SAS ®

Business Knowledge

Series

2014

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Grow with us

17JUL2014

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.

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

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SAS EDUCATION

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Fraud Detection using Supervised,

Unsupervised and Social Network Analytics

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, self-

organizing 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, …

Presented by Dr. Christophe Mues, Assistant Professor at the School of Management of the University of Southampton (UK)

Course Code - FDSNA71

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SEPTEMBER

Course Fee

HKD14,500 / *ETP14,500

*Enterprise Training Points

Formats

Classroom

Course Schedule

September 15-16, 2014 (2.0 days)

Course Outline

Introduction

Fraud detection

Data preprocessing

Supervised methods for fraud detection

Unsupervised methods for fraud detection

Social networks for fraud detection

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.

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.

Instructor

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Exploratory Analysis for Large and Complex

Problems Using SAS Enterprise Miner

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

Presented by Jeff Zeanah, President of Z Solutions, Inc.

Course Code - BEAP71

5

October

JULY

Course Fee

HKD14,500 / *ETP14,500

*Enterprise Training Points

Formats

Classroom

Course Schedule

October 28-29, 2014 (2.0 days)

Instructor

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.

This course addresses SAS Enterprise Miner software.

Course Outline

Predictive Analytics and Exploratory Data Mining

Working with Unstructured Data

Exploratory Data Mining and Predictive Models

Complex Exploratory Modeling

Exploratory Findings

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.

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Exploration and Predictive Analytics Using

SAS Text Analytics

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

Presented by Jason Loh, product manager, information management & analytics of SAS North Asia regional team

Course Code - BDTX

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November

JULY

Course Fee

HKD10,900 / *ETP10,900

*Enterprise Training Points

Formats

Classroom

Course Schedule

November 20-21, 2014 (1.5 days)

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?

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.

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.

Instructor

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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 :

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