Training
Expand your analytical skill set by learning analytical modeling, machine learning, experimentation, forecasting and optimization.
Here's what you'll get:
9 training courses
3 certification exams
+ vouchers
7 digital badges
Included Courses
- Predictive Modeling
- Advanced Predictive Modeling
- Text Analytics, Time Series, Experimentation and Optimization
Applied Analytics Using SAS® Enterprise Miner™
COURSE 1
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.
- Compare and explain complex models.
- Generate and use score code.
- Apply association and sequence discovery to transaction data.
Neural Network Modeling
COURSE 1
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.
Predictive Modeling Using Logistic Regression
COURSE 2
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.
Data Mining Techniques: Predictive Analysis on Big Data
COURSE 3
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.
Using SAS® to Put Open Source Models Into Production
COURSE 4
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.
Text Analytics Using SAS® Text Miner
COURSE 1
In this course, you will learn to use SAS Text Miner to uncover underlying themes or concepts contained in large document collections, automatically group documents into topical clusters, classify documents into predefined categories, and integrate text data with structured data to enrich predictive modeling endeavors.
Learn how to:
- Convert documents stored in standard formats (Microsoft Word, Adobe PDF, etc.) into general-purpose HTML or TXT formats.
- Read documents from a variety of sources (web pages, flat files, data elements in a relational database, spreadsheet cells, etc.) into SAS tables.
- Process textual data for text mining (e.g., correcting misspellings or recoding acronyms and abbreviations).
- Convert unstructured text-based character data into structured numeric data.
- Explore words and phrases in a document collection.
- Query document collections using keywords (i.e., identifying documents that include specific words or phrases).
- Identify topics or concepts that appear in a document collection.
- Create user-influenced topic tables from scratch or by modifying machine-generated topics, or creating concepts using domain knowledge.
- Use derived topic tables or preexisting user-influenced topic tables (or both) to enhance information retrieval and document classification.
- Cluster documents into homogeneous subgroups.
- Classify documents into predefined categories.
Time Series Modeling Essentials
COURSE 2
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.
Experimentation in Data Science
COURSE 3
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.
Optimization Concepts for Data Science
COURSE 4
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.
Program Features
Online 24/7
E-learning courses accessible whenever – and wherever – you're ready to learn.
Hands-On Learning
Get full access to SAS software to practice what you've learned on real technology.
Globally Recognized Credentials
Earn a certification with tailored training that helps prepare you for each exam.
Pace Yourself
As long as you complete the nine courses within a 12-month time frame, you can go at your own pace.
Real-World Case Studies
Enhance your learning with practical application based on actual case studies.
SAS Expertise
Ask questions, find guidance and stay on track with help from SAS users and experts.
Prerequisite Skills
To enroll in the program, you need at least six months of programming experience in SAS or another programming language. We also recommend that you have 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
Discounts
- SAS Training Points: SAS Training Points can be purchased and used to pay for Academy offerings.
- Academic discount: Educators, students (part- and full-time) and staff directly employed by academic institution or school district are eligible for a 50% discount off the price of the Academy. Proof of affiliation is required (e.g., valid identification card, a letter from HR on institution letterhead or a copy of your current semester transcript). Promo code: A50.
- Partner discount: SAS partners are eligible for a 50% discount off the price of the Academy. Promo code: APD.
Guidelines
- Effective Date: March 26, 2020.
- Enrollment in the SAS Academy for Data Science is for one individual and is nontransferable.
- SAS will send an Academy program welcome notification upon receipt of your registration. The welcome notification will include your unique activation code to access SAS e-learning content and software for practice.
- If you purchased an annual license, you will have 12 months from the date of purchase to complete all Academy e-learning.
- E-learning content is accessible via the SAS Virtual Learning Environment. You will be asked to accept the terms of the e-learning license when you first sign in to your SAS profile.
- E-learning Academy programs include access to SAS software for a set number of hours so you can practice applying the concepts you learn. If you have not accessed your virtual lab software for 30 days, your software image will be discontinued and your work will be deleted.
- As a student in the Academy program, you are eligible for a 50% discount on instructor-led training for the duration of your e-learning license. View your confirmation email for instructions on how to register. This discount cannot be combined with any other discount or offer and cannot be applied to Six Sigma courses, SAS conferences, on-site courses, SAS certification exams, special events, discount bundles or SAS Training Points.
- Failure to comply with all guidelines may result in cancellation of your Academy program.
- Academy program invoices will include applicable sales tax.
Buy Now
Advanced Analytics
Professional
- 9 online courses.
- 12 months of unlimited access.
- 100 hours of cloud software access.
- 3 certification exam vouchers.
€1,295 / year
Explore More of the Academy
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education@por.sas.com | Call +351 21031 6000