THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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

Postgraduate Course: SAS Programming for Financial Analysis (CMSE11410)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummarySAS 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-world dataset.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Block 3 (Sem 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 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework (individual) - assesses all course Learning Outcomes
Feedback Formative: 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.

Summative: students will receive feedback on the assessment.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Know, understand and critically discuss the basics of coding on high-level programming languages.
  2. Know, understand and critically discuss the role of programming approaches in data analytics.
  3. Know, understand and critically discuss how programming allow for the creation of customised models and algorithms.
  4. Apply the main routines available in SAS for data analysis to a range of problems.
  5. Conceptualise and critically evaluate the structure of estimation commands in standard statistical packages.
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.
Additional Information
Graduate Attributes and Skills Communication, ICT, and Numeracy Skills

After completing this course, students should be able to:

Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
KeywordsNot entered
Contacts
Course organiserDr Belén Martín-Barragán
Tel: (0131 6)51 5539
Email: Belen.Martin@ed.ac.uk
Course secretaryMs Nikki Kohly
Tel: (0131 6)50 3825
Email: Nikki.Kohly@ed.ac.uk
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