SAS® 9 Predictive Analytics Learning​ Subscription

Want to learn how to predict the future? Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. This is where SAS excels. Go beyond knowing simply what happened in the past to predicting your best assessment of what will happen in the future.
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About this Subscription

More companies are turning to predictive analytics to increase their bottom line and drive a competitive edge. The interactive and easy-to-use software from SAS ensures that predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are now empowered to take advantage of these technologies, across all sectors, especially banking and financial services; retail; oil, gas and utilities; government; healthcare; and manufacturing. The most common uses of predictive analytics are detecting fraud, optimizing marketing campaigns, improving operations, and reducing risk.

Learn how to:

  • Engineer meaningful model inputs and preprocess data to improve model performance.
  • Integrate SAS predictive modeling capabilities with the R and Python programming languages.
  • Use predictive time-to-event modeling for customer history data using survival data mining methods.
  • Develop and evaluate profit-driven descriptive, predictive, and uplift analytics models.
  • Design, conduct, and analyze experiments specifically for marketing campaigns.
  • Manage analytical models using SAS Model Manager.
  • Perform predictive modeling with neural networks, tree models, and logistic regression models.
  • Use experimentation and incremental response models in data science.
  • Apply electric load forecasting for the power industry.

    • Before taking the predictive analytics courses in this subscription, you should have an understanding of basic statistical concepts, which you can gain from the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. It’s also recommended that you complete the  SAS® Programming 1: Essentials course or have equivalent knowledge. Both courses are available in instructor-led or free online e-learning formats.

    Courses Included

    Core Foundations of Predictive Analytics

    1

    • COURSE

      Applied Analytics Using SAS® Enterprise Miner™
      SHOW LESS ︿
      This course covers the skills that are required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). This course is appropriate for SAS Enterprise Miner 5.3 up to the current release.

      ENROLL

    • COURSE

      Predictive Modeling Using Logistic Regression
      SHOW LESS ︿
      This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets.

      ENROLL

    • COURSE

      SAS® Programming for R Users
      SHOW LESS ︿
      This course is for experienced R users who want to apply their existing skills and extend them to the SAS environment. Emphasis is placed on programming and not statistical theory or interpretation. Students in this course should have knowledge of plotting, manipulating data, iterative processing, creating functions, applying functions, linear models, generalized linear models, mixed models, stepwise model selection, matrix algebra, and statistical simulations.

      ENROLL

    • COURSE

      Strategies and Concepts for Data Scientists and Business Analysts
      SHOW LESS ︿
      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 that 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.

      ENROLL

    Advanced and Specialty Topics

    2

    • COURSE

      SAS® Enterprise Miner™ Integration with Open Source Languages
      SHOW LESS ︿
      This course introduces the basics for integrating R programming and Python scripts into SAS and SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data exploration, model prototyping, and supervised and unsupervised learning techniques.

      ENROLL

    • COURSE

      Using SAS® to Put Open Source Models into Production
      SHOW LESS ︿
      This course introduces the basics for integrating R programming and Python scripts into SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data exploration, model prototyping, and supervised and unsupervised learning techniques.

      ENROLL

    • COURSE

      Feature Engineering and Data Preparation for Analytics
      SHOW LESS ︿
      This course introduces programming techniques to craft and feature engineer meaningful inputs to improve predictive modeling performance. In addition, this course provides strategies to preemptively spot and avoid common pitfalls that compromise the integrity of the data being used to build a predictive model. This course relies heavily on SAS programming techniques to accomplish the desired objectives.

      ENROLL

    • COURSE

      Managing SAS® Analytical Models Using SAS® Model Manager Version 14.2
      SHOW LESS ︿
      This course focuses on the following key areas: managing SAS Model Manager data sources, creating a SAS Model Manager project, importing models into SAS Model Manager, using the SAS Model Manager Query Utility, creating scoring tasks, exporting models and projects into a SAS repository, and creating and configuring version life cycles. The course also covers generating SAS Model Manager model comparison reports, publishing and deploying SAS Model Manager models, creating SAS Model Manager production model monitoring reports, and creating user-defined reports.

      ENROLL

    • COURSE

      Experimentation in Data Science
      SHOW LESS ︿
      This course explores the essentials of experimentation in data science, why experiments are central to any data science efforts, and how to design efficient and effective experiments.
      The e-learning format of this course includes Virtual Lab time to practice.

      ENROLL

    • COURSE

      Advanced Predictive Modeling Using SAS® Enterprise Miner™
      SHOW LESS ︿
      This course covers advanced topics using SAS Enterprise Miner, including how to optimize the performance of predictive models beyond the basics. The course continues the development of predictive models that begins in the Applied Analytics Using SAS Enterprise Miner course, for example, by making use of the two-stage modeling node. In addition, some of the newest modeling nodes and latest variable selection methods are covered. Tips for working in an efficient way with SAS Enterprise Miner complete the course.

      The self-study e-learning includes:
      • Annotatable course notes in PDF format.
      • Virtual lab time to practice.


      ENROLL

    • COURSE

      Optimization Concepts for Data Science
      SHOW LESS ︿
      This course focuses on linear, nonlinear, and efficiency optimization concepts. You learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. The demonstrations in the course include examples of data envelopment analysis and portfolio optimization. The OPTMODEL procedure is used to solve optimization problems that reinforce concepts introduced in the course.

      The e-learning format of this course includes Virtual Lab time to practice.

      ENROLL

    • COURSE

      Survival Data Mining: A Programming Approach
      SHOW LESS ︿
      This advanced course discusses predictive hazard modeling for customer history data. Designed for data analysts, the course uses SAS/STAT software to illustrate various survival data mining methods and their practical implementation.

      Note: Formerly titled Survival Data Mining: Predictive Hazard Modeling for Customer History Data, this course now includes hands-on exercises so that you can practice the techniques that you learn. Other additions include a chapter on recurrent events, new features in SAS/STAT software, and an expanded section that compares discrete time approach versus the continuous time models such as Cox Proportional Hazards models and fully parametric models such as Weibull.

      ENROLL

    • COURSE

      Decision Tree Modeling
      SHOW LESS ︿
      This course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees. In addition, this course examines many of the auxiliary uses of trees such as exploratory data analysis, dimension reduction, and missing value imputation.

      ENROLL

    SAS Products Covered

    • SAS Enterprise Miner
    • SAS/STAT
    • SAS/IML
    • Base SAS
    • SAS Model Manager
    • SAS Text Miner
    • SAS Visual Statistics
    • SAS Visual Analytics
    • SAS Decision Manager
    • SAS/OR


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    “I think the self-paced training was the BEST I’ve ever taken. The videos were short and segmented correctly to keep my attention and the activities and quizzes were just enough to help my confidence.”

    Tony Mayo, SAS Customer