ADVANCED ANALYTICS

The Advanced Analytics Professional is comprised of nine (9) online courses and one hundred (100) hours of cloud software access which can be accessed by an enrolled user within twelve (12) months upon subscription.

One (1) exam voucher is also included for the user to redeem upon taking any of the certification exams below:

• SAS Certified Predictive Modeler Using SAS Enterprise Miner 14
• SAS Certified Specialist: Advanced Predictive Modeling
• SAS Certified Specialist: Text Analytics, Time Series, Experimentation and Optimization

PREREQUISITE SKILLS:

To enroll in the program, you need

• at least six months of programming experience in SAS or another programming language
• at least six months of experience using mathematics and/or statistics in a business environment.

If you're just getting started or need to brush up on your skills, we recommend:

• Statistics 1: Introduction to ANOVA, Regression and Logistic
• Regression – available as an instructor-led course or free online e-learning course.

For programming:

• SAS Programming 1: Essentials - available as an instructor-led course or free online e-learning course
• SAS Programming 2: Data Manipulation Techniques - available as an instructor-led course or online e-learning course

Or

• SAS Programming for R Users – available as a free online e-learning

ADVANCED ANALYTICS COURSES

1. Applied Analytics Using SAS® Enterprise Miner™

This course covers the skills required to assemble analysis flow diagrams using SAS Enterprise Miner for both pattern discovery (segmentation, association and sequence analyses) and predictive modeling (decision trees, regression and neural network models). Learn how to:

• Define a SAS Enterprise Miner project and explore data graphically.
• Modify data for better analysis results.
• Build and understand predictive models, including decision trees and regression models.
• Define a SAS Enterprise Miner project and explore data graphically.
• Modify data for better analysis results.
• Build and understand predictive models, including decision trees and regression models.

Completing Course #1 of this track helps prepare the student for the SAS® Certified Predictive Modeler Using SAS® Enterprise Miner 14 exam.

2. Neural Network Modeling

This course helps you understand and apply two popular artificial neural network algorithms – multilayer perceptrons and radial basis functions. Both the theoretical and practical issues of fitting neural networks are covered. Learn how to:

• Construct multilayer perceptron and radial basis function neural networks.
• Construct custom neural networks using the NEURAL procedure.
• Choose an appropriate network architecture and determining the relevant training method.
• Avoid overfitting neural networks.
• Perform autoregressive time series analysis using neural networks.
• Interpret neural network models.

3. Predictive Modeling Using Logistic Regression

This course explores predictive modeling using SAS/STAT® software, with an emphasis on the LOGISTIC procedure. Learn how to:

• Use logistic regression to model an individual's behavior as a function of known inputs.
• Select variables and interactions.
• Create effect plots and odds ratio plots using ODS Statistical Graphics.
• Handle missing data values.
• Tackle multicollinearity in your predictors.
• Assess model performance and compare models.
• Recode categorical variables based on the smooth weight of evidence.
• Use efficiency techniques for massive data sets.

4. Data Mining Techniques: Predictive Analysis on Big Data

This course introduces applications and techniques for assaying and modeling large data. It presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models and mixture distribution models. You will perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics and SAS In-Memory Statistics. Learn how to:

• Use applications designed for big data analyses.
• Explore data efficiently.
• Reduce data dimensionality.
• Build predictive models using decision trees, regressions, generalized linear models, random forests and support vector machines.
• Build models that handle multiple targets.
• Assess model performance.
• Implement models and score new predictions.

5. Using SAS® to Put Open Source Models into Production

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. Learn how to:

• Call R packages in SAS.
• Use Python scripts in SAS.
• Integrate open source data exploration techniques in SAS.
• Integrate open source models in SAS Enterprise Miner.
• Create production (score) code for R models.

Completing Courses #2-5 of this track helps prepare the student for the SAS® Certified Specialist: Advanced Predictive Modeling exam.

6. Text Analytics Using SAS® Text Miner

This course introduces applications and techniques for assaying and modeling large data. It presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models and mixture distribution models. You will perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics and SAS In-Memory Statistics. Learn how to:

• Use applications designed for big data analyses.
• Explore data efficiently.
• Reduce data dimensionality.
• Build predictive models using decision trees, regressions, generalized linear models, random forests and support vector machines.
• Build models that handle multiple targets.
• Assess model performance.
• Implement models and score new predictions.

7. Time Series Modeling Essentials

In this course, you'll learn the fundamentals of modeling time series data, with a focus on the applied use of the three main model types for analyzing univariate time series: exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unobserved components (UCM). Learn how to:

• Create time series data.
• Accommodate trend, as well as seasonal and event-related variation, in time series models.
• Diagnose, fit and interpret exponential smoothing, ARIMAX and UCM models.
• Identify relative strengths and weaknesses of the three model types.

8. Experimentation in Data Science

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. Learn how to:

• Define common terminology in designed experiments.
• Describe the benefits of multifactor experiments.
• Differentiate between the impact of a model and the impact of the action taken from that model.
• Fit incremental response models to evaluate the unique contribution of a marketing message, action, intervention or process change on outcomes.

9. Optimization Concepts for Data Science

This course focuses on linear, nonlinear and efficiency optimization concepts. Participants will learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. Course demonstrations 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. Learn how to:

• Identify and formulate appropriate approaches to solving various linear and nonlinear optimization problems.
• Create optimization models commonly used in industry.
• Formulate and solve a data envelopment analysis.
• Solve optimization problems using the OPTMODEL procedure in SAS.

