Business Knowledge Series Instructors

In this course, students learn how to develop credit risk models in the context of the Basel guidelines. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and exercises.

Please note: This course is not intended to teach credit risk modeling using SAS. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary.

Learn how to

• Develop probability of default (PD), loss given default (LGD), and exposure at default (EAD) models.
• Validate, backtest, and benchmark credit risk models.
• Stress test credit risk models.
• Develop credit risk models for low default portfolios.
• Use new and advanced techniques for improved credit risk modeling.

Who should attend

Anyone who is involved in building credit risk models or is responsible for monitoring the behavior and performance of credit risk models

In today's big data world, many companies have gathered huge amounts of customer data about marketing success, use of financial services, online usage, and even fraud behavior. Given recent trends and needs such as mass customization, personalization, Web 2.0, one-to-one marketing, risk management, and fraud detection, it becomes increasingly important to extract, understand, and exploit analytical patterns of customer behavior and strategic intelligence. This course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data science techniques for advanced customer intelligence applications. This highly interactive course provides a sound mix of both theoretical and technical insights as well as practical implementation details and is illustrated by several real-life cases. References to background material such as selected papers, tutorials, and guidelines are also provided.

Learn how to

• Apply a series of powerful, recently developed, cutting-edge analytical and data science techniques.
• Ensure the practical application of these techniques to optimize strategic business processes and decision making.
• Explore a futuristic vision of how emerging data science techniques might change your key business processes.
• Deploy, monitor, and optimally backtest analytical models.

Who should attend

Those involved in estimating, monitoring, auditing, or maintaining models for various types of customer intelligence; those involved with using data mining techniques for various types of customer intelligence, job titles including business analysts in various settings (for example, risk management, manufacturing, telco, retail, advertising, public, pharmaceutical, and so on), marketing/CRM managers, fraud managers, customer intelligence managers, risk analysts, CRM analysts, marketing analysts, senior data analysts, and data miners

A typical organization loses an estimated 5  percent of its yearly revenue to fraud. This course shows how learning fraud patterns from historical data can be used to fight fraud. The course discusses the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled data set), and social network learning (using a networked data set). The techniques 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, and counterfeiting. The course provides a mix of both theoretical and technical insights, as well as practical implementation details. During the course, the instructor reports extensively on his recent research insights about the topic. Various real-life case studies and examples are presented for further clarification.

Learn how to

• Preprocess data for fraud detection (sampling, missing values, outliers, categorization, and so on).
• Build fraud detection models using supervised analytics (logistic regression, decision trees, neural networks, ensemble models, and so on).
• Build fraud detection models using unsupervised analytics (hierarchical clustering, non-hierarchical clustering, k-means, self organizing maps, and so on).
• Build fraud detection models using social network analytics (homophily, featurization, egonets, PageRank, bigraphs, and so on).

Who should attend

Fraud analysts, data miners, and data scientists; consultants working in fraud detection; validators auditing fraud models; and researchers in financial services companies, banks, insurance companies, government institutions, health-care institutions, and consulting firms

Presented by Howard S. Friedman, Ph.D., Professor, Columbia University, and Partner, DataMed Solutions LLC

This course introduces some methods commonly used in program evaluation and real-world effectiveness studies, including two-stage modeling, interrupted time-series, regression discontinuity, and propensity score matching. These methods help address questions such as: Which medicine is more effective in the real world? Did an advertising program have an impact on sales? More generally, are the changes in outcomes causally related to the program being run?

Learn how to

• Identify alternative techniques to propensity score-based ones, including those that require different two-stage setups (Heckman) and time-series techniques
• Apply quasi-experimental analysis methods to real-world data for the following techniques: propensity score matching, Heckman's two-stage model, interrupted time series, and regression discontinuity.

Who should attend

Data analysts, statisticians, and economists in the fields of finance, telecommunications, pharmaceuticals, and retail and in the public sector, who have an understanding of basic statistics and SAS programming. Those who work in areas of economics, program evaluation, and real-world effectiveness studies will find this course highly relevant.

This course will help students learn basic concepts in Machine Learning with a specific focus on helping them be better data science customers. It will introduce basic definitions, key methodologies, and weave in discussions around key questions they should ask as customers.

Learn how to

• Distinguish between when a project might be appropriate for statistical analysis versus machine learning methods.
• Identify if the data science team has followed some of the best practices in model construction.
• Discuss with the data science team the methods they used for modeling along with the advantages and disadvantages of each method.

Who should attend

Business managers who currently work with data science teams or who plan to work with data science teams in the future.  Statisticians and data analysts who are interested in an introduction to the basic concepts of data science.

This industry-specific course focuses on the payer side of the industry: the origin, content, management, and use of administrative healthcare data. During the course, students become acquainted with the providers, payers, and users of the U.S. healthcare system. This course is not a hands-on programming course, but rather, it explores the business of medical claims data, health plan members, and service providers. As a supplement to this course, programmers should consider also registering for Administrative Healthcare Data and SAS: Hands-On Programming Workshop, a one-day course that focuses on programming methods and techniques that are useful in the healthcare industry. The two courses are offered on contiguous days.

Learn how to

• Recognize the different origins, content, and uses of administrative healthcare data.
• Understand the intricacies of national claims coding structures.
• Be aware of evolving issues that face industry payers.

