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DRPS : Course Catalogue : School of Biological Sciences : Postgraduate

Postgraduate Course: Introduction to Python Programming for Data Science (PGBI11123)

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
SummaryThe 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 Data Science Technology and Innovation students 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 related to data science
- 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, and students and lecturer
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed:
Prohibited Combinations Students MUST NOT also be taking Python Programming for the Life Sciences (BICH11008) OR Introduction to Practical Programming with Objects (INFD11001) OR Bioinformatics Programming and System Management (PGBI11095)
Other requirements None
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  40
Course Start Semester 2
Course Start Date 15/01/2024
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Online Activities 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) ICA Multiple Choice Questions: 10%
Mid-course Programming Project: 45%
Final Programming Project: 45%
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 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. Demonstrate a critical understanding of basic Python programming, and its use in managing, analysing and visualising data via data science-oriented modules such as NumPy, SciPy, pandas and matplotlib.
  2. Demonstrate extensive skill in using iPython and Jupyter Notebooks hosted on Edina's Noteable.
  3. Plan, conceptualise and implement complete, realistic Python applications to a given specification.
  4. Develop original and creative code which is functional, efficient, clear, readable and well documented.
  5. Exercise autonomy and initiative in locating and utilising supporting resources, including 3rd party library code, documentation, and online materials to support development and debugging.
Reading List
Additional Information
Graduate Attributes and Skills 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'.
Keywordsprogramming,coding,python,data science,analytics,visualisation
Course organiserDr Douglas Houston
Tel: (0131 6)50 7358
Course secretaryMr Alex Ramsay
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