Postgraduate Course: SAS Programming for Financial Analysis (CMSE11410)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | SAS software is the standard software used in Financial and Banking industry for analysing and comparing datasets, as well for creating and coding customised models. The aim of this course is to provide students with an introduction to programming in a high-level language, using SAS as an example. Students will acquire hands-on practice on SAS by analysing and manipulating databases. |
| Course description |
SAS software is the standard software used in Financial and Banking industry for analysing and comparing datasets, as well for creating and coding customised models. The aim of this course is to provide students with an introduction to programming in a high-level language, using SAS as example. Students will acquire hands-on practice on SAS by analysing and manipulating databases. Students will acquire skills on reading, running, manipulating and creating programs in SAS environment. Good programming practice for sharing their programs with others. Database creation and manipulation. Data analysis and regression via SAS. Introduction to macro language and program development for repetitive analysis.
Outline content:
- Introduction, getting started with SAS
- Reading and creating datasets.
- Combine datasets
- Statistics and Regression
- Introduction to Macro Language
Student Learning Experience:
Students will learn mainly via demonstrations and hands-on exercises. The students will be provided with exercises to practice at home. Evidence of knowledge will consist on an individual piece of code for analysing a real-word dataset.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc Finance, Technology and Policy students, or by permission of course organiser. Please contact the course secretary. |
Course Delivery Information
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| Academic year 2019/20, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Case Study Analysis (Individual) 100%
Assessment of this course is through an individual project where a dataset provided by the lecturer will be analysed. The students should use SAS software to analyse it and present the result
All of the below elements of the course will be tested in the course assessment.
- Introduction, getting started with SAS: LO 1,2.
- Reading and creating datasets: LO 1,2,3,4.
- Combine datasets: LO 1,2,4.
- Statistics and Regression: LO 1,2,3,4,5.
- Introduction to Macro Language. LO 1,2,3,4. |
| 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 to assess their knowledge.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Know, understand and critically discuss the basics of coding on high-level programming languages.
- Know, understand and critically discuss the role of programming approaches in data analytics.
- Know, understand and critically discuss how programming allow for the creation of customised models and algorithms.
- Apply the main routines available in SAS for data analysis to a range of problems.
- Conceptualise and critically evaluate the structure of estimation commands in standard statistical packages.
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Reading List
Recommended:
Lora D. Delwiche and Susan J. Slaughter (2012) The Little SAS Book: A Primer. 5th edition.
Stephen. McDaniel Chris Hemedinger., Hoboken, N.J. SAS for dummies. Wiley Publishing, Inc. ;2010
Further reading:
SAS Institute SAS/STAT User¿s Guide 9.1.3. http://support.sas.com/onlinedoc/913/docMainpage.jsp
Cody, Ron (2007) Learning SAS by example: a programmer's guide. SAS Institute.
Cody, Ronald P., and Jeffrey K. Smith. Applied statistics and the SAS programming language. North-Holland, 1985.
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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 estimation commands in standard statistical package.
6. Demonstrate substantial authority and exercise a high level of autonomy and initiative in professional and equivalent activities.
7. 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
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| Special Arrangements |
For Business School / EFI Fintech PG students only, or by special permission of the School. Please contact the course secretary. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Belén Martín-Barragán
Tel: (0131 6)51 5539
Email: Belen.Martin@ed.ac.uk |
Course secretary | Miss Yvonne Stewart
Tel: (0131 6)51 5333
Email: Yvonne.Stewart@ed.ac.uk |
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