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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Working with Data in SAS (CMSE11453)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThe aim of the course is to provide the students with the knowledge and skills to work with datasets using SAS programming language. SAS is widely used by in banking for processing and analysing datasets. A brief introduction to Macro Language and basic coding commands is also included in the course.

Course description The course develops computational skills essential to make decisions when working in a bank. The course provides the basic knowledge needed to run programs in a SAS environment, to input and manipulate data, combining and modifying datasets and running statistical procedures using SAS. A basic introduction to coding informatics programs and creating Macro commands will be included.

Outline Content
A selection of topics among:

- Introduction, getting started with SAS
- Sorting, printing and summarising
- Formats and informats
- Manage the variables
- Help and General syntax
- Plotting and graphics
- Macro 1
- Macro 2
- Linear regression

Student Learning Experience
During the course, the students will be experiencing a real contact with the statistic software SAS. The program is introduced using a mixture of demonstration, self-learning and practice. A mixture of on-site, synchronous and asynchronous Q&A sessions will be used for interactions.
Each lecture will consist on a mini-project of increasing difficulty.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2020/21, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Seminar/Tutorial Hours 10, Formative Assessment Hours 5, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 73 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Case Study Analysis 100% - Assesses LO1, LO2, LO3, LO4.

The case study is composed by the following elements:
- Group 50%
- Peer Assessment 10%
- Individual 40%

The project/assignment will consist of one project consisting of quantitative analysis of business cases in which the students will go through the key steps of the data analysis process. All learning outcomes will be assessed in such assignment. The project will have group work elements (50%) individual work elements (40%) elements and peer assessment elements (10%).
Feedback Feedback on formative assessed work will be provided within 15 working days of submission, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which has been made clear to students at the start of the academic year.

Students will gain feedback on their understanding of the material when they perform computer lab exercises. Students may ask questions in lectures and in forums to assess their knowledge.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Know, understand and critically discuss how SAS programs work.
  2. Know, understand and critically discuss the main parts of a SAS program.
  3. Apply the main routines available in SAS for data analysis, to a range of problems.
  4. Conceptualise and critically evaluate the structure of estimation commands in standard statistical package.
Reading List
Delwiche, L.D. and Slaughter, S.J (2019) The Little SAS Book: A Primer. 6th edition.
Delwiche, L.D, Ottesen, R.A., and Slaughter, S.J. (2020) Exercises and Projects for The Little SAS Book, Sixth Edition.

McDaniel, S., Hemedinger, C., (2010) Hoboken, N.J. SAS for dummies. Wiley Publishing, Inc.

Further reading:
SAS Institute SAS/STAT User's Guide 9.1.3.
Cody, Ron (2007) Learning SAS by example: a programmer's guide. SAS Institute.
Cody, Ronald P., and Jeffrey K. Smith (1985). Applied statistics and the SAS programming language. North-Holland.
Additional Information
Graduate Attributes and Skills Cognitive Skills
On completion of the course a student should be able to:
1. Demonstrate a critical understanding of the role of high-level programming language in analytics.
2. Demonstrate a critical understanding of the potential and limitations of high-level programming language.
3. Demonstrate a critical understanding of the aims and potential of data analytics.
4. Demonstrate a high level understanding of the application of programmes to analyse and manage databases.
5. Conceptualise and critically evaluate the structure of process of analysing datasets, including collection, pre-processing, analysing the data and presenting the results.
6. Demonstrate leadership and/or originality in tackling and resolving problems and issues.

Subject Specific Skills:
After completing this course, students should be able to:
1. Read, run, manipulate and create programs in a SAS environment,
2. Manipulate programs written by others,
3. Input and manipulate data in SAS,
4. Analyse data and estimate regression models using SAS,
5. Develop programs in SAS for repetitive analysis,
6. Use SAS to analyse and manage databases.
KeywordsNot entered
Course organiserDr Belén Martín-Barragán
Tel: (0131 6)51 5539
Course secretaryMs Rhiannon Pilkington
Tel: (0131 6)50 8072
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