks for fraud detection Instructor Dr. Christophe Mues is an assistant professor at the School of Management of the University of Southampton (UK). One of his key research interests is in the business intelligence domain, where he has investigated the use of decision table and diagram techniques in a variety of problem contexts, most notably business rule modeling and validation. Two other key research areas are knowledge discovery and data mining, with a strong interest in applying data mining techniques to financial risk management and, in particular, credit scoring. He has cooperated with public services, companies, and financial institutions in each of these areas, and his findings have been published in various journals and presented at international conferences. He has taught training courses on Credit Scoring for Basel II in several European and Asian countries, all in collaboration with SAS. 4
October Course Code - BEAP71 Exploratory Analysis for Large and Complex Problems Using SAS Enterprise Miner Presented by Jeff Zeanah, President of Z Solutions, Inc. Course Overview This course is intended for analysts working with virtually any type of exploratory data analysis problem. Discovery in a complicated data set is one of the analyst's toughest problems. The course covers this discovery process using many real-world problems. There is a focus on fraud detection, with the recognition that the core principles of modeling to solve fraud detection are the basis of all exploratory data analysis. Analytical methods used in the course include decision trees, logistic regression, neural networks, link analysis, and social network analysis. In addition, analysts receive practical advice on presenting complex findings to their audience. Course objectives Learn how to analyze in multiple dimensions escape the limits of common methods explore your most complex problems successfully present findings to your audience find rare events find hidden relationships reach deep into your data and find what others cannot. Who should attend Data analysts (market researchers, fraud researchers, and sales analysts); expert modelers or those who want to become expert; and the creative and curious 5
sas.com/hongkong/training +852 2105 3533 hkcrm@sas.com JULY Course Schedule October 28-29, 2014 (2.0 days) Course Fee HKD14,500 / *ETP14,500 *Enterprise Training Points Formats Classroom Prerequisites To maximize the return on investment from the class, you should have the following skills and experience: background in analytical methods experience with predictive modeling familiarity with Base SAS, SAS/STAT, and SAS/GRAPH software, which you can acquire by taking the SAS Programming 1: Essentials or Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. This class is taught in SAS Enterprise Miner and foundation SAS. Familiarity with SAS Enterprise Miner at the level presented in the Applied Analytics Using SAS Enterprise Miner course is helpful. Most of the techniques shown in this course using SAS Enterprise Miner are supplemented with similar approaches in foundation SAS. Course Outline Predictive Analytics and Exploratory Data Mining Working with Unstructured Data Exploratory Data Mining and Predictive Models Complex Exploratory Modeling Exploratory Findings Instructor Jeff Zeanah is the President of Z Solutions, Inc., a firm focused on the support of organizations through predictive analytics and exploratory data analysis. His primary interest and research addresses the problems that organizations face in improving their business decisions through data analysis, neural networks, predictive analytics, exploratory data analysis and the selling of the analytical results. Jeff has consulted with industry leaders in manufacturing, high tech, retail, public health, science, finance, nutrition, and utilities. He is the developer of exploratory approaches and techniques that have been used by Fortune 500 companies, independent researchers, government agencies, and over 30 universities worldwide. Jeff's practice has been in areas as diverse as sizing electric transformers (for which he holds a U.S. patent), market research, fraud detection, health systems, and wine making. In addition to delivering the Business Knowledge Series course that he developed, he is also a contract instructor for SAS, and he serves on the board of the Institute for Business Intelligence at The University of Alabama. 6
November Course Code - BDTX Exploration and Predictive Analytics Using SAS Text Analytics Presented by Jason Loh, product manager, information management & analytics of SAS North Asia regional team Course Overview Confronted with big data issues, many organizations struggle to get the best possible value from text data. Because of data ambiguity and complexity, it s not easy to discern, quantify, analyze or exploit insights from text-based data. Analytics and Marketing executives struggle to combine text-based information with structured data to get a full, accurate view of the enterprise. Customers use SAS to combine structured and unstructured text data into organizational assets - to assess, analyze, understand and act upon the insight buried in electronic text including social media content, call center logs, product choices, customer applications and more. As a result, customers make effective, proactive business decisions, streamline priorities and achieve critical ROI in highly competitive markets. Course objectives This course is designed to introduce Big Data analysis methods and technologies to analytics/ marketing teams. Throughout the course, you will learn the concepts around the foundation of SAS Text Analytics and SAS Natural Language Processing, entity extraction, categorization, taxonomy building, taxonomy accuracy tests how to discover topics with Contextual Analysis Text Parsing, Text Filtering, Text Topic discovery, automatically building categorization rules how the global organizations are leveraging Big Data Analytics in practical business applications around the world. Who should attend Analytics and Marketing executives with the functions of: Marketing Brand Strategy Digital Analytics Customer Intelligence Marketing Strategy and Innovation Global Marketing Customer Relationship and Engagement Brand Innovation Marketing and Communications 7
sas.com/hongkong/training +852 2105 3533 hkcrm@sas.com JULY Course Schedule November 20-21, 2014 (1.5 days) Course Fee HKD10,900 / *ETP10,900 *Enterprise Training Points Formats Classroom Prerequisites Some experience with SAS and SAS Enterprise Miner is useful, but it is not mandatory. No experience with text analysis is necessary. This course addresses the topic of text analytics, with demonstrations and exercises with SAS Contextual Analysis and related SAS technologies. Course Outline Introduction Global Business Trends in Unstructured Data Analytics Understanding customer digital lifestyle from online behavior and preferences Leveraging both structured and unstructured data Where will we go today? Instructor Jason Loh is a Product Manager for Information Management & Analytics of SAS North Asia regional team, with a focus on Text Analytics amongst other SAS solutions. He graduated with a double degree in Business and IT from Monash University, Australia and been working in the field of analytics for 12 years and the recent 5 years in SAS in advisory and technical roles, presenting annually in Text Analytics/ Analytics public conferences and customer knowledge sharing sessions, and conducted workshops for National University of Singapore. Jason is involved in the design/ delivery of a range of successful analytics projects for customers in sectors including government/ manufacturing/ banking and communications across Asia. 8
SAS Training Course Registration Form Course Information Course Title Course Code Schedule Date Course Fee Delegate Information Name Title Address Company Department Telephone Payment Information Email By Enterprise Training Points (Ref No. ) Bill my company (please fill in Billing Information) Billing Information Name Title My Billing Information is the same as above Billing Address Telephone I acknowledge and agree the Terms and Conditions Company Department Email Delegate Signature Authorized Signature with Company Chop (Applicable to company enrollment) Date Guidelines: Completed registration form can be email to hkcrm@sas.com or fax to 2568 7218 for submission. If more than one delegate for the same account registration, please provide a separate list for the additional delegate details. No registration will be processed unless this form is completed with authorized signature and company chop. Please forward your registration form at least 15 working days before the scheduled date of each course. A confirmation email and a hardcopy invoice will be sent to you before the scheduled date of the course. Post-dated cheque is not acceptable. Please refer to Terms and Conditions on SAS HK Education website for other terms : http://www.sas.com/offices/asiapacific/hongkong/training/terms/ sas.com/hongkong/training +852 2105 3533 hkcrm@sas.com