Completing Courses #6-9 of this track helps prepare the student for the SAS® Certified Specialist: Text Analytics, Time Series, Experimentation and Optimization exam.

AI & MACHINE LEARNING

Learn how to analytically approach business problems and understand each step of the analytical life cycle with this in-depth credential.

AI & Machine Learning Professional, comprised of five (5) online courses and seventy (70) hours of cloud software access which can be accessed by an enrolled user within twelve (12) months upon subscription. One (1) exam voucher is also included for the user to redeem upon taking any of the certification exams below:

• SAS Certified Specialist: Machine Learning
• SAS Certified Specialist: Forecasting and Optimization
• SAS Certified Specialist: Natural Language Processing and Computer Vision

PREREQUISITE SKILLS:

• It is recommended that you have prior programming experience using SAS, Python or R. You can gain programming knowledge in the SAS Programming 1: Essentials course.
• Candidates should also have some experience with the visual interfaces in SAS Viya. You can learn about those interfaces in SAS Visual Statistics in SAS Viya: Interactive Model Building or SAS Visual Analytics 1 for SAS Viya: Basics.
• It is helpful to have a conceptual understanding of regression models, which you can gain by completing Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression.
• It is also helpful to have a conceptual understanding of neural network models, which you can gain by completing Neural Networks: Essentials or Neural Network Modeling.

AI & MACHINE LEARNING COURSES

1. Machine Learning Using SAS® Viya®

This course, which is at the core of the SAS Viya Data Mining and Machine Learning curriculum, teaches you the theoretical foundation for techniques associated with supervised machine learning models. Learn how to analytically approach business problems – and use a business case study to understand each step of the analytical life cycle. This course uses Model Studio, the pipeline flow interface in SAS Viya that helps you prepare, develop, compare and deploy advanced analytics models. Learn how to:

• Apply the analytical life cycle to a business need.
• Prepare and explore data for analytical model development.
• Create and select features for predictive modeling.
• Develop supervised learning models based on different techniques, from decision tree to support vector machines.
• Evaluate and select the best model based on business needs.
• Deploy and manage analytical models under production.

Completing Course #1 of this track helps prepare the student for the SAS® Certified Specialist: Machine Learning Using SAS® Viya® 3.5 exam.

2. SAS® Visual Text Analytics in SAS® Viya®

This course, which enables you to use SAS Viya in a distributed computing environment, explores the five components of visual text analytics: parsing, concept derivation, topic derivation, text categorization and sentiment analysis. You’ll parse and analyze documents to find dominant themes and construct linguistic queries to satisfy specific information needs. You’ll also develop an integrated solution using information extracted from subject matter expert rules and machine learning results for model and rule-based topics and categories. Learn how to:

• Use the point-and-click interface of Model Studio and SAS Visual Text Analytics.
• Interpret term maps and automatically identify key textual topics in your large document collections.
• Create robust models for categorizing the content according to specific needs.
• Create, modify and enable (or disable) custom concepts and test linguistic rule definitions with validation checks within the same interactive GUI.
• Extract individual instances of concepts from within documents.
• Create custom Boolean rules to categorize documents with respect to a categorical target variable and automatically modify those rules.

3. Deep Learning Using SAS® Software

Discover the essential components of deep learning, as well as how to build deep feedforward, convolutional and recurrent networks. Use neural networks to solve problems that include traditional classification, image classification and time-dependent outcomes. Explore practical methods used to enhance training data to produce better models, as well as a method for efficiently searching hyperparameters. Learn how to:

• Define and understand deep learning and build models using deep learning techniques.
• Apply models to score (inference) new data.
• Modify data for better analysis results.
• Search the hyperparameter space of a deep learning model.

Completing Courses #2-3 of this track helps prepare the student for the SAS® Certified Specialist: Forecasting and Optimization Using SAS® Viya® 3.5 exam.

4. Forecasting Using Model Studio in SAS® Viya®

Get a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya. Learn how to load data into memory and visualize the time series data to be modeled while introducing and implementing attribute variables. Use pipelines to generate forecasts and select champion pipelines – and discover how to incorporate large-scale forecasting practices. These include the creation of data hierarchies, forecast reconciliation, overrides and forecast model selection best practices. Learn how to:

• Automatically create and fit custom forecast models using structured analytic workflows or pipelines.
• Visualize modeling data using attribute variables.
• Refine forecast models to improve forecast accuracy.
• Apply overrides-generated forecasts.
• Generate forecast data sets for deployment.

5. Optimization Concepts for Data Science and Artificial Intelligence

This course focuses on linear, nonlinear and mixed-integer linear optimization concepts in SAS Viya. Discover how to formulate optimization problems and make formulations efficient by using index sets and arrays. The demonstrations in the course include examples of diet formulation and portfolio optimization. The OPTMODEL procedure is used to solve optimization problems that reinforce the concepts you've learned. Learn how to:

• Identify and formulate appropriate approaches to solving various linear, mixed-integer linear and nonlinear optimization problems.
• Create optimization models commonly used in the industry.
• Solve optimization problems using the OPTMODEL procedure in SAS.

Completing Courses #4-5 of this track helps prepare the student for the SAS® Certified Specialist: Natural Language Processing and Computer Vision exam.