Coronavirus: Die Gesundheit und Sicherheit unserer Kursteilnehmer und Mitarbeiter ist uns sehr wichtig. Live von einem Trainer durchgeführte Schulungen in unseren virtuellen Live Web Klassenzimmern ersetzen derzeit die Präsenzkurse aus dem offenen Kursprogramm. Viele Kurse sind auch als selfpaced E-Learning verfügbar. Bitte beachten Sie auch unser kostenloses Trainingsangebot.



SAS Visual Forecasting

Kurstitel Stufe Kursformat
FDP-Shaping a Data Science Curriculum: Data Management, Machine Learning and Artificial Intelligence
This FDP supports developing a data science program that covers a variety of topics and enables students to acquire the skills that industry is looking for their employees to have. The FDP helps universities develop a pool of talent with the range of analytical and technology skills to work in a data-rich business environment.

1 Einsteiger Live Web Classroom
Forecasting Using Model Studio in SAS® Viya®
This course provides a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya. The course begins by showing how to load the data into memory and visualize the time series data to be modeled. Attribute variables are introduced and implemented in the visualization. The course then covers the essentials of using pipelines for generating forecasts and selecting champion pipelines in a project. It also teaches you how to incorporate large-scale forecasting practices into the forecasting project. These include the creation of data hierarchies, forecast reconciliation, overrides, and best practices associated with forecast model selection.

2 Basiswissen Classroom Live Web Classroom e-Learning
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 Für Fortgeschrittene e-Learning
Large-Scale Forecasting Using SAS® Viya®: A Programming Approach
This course teaches students to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. For the course project, students build and then refine a large-scale forecasting system. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Students are then asked to improve overall baseline forecasting performance by modifying default processes in the system.

3 Für Fortgeschrittene e-Learning
Models for Time Series and Sequential Data
This course teaches students to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data (or ARMA) to models for trend and seasonality (ARIMA) and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms. The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations of creating combined (or ensemble) and hybrid model forecasts.

3 Für Fortgeschrittene e-Learning