SAS Visual Statistics

Názov Úroveň Typ školenia
Strategies and Concepts for Data Scientists and Business Analysts Business Knowledge Series
To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.

In this course, you gain the skills data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data.

This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience.

3 Intermediate Live Web Classroom
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 New
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 New
This course introduces SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data. This course is appropriate for users of SAS Visual Analytics 7.2, 7.3, and 7.4.

The self-study e-learning includes:

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

3 Intermediate Live Web 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 process of building supervised learning models that begins in the SAS® Viya® :機械学習 course and extends it to ensemble modeling after assessing the classifier performance. In this course, you learn how to do feature engineering, cluster variables (as well as cluster observations), preprocess nominal variables, detect anomalies, and perform semi-supervised learning. 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 stage 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