| Unit name | Introduction to Coding and Data Analysis for Scientists |
|---|---|
| Unit code | SCIF10002 |
| Credit points | 20 |
| Level of study | C/4 |
| Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
| Unit director | Professor. Rigby |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Chemistry |
| Faculty | Faculty of Science |
Why is this unit important?
The Python computer language will be used to cover the basics of computer programming for scientists. Methods of scientific programming and important background concepts of computer science will also be explored, to give students the knowledge necessary to participate in higher level computing courses. These programming skills will then be applied to enhance scientific data collection and analysis.
Teaching will be delivered, as much as possible, through hands-on programming workshops, supported by tutorials and seminars. Much of the material lends itself to flipped/modular/bite-sized teaching, allowing the students to accumulate credits throughout the teaching period. It is anticipated that approximately two-thirds of the time will be devoted to modern programming methods.
How does this unit fit into your programme of study
This unit is designed for students in the first year of the “X with Computing” and “Data Science” degrees in the Faculty of Science. It provides the foundations of coding and data analysis using Python and future units in these degrees will build on it.
An overview of content:
The topics covered are as follows:
How will students, personally, be different as a result of the unit
The ability to code is transformative in increasingly diverse fields. You will be able to tackle conceptually challenging or time-consuming tasks that other students cannot, increasing your career options and employability.
Learning Outcomes:
After completing this unit, students should be able to:
The learning of programming languages and computational techniques is most effective when it is practice-based. You will learn through a blended approach. This will involve a mixture of face-to-face and online teaching, asynchronous lectures, online material to introduce the more mathematical or theoretical concepts, synchronous group workshops to allow you to develop your understanding and put into practice what you have learnt, as well as tutorials and seminars to explore the context and applications of the course material. We will make use of online forum and collaboration tools to foster a collaborative and creative mindset. Feedback will be provided for both coursework and formal assessments.
This unit will be continuously assessed using a hierarchy of methods aimed at assessing specific key concepts, through to broader, more open-ended applications. The format of assessment puts a strong emphasis on basic coding competency.
Tasks which help you learn and prepare you for summative tasks (formative):
Formative assessment is built into every aspect of this practice-based course. In workshops, you will be provided with interactive coding worksheets containing a range of problems in scientific computing. By working through these problems in a workshop environment, you will be provided with instant feedback from the lecturer and your peers. Between workshops, online tests will be provided that will allow you to interrogate further your knowledge and understanding of key concepts.
Tasks which count towards your unit mark (summative):
Summative assessment will be through six online tests (30%, ILO's 1, 3, 4 and 5) and a set of four programming exercises (70%, ILOs' 1, 2, 4 and 5).
When assessment does not go to plan
If you are unable to complete successfully the assessment for the unit, either because of exceptional circumstances or through academic failure, you will be set a single alternative synoptic assessment to test all of the intended learning outcomes of this unit on an appropriate reassessment timescale
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SCIF10002).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
