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

Postgraduate Course: Python Programming (CMSE11433)

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 course will provide students with the basics of programming in Python. It requires no background knowledge and is specifically tailored to the novice's needs.
Anyone with an interest in technology will greatly benefit from following this course. A number of motivating examples will be considered from multiple areas including but not limited to business, finance, and data analysis.
Course description Academic Description
This course aims at introducing business students to the topic of programming in python as it is a key building block of many business analytics courses. Indeed, being able to collect and transform data, perform analyses on them, and do this in an efficient way, is the basic setup of many topics in statistics, business modelling, operational research, and so on. By providing a thorough background in the building blocks of programming and its applications, this course aims to provide non-technical profiles with the necessary basics to be mature in a programming environment.

Outline Content
The course is built (by following out of the following topics:
Lecture 1: strings, variables and operators, Control Structures
Lecture 2: list & tuples, sets and dictionaries, iterators & generators, functions
Lecture 3: Object-Oriented Programming, Errors and Exceptions
Lecture 4: numpy, pandas
Lecture 5: applications

Student Learning Experience
Teaching will take the form of class lectures, and lab sessions. The concepts and methods discussed during the lectures will be illustrated by means of assignments. Continuous feedback will be provided on a 2-weekly basis. The final exam takes the form of a computer exam in which various topics are combined in three different business problems for which a solution needs to be provided or extended.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
Academic year 2019/20, Not available to visiting students (SS1) Quota:  47
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Seminar/Tutorial Hours 20, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 66 )
Assessment (Further Info) Written Exam 0 %, Coursework 70 %, Practical Exam 30 %
Additional Information (Assessment) Group Assignments (70% weighting)
Assesses Learning Outcomes 1,2,3 and 5.
Weekly group assignments will be given that test the concepts seen in class.

Individual Examination (30% weighting)
Assesses Learning Outcomes 1 to 4.
Students will need to sit a computer-based exam.
Feedback The assessments will be marked according to the University common marking scheme. Feedback on formative assessed work will be provided in line with the Taught Assessment Regulation turnaround period, 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 will be communicated to students during semester.

No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Utilise basic Python programming constructs
  2. Apply a variety of programming paradigms such as procedural as well as object-oriented programming
  3. Operationalise existing python libraries, such as numpy and pandas, in the context of small coding exercises
  4. Document code and describe/communicate the structure of a programme
  5. Independently carry out a requirement analysis to identify procedures and data needed for tackling a problem and be able to communicate them to the relevant stakeholders.
Reading List
Learning Python (2013), Mark Lutz
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Pawel Orzechowski
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
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