SAS Visual Statistics

標題 等級 培訓格式
FDP - Shaping a Business Analytics Curriculum
This outline is provisional and subject to change.The Faculty Development Program (FDP) teaches you fundamental concepts and relevant techniques in self-service data preparation, business visualization, and modeling using a powerful mix of point-and-click visual SAS tools. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advanced analytics for designing better curriculum and effective pedagogy.

0 No level Live Web Classroom
FDP - Shaping an Analytics and Machine Learning Curriculum
This outline is provisional and subject to change.The Faculty Development Program (FDP) teaches you fundamental concepts and relevant techniques in analytics and machine learning using a powerful mix of point-and-click visual SAS tools, including visual analytics, visual data mining and machine learning, and text analytics. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advanced analytics for designing better curriculum and effective pedagogy.

0 No level Live Web Classroom
Data Mining Techniques: Predictive Analytics on Big Data
This course introduces applications and techniques for assaying and modeling large data. The course also presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models, and mixture distribution models. Students perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics, and SAS In-Memory Statistics.

3 Intermediate e-Learning
SAS Visual Statistics: Interactive Model Building
本課程為SAS 商業智慧進階課程,介紹如何利用SAS Visual Statistics 進行高互動性探索式模型建置。探索式模型建置是巨量資料建模中的一個重要環節。

3 Intermediate Classroom e-Learning
Advanced Machine Learning Using SAS Viya
This course teaches you how to optimize the performance of predictive models beyond the basics by implementing various data munging and wrangling techniques. The course continues the development of supervised learning models that begins in the Machine Learning Using SAS Viya course and extends it to ensemble modeling. Running unsupervised learning and semi-supervised learning models are also discussed. In this course, you learn how to do feature engineering and clustering of variables, and how to preprocess nominal variables and detect anomalies. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. Importing and running external models in Model Studio is also discussed, including open source models. SAS Viya automation capabilities at each level of machine learning are also demonstrated, followed by some tips and tricks with Model Studio.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

4 Expert e-Learning