SAS Predictive Analytics and Machine 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 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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    SAS Advanced Predictive Modeling Certification

    2
    • COURSE

      Neural Network Modeling
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      This course helps you understand and apply two popular artificial neural network algorithms: multi-layer perceptrons and radial basis functions. Both the theoretical and practical issues of fitting neural networks are covered. Specifically, this course teaches you how to choose an appropriate neural network architecture, how to determine the relevant training method, how to implement neural network models in a distributed computing environment, and how to construct custom neural networks using the NEURAL procedure.

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

    • 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

      Data Mining Techniques: Predictive Analytics on Big Data
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      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.

    • 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.

    • CERTIFICATION PREP

      Practice Exam: SAS Advanced Predictive Modeling
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      This exam is designed for analysts who are creating advanced predictive models using big data. Successful candidates should have experience in the following areas:

      • Deploying open-source models in SAS.
      • Using machine learning and predictive modeling techniques.
      • Applying machine learning and predictive modeling techniques to big, distributed and in-memory data sets.



    SAS9

    3
    • 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

      Survival Data Mining: A Programming Approach
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      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.

    • 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

      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.

      The self-study e-learning includes:

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


    • 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.

    • COURSE

      Design of Experiments for Direct Marketing
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      This course deals with the concepts and techniques that are used in the design and analysis of experiments. The course primarily focuses on direct marketing applications, but it is also relevant for someone interested in designing experiments in the fields of physical, chemical, biological, medical, economic, social, psychological, and industrial sciences; engineering; or agriculture. This course teaches you how to design efficient marketing experiments with more than one factor, analyze the results that your experiments yield, and maximize the information that is gleaned from a marketing campaign. Factorial and fractional factorial designs are discussed in greater detail.

    • 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.

      The self-study e-learning includes:

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


    • 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

      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

      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

      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

      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.

      The self-study e-learning includes:

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


    SAS Machine Learning Specialist Certification

    4
    • COURSE

      Machine Learning Using SAS® Viya®
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      This course discusses the theoretical foundation for techniques associated with supervised machine learning models. A series of demonstrations and practices is used to reinforce all the concepts and the analytical approach to solving business problems. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, by illustrating data exploration, data preprocessing, feature selection, model training and validation, model assessment, and scoring. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data.

    • CERTIFICATION PREP

      Practice Exam: SAS Viya Supervised Machine Learning Pipelines
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      This Certification is for data scientists who create supervised machine learning models using pipelines in SAS Viya. Successful candidates should be familiar with SAS Visual Data Mining and Machine Learning software and be skilled in tasks such as:

      • Preparing data and feature engineering
      • Creating supervised machine learning models
      • Assessing model performance
      • Deploying models into production



    VIYA

    5
    • COURSE

      Supervised Machine Learning Procedures Using SAS® Viya® in SAS® Studio
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      This course covers a variety of machine learning techniques that are performed in a scalable and in-memory execution environment. The course provides hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The machine learning techniques include logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, factorization machine, and Bayesian networks.

      The self-study e-learning includes:

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


    • COURSE

      Neural Networks: Essentials
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      This course combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application. During the course, you programmatically build a neural network and discover how to adjust the model’s essential parameters to solve different types of business challenges. You implement early stopping, build autoencoders for a predictive model, and perform an intelligent automatic search of the model hyperparameter values. The last lesson introduces deep learning. You gain hands-on practice building neural networks in SAS 9.4 and the cutting-edge, cloud-enabled in-memory analytics engine for big data analytics, SAS Viya.

      The self-study e-learning includes:

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


    • COURSE

      Interactive Machine Learning in SAS® Viya®
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      This course provides a theoretical foundation for using machine learning capabilities in SAS Viya, as well as hands-on experience using the tool through the SAS Visual Analytics interface. The course uses an interactive approach to teach you visualization, model assessment, and model deployment while introducing you to a variety of machine learning techniques.

      The SAS Viya 3.5 e-learning version of this course uses the title SAS Visual Data Mining and Machine Learning in SAS Viya: Interactive Machine Learning.



    • COURSE

      Using SAS® Viya® REST APIs with Python and R
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      In this course, you learn to use the R and Python APIs to take control of SAS Cloud Analytic Services (CAS) and submit actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models on CAS using familiar open-source functionality via the SWAT (SAS Wrapper for Analytics Transfer) package.

