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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : School of Biological Sciences : Postgraduate

Postgraduate Course: Python Programming for the Life Sciences (BICH11008)

Course Outline
SchoolSchool of Biological Sciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThis course is aimed at those with no prior experience of programming. Therefore, the course will consist of introductory programming learning material presented in the Python language. All material and teaching will be available online through Learn and Collaborate.
Course description This course is aimed at those with no prior experience of programming. Therefore, the course will consist of introductory programming learning material presented in the Python language. All material and teaching will be available online through Learn and Collaborate, and will consist of:
Exercises to demonstrate the main principles of computer programming through hands-on activities
Video lectures to explain and expand on more difficult points
Collaborate flipped classrooms to provide face-to-face contact time with lecturers
Group online discussion forum to allow communication between students
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Introduction to Python Programming for Data Science (PGBI11123)
Other requirements None
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  40
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Successful completion of programming exercises and production of correctly functioning code 50%
Successful completion of group-based coding project and production of correctly functioning program 50%
Feedback Expected output to the exercises will be provided during each sessions so students can check their code is functioning correctly
Model answers (code) to all exercises will be distributed the following week so that students can see how the correct output is generated.
Students will be instructed in the meaning of Python error messages and other debugging skills so that the computer will provide meaningful feedback as the student works
The formative group project in week 5 will provide instant feedback to students- their program will either work or not and in addition more nuanced feedback from a lecturer on the strengths and weaknesses of their work will be provided
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Describe the growing importance of programming and relevance to the Life Sciences
  2. Explain the capabilities and limitations of algorithms
  3. Give examples of currently intractable problems that will be solved in the future through coding
  4. Use coding basics: simple variables, string operators, arithmetic operators
  5. Solve examples problems via writing functional code: functions, complex variables (arrays), applyingan example function
Reading List
Nine Algorithms That Changed the Future, MacCormick, J. (2013)Nature Volume 440 Number 7083 Special Issue, How Computers will Change the Face of Science (2006)Python for Biologists: A complete programming course for beginners, Jones M.(2013)
Additional Information
Graduate Attributes and Skills Students will acquire a number of the transferable skills specified in the Vitae Researcher Development Framework (Domains A, B and D) and the CBI's Future Fit higher education report :
Enquiry: students will be confident in their ability to successfully search for and identify programming knowledge resources.
Personal and intellectual autonomy: Students will become accustomed to solving programming problems autonomously.
Communication: Students will be familiar with online communication, collaboration and knowledge transfer.
Personal effectiveness: Flexibility; many students have limited exposure to maths/computation in which there is more than one 'right' answer, but in programming, there is always more than one way to do it.
Application of numeracy and information technology: Students will advance beyond traditional 'IT Skills' learning (which usually consists of learning how to use software packages such as Word) and will understand what makes such programs work 'under the bonnet'.
The emphasis on interdisciplinary training (computing, biology and mathematics) provides novel opportunities for graduates.
KeywordsPython,Programming,Life Sciences
Contacts
Course organiserDr Douglas Houston
Tel: (0131 6)50 7358
Email: DouglasR.Houston@ed.ac.uk
Course secretaryMs Andrea Nichol
Tel: (0131 6)50 8643
Email: Andrea.Nichol@ed.ac.uk
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