SAS Certified Data Scientist

The SAS Certified Data Scientist program can deepen your knowledge, jump-start your career and boost your earning power. The data science certification program includes five certification exams. To earn the SAS Certified Data Scientist credential, you must pass all five exams:
  • SAS Big Data Preparation, Statistics and Visual Exploration
  • SAS Big Data Programming and Loading
  • SAS Certified Specialist: Machine Learning Using SAS Viya 3.4
  • Natural Language & Computer Vision Specialist
  • SAS Certified Specialist: Forecasting and Optimization Using SAS Viya 3.4

Big Data Challenges and Analysis-Driven Data

Topics Covered
  • Reading external data files
  • Storing and processing data
  • Combining Hadoop and SAS
  • Recognizing and overcoming big data challenges

Exploring Data with SAS Visual Analytics

Topics Covered
  • Finding previously unknown relationships and spotting trends in your data.
  • Visualizing data using charts, plots and tables.
  • Using the auto charting function to visualize data in the best possible way
  • Using advanced graphs, such as network diagrams, San key diagrams and word clouds
  • Easily adding analytics to your graphs, and including descriptions of the analytics results
  • Navigating through your data using on-the-fly hierarchies

Preparing Data for Analysis and Reporting

Topics Covered
  • Creating and reviewing data explorations and data profiles
  • Creating data jobs for data improvement.
  • Establishing monitoring aspects for the data
  • Understanding the QKB components
  • Using the component editors.
  • Understanding various definition types

Introduction to ANOVA, Regression and Logistic Regression

Topics Covered
  • Generating descriptive statistics and exploring data with graphs
  • Performing analysis of variance and applying multiple comparison techniques
  • Performing linear regression and assessing the assumptions
  • Using regression model selection techniques to aid in the choice of predictor variables in multiple regression
  • Using diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression
  • Using chi-square statistics to detect associations among categorical variables
  • Fitting a multiple logistic regression model
  • Scoring new data using developed model

Introduction to SAS and Hadoop Essentials

Topics Covered
  • Accessing Hadoop distributions using the LIBNAME statement and the SQL pass-through facility
  • Using options and efficiency techniques for optimizing data access performance
  • Joining data using the SQL procedure and the DATA step
  • Reading and writing Hadoop files with the FILENAME statement
  • Executing and using Hadoop commands with PROC HADOOP
  • Using Base SAS procedures with Hadoop.

DS2 Programming Essentials with Hadoop

Topics Covered
  • Identifying the similarities and differences between the SAS DATA step and the DS2 DATA step.
  • Converting a Base SAS DATA step to DS2.
  • Creating DS2 variable declarations, expressions and methods for data conversion, manipulation and conditional processing.
  • Creating user-defined and predefined packages to store, share and execute DS2 methods.
  • Creating and executing DS2 threads for parallel processing.
  • Leveraging the SAS In-Database Code Accelerator to execute DS2 code outside of a SAS session.
  • Executing DS2 code in the SAS High-Performance Analytics grid using the HPDS2 procedure.

Big Data Analysis with Hive and Pig

Topics Covered
  • Using Hive to design a data warehouse in Hadoop
  • Performing data analysis using HiveQL
  • Organizing data in Hadoop by usage
  • Performing analysis on unstructured data using Pig
  • Joining massive data sets using Pig
  • Using user-defined functions (UDFs)
  • Analyzing big data in Hadoop using Hive and Pig

Getting Started with SAS In-Memory Statistics

Topics Covered
  • Processing in-memory tables with PROC LASR and PROC IMSTAT
  • Accessing data more efficiently via intelligent partitioning
  • Creating filters and joins on in-memory data
  • Exporting ODS result tables for client-side graphic development
  • Producing descriptive statistics including counts, percentiles and means
  • Creating multidimensional summaries including cross-tabulations and contingency tables
  • Deriving kernel density estimates using normal functions

SAS Certified AI & Machine Learning Professional

This program is divided in three modules. I. Programming for SAS® Viya® : This module leverages the power of SAS Cloud Analytic Services (CAS) to access, manage, and manipulate in-memory tables. This course is not intended for beginning SAS software users. II. Self -Services Data Preparation in SAS® Viya® : These self-service data preparation capabilities include bringing data in from a variety of sources, preparing and cleansing the data to be fit for purpose, analyzing data for better understanding and governance, and sharing the data with others to promote collaboration and operational use. III. Neural Networks Essentials : This program combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application.

Module 1 - Machine Learning Using SAS Viya 3.4

Topics Covered
  • Prepare and explore data for analytical model development
  • Create and select features for predictive modeling
  • Develop a series of supervised learning models based on different techniques such as decision tree, ensemble of trees (forest and gradient boosting), neural networks, and support vector machines.
  • Evaluate and select the best model based on business needs
  • Deploy and manage analytical models under production.

Module 2 - Natural Language Processing & Computer Vision Using SAS Viya 3.4

There are TWO programs in this module.

2A. SAS Visual Text Analytics in SAS Viya This program explores the five components of Visual Text Analytics: parsing, concept derivation, topic derivation, text categorization, and sentiment analysis. Sophisticated linguistic queries are constructed to satisfy specific information needs.
Topics Covered
  • Interpret term maps
  • Identify key textual topics automatically in your large document collections
  • Create, modify, and enable (or disable) custom concepts and test linguistic rule definitions with validation checks within the same interactive GUI
  • Create custom Boolean rules to categorize documents with respect to a categorical target variable
  • Modify automatically generated Boolean category rules
  • Extract a document-level sentiment score.
2B. Deep Learning Using SAS Software
This program introduces the essential components of deep learning. Participants learn how to build deep feedforward, convolutional, and recurrent networks. The neural networks are used to solve problems that include traditional classification, image classification, and time-dependent outcomes.
Topics Covered
  • Define and understand deep learning
  • 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
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