The big data certification program is ideal for individuals ready to build on their basic programming knowledge by learning how to gather and analyze big data in SAS.
The big data certification program includes two certification exams. To earn the SAS Certified Big Data Professional credential, you must pass both exams:
- SAS Big Data Preparation, Statistics and Visual Exploration
- SAS Big Data Programming and Loading
Already have the skills covered in the big data certification program courses? If so, you may take the exams without completing the coursework.
The six-week big data certification program curriculum includes the following courses:
- Big Data Challenges and Analysis-Driven Data
- SAS Fundamentals: Programming, SQL and Macro Language
- Exploring Data With SAS Visual Analytics
- Statistics 1: Introduction to ANOVA, Regression and Logistic Regression
- Preparing Data for Analysis and Reporting
- Introduction to SAS and Hadoop: Essentials
- DS2 Programming Essentials With Hadoop
- Big Data Analysis With Hive and Pig
- Getting Started With SAS In-Memory Statistics
- Working With SAS Data Loader for Hadoop
Tution Fee - 600000 INR
Course Duration - 6 weeks
To enroll in the SAS Certified Big Data Professional program, you need at least six months of programming experience in SAS or another programming language. Need to brush up on your programming skills? We recommend that you first complete the SAS Programming 1: Essentials course, which is available as an instructor-led or free online e-learning course. You might also find the SAS Programming 2: Data Manipulation Techniques course helpful.
Thinking about getting your big data certification? It's a worthwhile step, as the ability to keep up with constantly changing technologies can make or break your career.
The SAS Certified Big Data Professional program – offered by the SAS Academy for Data Science – can give you the extra edge you're looking for.
The academy's big data certification program focuses on these areas:
- Critical SAS programming skills.
- Accessing, transforming and manipulating data.
- Improving data quality for reporting and analytics.
- Fundamentals of statistics and analytics.
- Working with Hadoop, Hive, Pig and SAS.
- Exploring and visualizing data.
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