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School of Life Sciences

R&P Skills I: Neuroscience (X2105)

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Research and Professional Skills I: Neuroscience

Module X2105

Module details for 2025/26.

15 credits

FHEQ Level 5

Module Outline

This is an interdisciplinary module designed to develop your research skills and employability. You will collect data using a range of laboratory techniques or simulations and apply analytical methods to address specific questions related to Neuroscience. These exercises will be placed in a wider context of material covered in other modules and will equip you with some of the skills needed to carry out research and be more competitive for graduate jobs. The module also includes a series of seminars focused on making career choices, preparing application documents, and building up your professional experience alongside your studies.

Module learning outcomes

Implement suitable statistics software to appropriately handle, analyse and interpret quantitative data collected either by experimentation, or from online databases or publications

Work cooperatively on scientific experiments and relate the laboratory techniques to how the techniques have contributed to our understanding of theoretical concepts taught in other modules

Prepare a lab report that demonstrates an understanding of the scientific methods and an ability to draw objective inferences from experimental results

Compose an effective CV and prepare for job interviews

TypeTimingWeighting
Coursework50.00%
Coursework components. Weighted as shown below.
Problem SetT1 Week 6 80.00%
PortfolioT1 Week 11 20.00%
Report (1500 words)Semester 1 Assessment Week 1 Mon 16:0050.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

TermMethodDurationWeek pattern
Autumn SemesterOnline Lecture1 hour11111000000
Autumn SemesterSeminar2 hours00100000000
Autumn SemesterSeminar1 hour01010000000
Autumn SemesterPractical2 hours11111011111
Autumn SemesterOnline Seminar1 hour00000010000
Autumn SemesterLecture1 hour00000100000

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Dr Andrew Penn

Assess convenor, Convenor
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