Foundation degrees

Course Information

Research and Professional Skills

Module summary

Module code: BIOT1004
Level: 5
Credits: 15
School: Engineering and Science
Department: Pharm, Chemical and Envi Sci.
Module Coordinator(s): Bruce Alexander



• To provide the necessary background for students to appreciate the relevance and importance of rigorous analytical data and the methods required to process chemical, forensics and pharmaceutical sciences data.
• To provide students with the relevant tools to plan, and carry out investigations in an appropriate manner.
• To gain an understanding of the skills and attributes required for a graduate to be successful in the employment market and in their own personal growth.

Learning outcomes

On successful completion of this course a student will be able to:
1 Identify, select and apply appropriate statistical methods for analytical data interpretation.
2 Understand the importance of critical analysis in analytical research and data presentation.
3 Employ self-reflection to identify strengths and weaknesses in the student’s skillset and adopt a plan to develop themselves and prepare for the employment market. This should reflect the University Greenwich Graduate Attributes, Sustainable Development and research informed learning.
4 Appropriate use of analysis software, data recording, data management and presentation, and bibliographic management.

Indicative content

Students will typically encounter subjects taken from the following topics, either through formal lectures and workshops, or through activities linked to the Employability and Personal Development Planning ePortfolio:

Numerical and Analytical skills. • Analysing data, presenting reports. - Selection and use of computer software for Data Collation, Storage and Management;An introduction to Data: Types and formats of analytical data;An Introduction to Univariate Data Analysis:Distributions and Probabilities;Hypothesis testing;Inferential statistics: Parametric tests including t-test, F-test, ANOVA, Pearson’s correlation.
An Introduction to data modelling and regression analysis;Trend analysis;Detection of outliers.

Enquiry based Learning and Research. - Scientific methods & principles of scientific investigation.

Communication • Written • Oral • Presentations. - Data presentation and visualisation. Networking and interview skills.

Digital literacy / Information technology. • ICT packages. • Social media knowledge. • Online marketing and business knowledge. - Word processing. Excel and other statistical packages with functions to support activities above. Development of a professional profile, e.g. LinkedIn.

Information literacy. - Bibliographic databases and reference management.

Professionalism. • Cultural competence. • Ethics. • Respect for others. - Reflection and action planning.

Enterprise, creativity and environmental awareness. Design and development of processes, systems, services and products. - Introductory Quality management and assurance – e.g. ISO 17025:2005.

Learning. Using Feedback and Exam Skills and Techniques. - Using reflection to enhance exams; what have I learnt from feedback.

Teaching and learning activity

The course will be taught largely through lectures and laboratory-based work. The theoretical principles will be explored in the lectures and the laboratory work will make practical use of the principles by allowing students to work their way through data relevant to their degree subject.

For the personal development planning component of the course, the learning is largely self-directed with some opportunity for face-to-face lectures and ongoing support by personal tutors/academic mentors. The student is required to share with their personal tutors their reflections and plans.


Practical Computer Laboratory coursework.
Learning outcomes: 1,2,4
Grading Mode: Numeric.
Weighting: 35%.
Computer and IT Data Analysis and Interpretation reports.

PDP Portfolio
Learning outcomes: 3.
Grading Mode: Numeric.
Weighting: 25%.
Submission of an employability and personal development eportfolio, curriculum vitae and attendance at employability workshops.

Learning outcomes: 1,2,4
Grading Mode: Numeric.
Weighting: 40%.
2 hr paper.

Students are not required to pass all summative assessments in order to pass the course.

Nature of FORMATIVE assessment supporting student learning: • Laboratory sessions. • Formative submission of PDP, formative feedback from personal tutors electronically and/or as part of personal tutoring meetings.

Pass Mark 40%.