Who should attend

Analysts and programmers who need to understand and work with administrative healthcare data

Presented by Mark Dalesandro, independent consultant, based on materials developed by Craig Dickstein, co-author of Administrative Healthcare Data: A Guide to Its Origin, Content, and Application Using SAS

This hands-on workshop is open to SAS programmers who attended the Administrative Healthcare Data and SAS course. This workshop explores programming methods and techniques that are useful in the management of administrative healthcare data.

Learn how to

• Take advantage of the medical claims code architecture. 
• Visualize data anomalies.
• Construct data-driven programs.
• Build and use utility macros.
• Transform data structures for ease of access. 

Who should attend

SAS programmers who have taken the Administrative Healthcare Data and SAS course

Presented by Eric Siegel, Ph.D., founder of Predictive Analytics World, author of Predictive Analytics, and former Columbia University professor.

• Accessible to business learners and yet vital to techies as well
• A vendor-neutral, universally applicable curriculum
• Equivalent to a full-semester MBA or graduate-level course

Machine learning is booming. It reinvents industries and runs the world. According to Harvard Business Review, machine learning – also known as predictive analytics – is “the most important general-purpose technology of our era.”

But while there are so many how-to courses for hands-on techies, there are practically none that also serve the business leadership of machine learning. This is a striking omission since success with machine learning relies on a very particular project leadership practice just as much as it relies on adept number crunching. Without that leadership, most machine learning projects fail.

By filling that gap, this course empowers you to generate value with machine learning, whether you are a techie, a business leader, or some combination of the two. It delivers the end-to-end expertise that you need, covering both the core technology and the business-side practice.

Why cover both sides? Because both sides need to learn both sides! Everyone leading or participating in the deployment of machine learning must study them both.

Beyond the core tech. As with most machine learning courses, you'll learn how the technical methods work “under the hood” – in an accessible way that's understandable to all learners. But you'll also continue beyond that to master critical business-side best practices that are usually omitted.

Learn how to

• Apply: Identify business opportunities for applying machine learning, to spike sales, accumulate clicks, fight fraud, and deny deadbeats.
• Plan: Determine how machine learning will drive operations, the staffing requirements to get there, and the projected win in terms of profit or ROI – and then internally sell the project, gaining buy-in from your colleagues.
• Lead: Manage or participate in the end-to-end implementation of machine learning, from the generation of predictive models to their launch into production.
• Watch your step: Circumvent the prevalent, treacherous pitfalls that otherwise derail machine learning projects and quell the overhype of “artificial intelligence.”
• Prep the data: Formulate the data requirements, which rely heavily on business priorities, and describe them in both technical and management-level language.
• Regulate: Foresee and mitigate ethical pitfalls, the risks to social justice that stem from machine learning (also known as AI ethics or equitable algorithms).

Who should attend

Anyone who wants to participate in the value-driven use of machine learning, no matter whether you will do so in the role of enterprise leader or quant. Since there is no hands-on and no heavy math (other than one spreadsheet-based exercise, as well as optional hands-on opportunities with SAS software), this program serves business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants. But technical learners should take another look. Before jumping straight into the hands-on, as data scientists are inclined to do, consider one thing: This holistic curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact. This course is also a good fit for university students, including those enrolled in an MBA program.

This course introduces electric load forecasting from both statistical and practical aspects using language and examples from the power industry. Through conceptual and hands-on exercises, participants experience load forecasting for a variety of horizons from a few hours ahead to 30 years ahead. The overall aims are to prepare and sharpen the statistical and analytical skills of participants in dealing with real-world load forecasting problems and improve their ability to design, develop, document, and report sound and defensible load forecasts.

According to statistics gathered on the first five offerings, this course was highly rated by students who ranged from new graduates with no industry or SAS experience to forecasting experts with over 30 years of industry experience and over 20 years of SAS programming background. The students represented all sectors of the industry: GT, ISO, distribution companies, REPs, IOU, co-op, municipal, regulatory commission, and consulting firm. Titles of the participants ranged from analyst, engineer, manager, to director and vice president.

For advanced topics, pair this course with Electric Load Forecasting: Advanced Topics and Case Studies. The two courses are offered on contiguous days.

Learn how to

• Classify load forecasts.
• Use basic graphic methods to discover the salient features of load profiles.
• Build a benchmark model for a wide range of utilities.
• Capture special effects for a local utility.
• Forecast loads for both small and large utilities.
• Improve very short-term forecasting accuracy.
• Perform weather normalization
• Use macroeconomic indicators for long-term load forecasts.
• Detect outliers
• Continue improving forecasting practice.
• Avoid making frequently made mistakes.

Who should attend

Load/price forecasters, energy traders, quantitative/business analysts in the utility industry, power system planners, power system operators, load research analysts, and rate design analysts

This hands-on workshop is open to those who attended the Electric Load Forecasting: Fundamentals and Best Practices course. This course includes lecture and hands-on lab exercises that explore advanced topics in electric load forecasting.

Learn how to

• Perform time series cross validation.
• Select weather stations.
• Detect outliers and cleanse data.
• Use comprehensive temperature information.
• Combine forecasts.

Who should attend

Load/price forecasters, energy traders, quantitative/business analysts in the utility industry, power system planners, power system operators, load research analysts, and rate design analysts