SAS® Academy for Data Science
Demonstrate your ability to use the tools and technology designed to handle big data. The SAS Certified Big Data Professional program delivers the extra edge you're looking for.
SAS® Big Data Certification Curriculum
Course content is designed to prepare you for the certification exams.
Real-world case studies enable you to apply what you have learned.
Pass both exams to earn your certification credential.
- Critical SAS programming skills.
- Accessing, transforming and manipulating data.
- Improving data quality for reporting and analytics.
- Essential communication skills.
- Fundamentals of statistics and analytics.
- Working with Hadoop, Hive, Pig and SAS.
- Exploring and visualizing data.
SAS software covered
- Base SAS®
- SAS® Enterprise Guide®
- SAS® Enterprise Miner™
- SAS® In-Memory Statistics
- SAS® Studio
- SAS® Visual Analytics
- DataFlux® Data Management Server
- DataFlux® Data Management Studio
To enroll in the program, you need at least six months of programming experience in SAS or another programming language. If you need to brush up on your programming skills, the SAS Programming for Data Science Fast Track will give you a good foundation.
What You Will Learn
Module 1: Big Data Preparation, Statistics and Visual Exploration
Course 1: Big Data Challenges and Analysis-Driven Data
This course provides an overview of the challenges associated with big data and analysis-driven data.
- Reading external data files.
- Storing and processing data.
- Combining Hadoop and SAS.
- Recognizing and overcoming big data challenges.
Course 2: Exploring Data With SAS Visual Analytics
In this course, you'll learn how to use SAS Visual Analytics Explorer to explore in-memory tables from the SAS® LASR™ Analytic Server and perform advanced data analyses.
- Finding previously unknown relationships and spotting trends in your data.
- Visualizing data using charts, plots and tables.
- Using the autocharting function to visualize data in the best possible way.
- Using advanced graphs, such as network diagrams, Sankey 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.
Course 3: Statistics 1: Introduction to ANOVA, Regression and Logistic Regression
This introductory SAS/STAT® course focuses on t-tests, ANOVA and linear regression, and includes a brief introduction to logistic regression.
- 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 models.
Course 4: Preparing Data for Analysis and Reporting
In this course, you'll learn how to perform data management tasks, such as improving data quality, entity resolution and data monitoring.
- Creating and reviewing data explorations.
- Creating and reviewing data profiles.
- Creating data jobs for data improvement.
- Establishing monitoring aspects for your data.
- Understanding the QKB components.
- Using the component editors.
- Understanding various definition types.
- Building a new data type (optional).
Course 5: Crafting Compelling (and true) Data Stories
Storytelling is a necessary skill when talking to key stakeholders. Insights uncovered in your data can move mountains if the right people say yes. But how do you move someone from simply being curious, all the way to, "Let's do this!" In this course, you'll learn why storytelling is a skill you need to develop, when a story works and when it doesn't, and how to communicate data in a meaningful way.
Module 1 prepares you for the SAS Big Data Preparation, Statistics and Visual Exploration certification exam.
Module 2: Big Data Programming and Loading
Course 1: Introduction to SAS and Hadoop: Essentials
This course teaches you how to use SAS programming methods to read, write and manipulate Hadoop data. You'll learn how to use Base SAS methods to read and write raw data with the DATA step, manage the Hadoop Distributed File System (HDFS) and execute MapReduce and Pig code from SAS via the HADOOP procedure. You'll also learn how to use SAS/ACCESS® Interface to Hadoop methods that allow LIBNAME access and SQL pass-through techniques to read and write Hive or Impala table structures.
- Accessing Hadoop distributions using the LIBNAME statement and the SQL pass-through facility.
- Creating and using SQL procedure pass-through queries.
- 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.
Course 2: DS2 Programming Essentials With Hadoop
This course focuses on DS2, a fourth-generation SAS proprietary language for advanced data manipulation, which enables parallel processing and storage of large data with reusable methods and packages.
- 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.
- Using 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.
Course 3: Hadoop Data Management With Hive, Pig and SAS
In this course, you will use processing methods to prepare structured and unstructured big data for analysis. You will learn to organize the data into structured tabular form using Apache Hive and Apache Pig. You will also learn SAS software technology and techniques that integrate with Hive and Pig, as well as how to use these open source capabilities by programming with Base SAS and SAS/ACCESS Interface to Hadoop, and with SAS Data Integration Studio.
- Moving data into the Hadoop ecosystem.
- Using Hive to design a data warehouse in Hadoop, perform data analysis using the Hive query language (HiveQL) and join data sources.
- Performing extract, transform and load (ETL).
- Organizing data in Hadoop by usage.
- Analyzing unstructured data using Pig.
- Joining massive data sets using Pig.
- Using user-defined functions (UDFs).
- Analyzing big data in Hadoop using Hive and Pig.
- Using SAS programming to submit Hive and Pig programs that execute in Hadoop, and store results in Hadoop or return results to SAS.
- Using SAS programming to move data between the SAS server and the HDFS.
- Constructing SAS Data Integration Studio jobs that integrate with Hive and Pig processes and the HDFS.
Course 4: Getting Started With SAS In-Memory Statistics
This course focuses on accessing data on the SAS LASR Analytic Server and performing exploratory analysis and preparation. Topics include starting the server, loading data and manipulating data on the SAS LASR Analytic Server using the IMSTAT procedure. IMSTAT topics include deriving new temporary and permanent tables and columns, calculating summary statistics (e.g., mean, frequency and percentile), and creating filters and joins on in-memory data.
- Starting up a SAS LASR Analytic Server.
- Loading tables into memory on the SAS LASR Analytic Server.
- Processing in-memory tables with PROC LASR and PROC IMSTAT.
- Accessing data more efficiently via intelligent partitioning.
- Deriving new temporary and permanent tables and variables.
- 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.
Module 2 prepares you for the SAS Big Data Programming and Loading certification exam.
The training has been very valuable for me to get a broad understanding and knowledge about the different tools and ways to extract value from data.
SAS Academy for Data Science Graduate
Credentials to advance your career.
SAS Academy for Data Science offers a comprehensive learning foundation that you can build your analytics career on.
- SAS® Certified Big Data Professional
Learn to manage big data with a focus on data quality and visual analytics.
- SAS® Certified Advanced Analytics Professional
Learn analytical modeling, machine learning, experimentation, and more.
- SAS® Certified Data Scientist
Learn both - how to manage big data and perform analytics.
Not sure about this program? Let us help.
Here are some of our most frequently asked questions.
Is this program right for me?
This program is ideal for those who want to build on their basic programming knowledge by learning how to gather and analyze big data in SAS.
What exams do I pass to receive the credential?
Already have these skills? You can take the exams without completing the coursework. Get details — including test dates, locations and fees — using the links above.
What’s included with the self-paced version?
- SAS Certified Big Data Professional: 150 e-Learning hours + 100 Virtual Lab hours
- Big Data Preparation, Statistics and Visual Exploration: 76 e-Learning hours + 50 Virtual Lab hours
- Big Data Programming and Loading: 74 e-Learning hours + 50 Virtual Lab hours
Certification exams must be purchased separately with Self-Paced version.
License duration: 180 days
Are practice exams available?
Yes! Students in the self-paced offering can purchase practice exams from Pearson Vue. Practice exams are included in the classroom offering.
How long will it take to complete the program?
If you dedicate 8-10 hours per week, you can complete the program in 6 months.
What is required to participate in the self-paced program?
View the e-Learning system requirements for more information.