    • COURSE

      Managing Models in SAS® Viya®
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      This applied, hands-on course teaches you how to manage models through their useful life cycle. You start by creating a modeling project, and then you add and compare models to it so that you can identify a champion model. The course uses models that are created using SAS Advanced Analytics capabilities and Python and R languages. The course also shows how to implement procedures that ensure that model governance and oversight approval is being followed by implementing workflow.

      You learn how to test a model in the production environment to which it will be deployed. After the model test runs successfully, you learn how to schedule the model to run automatically.

      Further, the course shows how to measure and monitor the ongoing performance of model accuracy over time. The performance monitoring process will also be scheduled to run automatically in class.

      An optional lesson shows how to register and score four types of SAS Visual Text Analytics models.

    • COURSE

      SAS Analytics: Getting Started
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      Get started on your journey to using SAS for advanced analytics by spending a bit of time each day with a new aspect of SAS Analytics in SAS Viya. Over the course of three weeks, you'll get a good idea of the skills you may need to develop and the tasks that will be needed to make the most of SAS Viya for predictive modeling, time series forecasting, optimization, and more advanced AI algorithms. At the end of your journey be sure to explore formal enablement opportunities you can take advantage of to continue your quest in becoming an expert in advanced analytics.

    • COURSE

      Responsible Innovation and Trustworthy AI
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      This course is designed for anyone who wants to gain a deeper understanding about the importance of trust and responsibility in AI, analytics, and innovation. The content is especially geared to those who are making business decisions based on machine learning and AI systems and those who are designing and training AI systems.

      Whether you are a programmer, an executive, an advisory board member, a tester, a manager, or an individual contributor, this course helps you gain foundational knowledge and skills to consider the issues related to responsible innovation and trustworthy AI. Empowered with the knowledge from this course, you can strive to find ways to design, develop, and use machine learning and AI systems more responsibly.

      This course will be released several modules at a time until all modules are available. We expect that each module can be completed in under an hour, and you can work at your own pace to complete the material. As we release new modules, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.

    • COURSE

      Leading with Analytics
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      You know that analytics can help your company succeed. However, it is not always clear where and how analytics can help. Even worse, it can sometimes seem like everyone is speaking a different language. This course helps you lead your organization to greater success by pairing your expertise about the business with an understanding of where and how data science can help. You build on your strengths to collaborate effectively with experienced data scientists and to mentor novice analytics professionals to engage in the business. You also learn about five organizational styles for analytics with proven business outcomes.

    • COURSE

      Generative AI Using SAS®
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      Generative Artificial Intelligence (GenAI) is a rapidly developing area of machine learning, with application across business, government, and academia. In this course, you will learn about different types of GenAI and see examples of how SAS can enhance your efforts to make the most of these techniques.

      This course will be released several lessons at a time until all lessons are available. We expect that each lesson can be completed in about an hour, and you can work at your own pace to complete the material. As we release new lessons, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.

    Machine Learning Leadership and Practice - End-to-End Mastery

    6
    • COURSE

      The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
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      This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning.

    • COURSE

      Launching Machine Learning: Delivering Operational Success with Gold Standard ML Leadership
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      This course will guide you to lead or participate in the end-to-end implementation of machine learning (aka predictive analytics). Unlike most machine learning courses, it prepares you to avoid the most common management mistake that derails machine learning projects: jumping straight into the number crunching before establishing and planning for a path to operational deployment.

      Whether you'll participate on the business or tech side of a machine learning project, this course delivers essential, pertinent know-how. You'll learn the business-level fundamentals needed to ensure the core technology works within – and successfully produces value for – business operations. If you're more a quant than a business leader, you’ll find this is a rare opportunity to ramp up on the business side, since technical ML trainings don’t usually go there. But know this: The soft skills are often the hard ones.

    • COURSE

      Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
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      This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants.

      And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers.

    SAS Products Covered

    • SAS Enterprise Miner
    • SAS/STAT
    • SAS/GRAPH
    • SAS In-Memory Statistics
    • SAS Visual Statistics
    • Base SAS
    • SAS/QC
    • SAS Decision Manager
    • SAS Model Manager
    • SAS/IML
    • SAS/INSIGHT
    • SAS Text Miner
    • SAS Visual Analytics
    • SAS Viya
    • SAS Visual Data Mining and Machine Learning
    • SAS Visual Data Science Decisioning
    • None
    • SAS Machine Learning


    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