Postgraduate Course: Introduction to Python Programming (PGBI11119)
Course Outline
| School | School of Biological Sciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | 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 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 |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2019/20, Not available to visiting students (SS1)
|
Quota: 24 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Online Activities 10,
Feedback/Feedforward Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
86 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Mid-course programming exercise (50%)
Final programming project (50%) |
| Feedback |
In addition to the mid-course exercise (for which the students will be provided both personally tailored feedback and a class-level general feedback), there will be regular smaller programming exercises every week. Model answers for these will be made available and students will be encouraged to compare and discuss their solutions during the online Collaborate sessions and on the Discussion Forum. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Describe the growing importance of programming, and its relevance to data science, and science in general.
- Give examples of coding that made the impossible possible.
- Use coding basics: simple variables, string operators, arithmetic operators.
- Utilise program navigation in code: flow control, Boolean logic, input/output.
- Solve example data science problems via writing functional code: functions, complex variables (arrays), large datasets.
|
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'. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Douglas Houston
Tel: (0131 6)50 7358
Email: DouglasR.Houston@ed.ac.uk |
Course secretary | Mrs Claire Black
Tel: (0131 6)50 8637
Email: Claire.Black@ed.ac.uk |
|
|
University Homepage