260777 Data Processing Using SAS
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Credit points: 3 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 260776c Foundation of Business Analytics
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 26777 Data Processing Using SAS
Description
In the fourth industrial revolution, data has become one of our most precious goods. It’s available and cheap and holds promise for enterprises that want to gain a competitive edge. However, raw data is also commonly incomplete, unstructured and inconsistent. The subject Data Processing with SAS looks at how this widely used tool can transform a raw dataset into valuable information for exploring business performance and for decision making. SAS is the market leader software for analytics, being used by more than 83,000 business, government and university sites around the world, including 92 of the top 100 companies on the 2018 Fortune Global 1000®. This subject provides the fundamentals required for the SAS certification Base Programming Specialist. Students learn about data validation and manipulation. Data validation is the process of converting raw data into quality data by addressing issues such as missing values. Data manipulation, using methods such as coding, makes data easier to read and analyse. Finally, students learn how to apply the appropriate statistical tools to extract valuable information from real datasets.
Subject learning objectives (SLOs)
| 1. | access and import different types of data within the SAS environment |
|---|---|
| 2. | explore, validate and manipulate data for analyses |
| 3. | run simple analyses, export the results and create reports |
Contribution to the development of graduate attributes
The subject introduces students to data processing, which is a fundamental step in Data Science. This subject gives students opportunities to develop their knowledge and proficiency in operating in the SAS environment. The learning activities enable students to develop and apply data analysis skills to real-world scenarios. Their business practice skills are further developed through learning activities related to the creation of reports.
This subject is aligned with the following Graduate Attribute(s):
- Intellectual rigour and innovative problem solving
- Professional and technical competence
Teaching and learning strategies
Orientation activities
Preparation for the Session - students are expected to undertake activities prior to the first week. These activities (approximately two hours in duration) include online readings, videos (database searching) and interaction with peers and are important in helping students prepare for the subject’s Assessment Tasks. This also provides students with an opportunity to meet and interact with peers.
Students will learn through independent learning activities, group work, peer review, and participation in collaborative online sessions through the learning management system.
Independent learning activities
Relevant readings, videos and activities will be made available online relevant to the topic of the week. Students are expected to come to the collaborative online sessions prepared. This will enhance the students’ ability to progress successfully throughout the subject and complete assessment items effectively. The online material aims to enhance students’ understanding of the topic or delve deeper into a more specific area. Information and links to all these learning activities can be accessed via Canvas as well as the subject outline.
Online collaborative sessions
The online collaborative sessions will provide opportunities for group activities and discussion, self-assessment, peer review and formative feedback from the subject facilitator. Online collaborative sessions will be conducted at a time that enables the majority of students to contribute.
Feedback
Feedback will be frequent and takes several forms, including self-assessment and peer review. Formative feedback throughout the subject aims to increase student performance at summative assessments
Content (topics)
- Understanding of SAS environment
- Accessing and importing different types of data
- Data exploration and validation
- Data preparation
- Data analysis
- Exporting results and reporting data
Assessment
Assessment task 1: Computer-based Data Analysis (Individual)
| Objective(s): | This addresses subject learning objective(s): 1 and 2 |
|---|---|
| Weight: | 50% |
Assessment task 2: Computer-based Data Analysis and Reporting (Individual)
| Objective(s): | This addresses subject learning objective(s): 2 and 3 |
|---|---|
| Weight: | 50% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
There is no required textbook. However, it is recommended students become familiar with SAS learning material available online at https://www.sas.com/en_au/learn/academic-programs/resources/free-sas-e-learning.html.
References
Business Data Processing, Prudhomme G. Arcler Press, 2018. ISBN-10: 1773614479
The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman, Springer, 2009. ISBN: 9780387848570
SAS e-learning: https://www.sas.com/en_au/learn/academic-programs/resources/free-sas-e-learning.html