There is a new version of this course. Please see SAS Certified Data Scientist.

This is the SAS Certified Data Science Program in the SAS Academy for Data Science. In Level 1, SAS Certified Big Data Professional, students learn critical SAS programming skills and introductory statistics, and they also learn how to manage and manipulate data, explore data visually, and craft compelling stories with data. In addition, students learn how SAS works with Hadoop, and they learn about Hadoop data management with Hive, Pig, and SAS. Level 1 also teaches programming essentials with Hadoop and provides an introduction to in-memory statistics.

In Level 2, The SAS Certified Advanced Analytics Professional, students learn a variety of machine learning and predictive modeling techniques as well as how to apply the techniques to big distributed and in-memory data sets. Students also learn about experimentation in business, optimization, and time series forecasting. The coursework, hands-on practice, and projects help prepare students for the certification exam needed to become a SAS Certified Data Science Professional.

Learn how to

• recognize and overcome big data challenges
• prepare data for analysis and reporting
• apply fundamental statistical techniques
• craft compelling stories with data
• explore and visualize your data
• work with SAS and Hadoop
• manage Hadoop data with Hive, Pig, and SAS
• program with Hadoop
• use SAS In-Memory Statistics
• apply machine learning and predictive modeling techniques to big distributed and in-memory data sets
• detect patterns in your data
• set up and run business experiments
• optimize your data
• use time-series forecasting techniques
• effectively communicate results and recommendations to executive-level management.

Who should attend

Business analysts, programmers, or statisticians with at least six months of experience writing SAS programs or another programming language and at least six months of experience using mathematics, statistics, or both in a business environment

A master's degree or higher in a quantitative or technical field is recommended but not required.