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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Unlock digital courses
Unlimited access to digital courses and books in this subscription.
Prepare for certification
Unlimited access to certification preparation materials.
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Earn a digital badge for each completed course.
Hands-on learning and practice
Get Full access to SAS software to practice as you learn.

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 in Subscription

    SAS Predictive Modeler Certification

    1
    • COURSE

      Applied Analytics Using SAS® Enterprise Miner™
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      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.

    • CERTIFICATION PREP

      Practice Exam: Predictive Modeling Using SAS Enterprise Miner 14
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    • CERTIFICATION PREP

      SAS Certified Predictive Modeler Using SAS Enterprise Miner 14
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      A00-255: Predictive Modeling using SAS Enterprise Miner 14 will retire on June 30, 2025. All certifications earned before this date will not expire and will remain valid after the exam is retired.

      Preparation for the Predictive Modeling Using SAS Enterprise Miner 14 certification exam includes learning how to build predictive models, assess and implement models, and perform pattern analysis using SAS Enterprise Miner 14.


    Advanced and Specialty Topics

    2
    • COURSE

      Predictive Modeling Using Logistic Regression
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      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.

    • COURSE

      Using SAS® to Put Open Source Models into Production
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      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.

    • COURSE

      SAS® Programming for R Users
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      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.

    • COURSE

      SAS® Enterprise Miner™ Integration with Open Source Languages
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      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.

    • COURSE

      Feature Engineering and Data Preparation for Analytics
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      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.

    • COURSE

      Managing SAS® Analytical Models Using SAS® Model Manager Version 14.2
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      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.

    • COURSE

      Experimentation in Data Science
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      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.


    • COURSE

      Strategies and Concepts for Data Scientists and Business Analysts
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      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.

    • COURSE

      Profit-Driven Business Analytics
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      This course provides actionable guidance on optimizing the use of data to add value and drive better business decisions. Combining theoretical and technical insights into daily operations and long-term strategy, this course acts as a development manual for practitioners who seek to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the instructor team draws upon their recent research to share deep insights about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this course provides invaluable guidance for practitioners seeking to reap the advantages of true profit-driven business analytics.

    • COURSE

      Advanced Predictive Modeling Using SAS® Enterprise Miner™
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      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(R) Enterprise Miner(TM) 5.2 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.


    • COURSE

      Advanced Analytics in a Big Data World
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      In today's big data world, many companies have gathered huge amounts of customer data about marketing success, use of financial services, online usage, and even fraud behavior. Given recent trends and needs such as mass customization, personalization, Web 2.0, one-to-one marketing, risk management, and fraud detection, it becomes increasingly important to extract, understand, and exploit analytical patterns of customer behavior and strategic intelligence. This course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data science techniques for advanced customer intelligence applications. This highly interactive course provides a sound mix of both theoretical and technical insights as well as practical implementation details and is illustrated by several real-life cases. References to background material such as selected papers, tutorials, and guidelines are also provided.

    • COURSE

      Optimization Concepts for Data Science
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      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.


    • COURSE

      Survival Data Mining: A Programming Approach
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    • COURSE

      Decision Tree Modeling
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      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.

    SAS Products Covered

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


    Digital Badges



    Earn a digital badge for each course that you complete and for each credential that you earn. Show off your achievements on your resume and in your social channels to highlight your skills and connect with potential employers.

    Certification Preparation

    When you complete the courses in this subscription, you will have the demonstrated skills necessary to prepare you to earn the SAS Certified Specialist: Advanced Predictive Modeling credential.

    “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