We’re here to help. As we face COVID-19 together, our commitment to you remains strong. If you want to advance critical, job-focused skills, you’re invited to tap into free online training options or join Live Web classes, with a live instructor and software labs to practice – just like an in-person class.



SAS/ETS

Title Level Training Formats
Electric Load Forecasting: Fundamentals and Best Practices Business Knowledge Series
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: G&T, 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.

3 Intermediate Live Web Classroom
Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models Business Knowledge Series
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?

3 Intermediate e-Learning
Stationarity Testing and Other Time Series Topics Business Knowledge Series
This course addresses a basic question in time series modeling and forecasting: whether a time series is nonstationary. This question is addressed by the unit root tests. One of the most common tests, the Dickey-Fuller test, is discussed in this lecture.

3 Intermediate Classroom Live Web Classroom e-Learning
Electric Load Forecasting: Advanced Topics and Case Studies Business Knowledge Series
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.

4 Expert Classroom
Time Series Feature Mining and Creation
In this course, you learn about data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis.

3 Intermediate e-Learning
Statistics 2: ANOVA and Regression
This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course.

3 Intermediate Live Web Classroom e-Learning
Time Series Modeling Essentials
The course covers the fundamentals of modeling time series data, and focuses on the applied use of the three main model types used to analyze univariate time series: exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unobserved components (UCM).

3 Intermediate Classroom Live Web Classroom e-Learning
State Space Modeling Essentials Using the SSM Procedure in SAS/ETS
This course covers the fundamentals of building and applying state space models using the SSM procedure (SAS/ETS). Students are presented with an overview of the model and learn advantages of the State Space approach. The course also describes fundamental model details, presents some straightforward examples of specifying and fitting models using the SSM procedure, and considers estimation in SSM, focusing on the Kalman filter and related details. The course concludes with a variety of SSM modeling applications, focused mainly on time series.

4 Expert Live Web Classroom
Forecasting Using SAS Software: A Programming Approach

4 Expert Classroom Live Web Classroom