Data Science using SAS
The Data Science using SAS class is a unique and focused class to better prepare you for high demanding Data Science Analyst level positions. This three unit and seven weeks class teaches you to understand the data to answer analysis questions as well as apply data cleaning techniques.
The core topics consist of SAS Enterprise Guide, Base and Advanced certification exam preparation as well as SQL for data manipulation and data cleaning. These are all essential topics across all big data industries. There are also advanced topics on data and visual analysis.
By the end of this class, you will know how to use SAS Enterprise Guide to write SAS programs, be prepared for the SAS base and advanced certification exams, use technical methods to prepare, clean, visualize and query data and apply data analysis. In addition, students will be asked to give presentations on topics such as Data Science, SAS and other industry-related topics.
- Working with SAS Enterprise Guide
- Applying Data Cleaning Techniques
- Using SQL for Data Creation and Query
- Reporting, Statistical and Visual Analysis
- Preparing for SAS Base Certification Exams
- Preparing for SAS Advanced Certification Exams
- Use SAS Enterprise Guide to write SAS programs
- SAS base and advanced certification exam preparation
- Use technical methods to prepare, clean, visualize and query data and apply data analysis.
Software: SAS Enterprise Guide for Academics - free for students to use in class.
Course typically offered: Online in the Spring and Fall.
Prerequisites: This class requires some computer programming experience in any language or knowing SAS programming since the class will be fast paced.
Next Steps: Upon completion, consider additional Data Science coursework for continued learning.
More Information: For more information about this course, please contact firstname.lastname@example.org.
Course Number: CSE-41320
Credit: 3.00 unit(s)
Related Certificate Programs: Business Intelligence Analysis, Data Mining for Advanced Analytics, Machine Learning Methods, Python Programming
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9/23/2019 - 11/22/2019