MAR8085 : Research Skills (Inactive)
MAR8085 : Research Skills (Inactive)
- Inactive for Year: 2024/25
- Module Leader(s): Dr Yongchang Pu
- Lecturer: Mr David McGeeney
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
This module aims to:
(1) introduce the underlying ideas and concepts associated with research methodologies, the ethics and
philosophy of science.
(2) equip students with a basic capability in understanding and using common statistical concepts and
techniques.
(3) learn Python programming language and its application to various research scenarios.
(4) enable students to understand machine learning techniques and its applications to research in engineering.
Outline Of Syllabus
Research Skills; Research objectives; research ethics.
Statistics; data summary and data presentation. Basic concepts of probability. The rationale behind sampling, randomisation and sampling strategies. Discrete distributions; binomial and Poisson.
Continuous distributions; normal and exponential. Estimation. The central limit theorem. Confidence intervals for means using the t-distribution. Approximate confidence intervals for proportions using a normal approximation. Hypothesis testing; basic ideas and concepts.
Interrelationships between testing and confidence intervals. Tests on means and proportions. X2 goodness of fit tests. Analysis of contingency tables. Correlation and regression. Practical classes will be based on the statistical package MINITAB. Multiple regression analysis (MRA) & principle component analysis (PCA) using PRIMER software. Modelling and simulation software.
Python programming language and its application to various research scenarios.
Machine learning techniques and its applications to research in engineering.
Learning Outcomes
Intended Knowledge Outcomes
On successful completion of this module, students will be able to demonstrate knowledge and understanding of:
IKO1. Research objectives, skills and ethics.
IKO2. Statistical concepts, tools, and their applications to engineering
IKO3. Python programming language.
IKO4. Machine learning and its applications to engineering
Intended Skill Outcomes
On successful completion of this module, students will develop the following subject specific and intellectual skills:
ISO1. Plan and manage a project
ISO2. Select appropriate statistical tools and process data for a research project
ISO3. Develop Python programs for engineering applications
ISO4. Develop machine learning models for engineering applications.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 12 | 1:00 | 12:00 | Present-in-person lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 11:00 | 11:00 | Open Book Assignment |
| Scheduled Learning And Teaching Activities | Small group teaching | 12 | 1:00 | 12:00 | Tutorial sessions, Present-in-person |
| Guided Independent Study | Skills practice | 1 | 2:00 | 2:00 | MCQ online quiz, compulsory to pass. |
| Guided Independent Study | Independent study | 1 | 40:00 | 40:00 | General revision, reviewing lecture notes, background reading |
| Guided Independent Study | Independent study | 1 | 11:00 | 11:00 | Examination revision |
| Guided Independent Study | Independent study | 12 | 1:00 | 12:00 | Online lectures |
| Total | 100:00 |
Teaching Rationale And Relationship
The lectures are designed to assist students in the acquisition of a knowledge base that will facilitate understanding of concepts, methods tools and techniques.
Independent study involves:
1. study following lectures and practicals
2. study in preparation for the assessed coursework and exams, which provides an opportunity to bring
together relevant knowledge and understanding and cognitive, research-related, and assessed key skills.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 100 | Open-book Report on statistics, taking approximately 11 hours. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 2 | M | MCQ online quiz, it is compulsory to pass it. |
Assessment Rationale And Relationship
The report on statistics (semester 2) provides students with an opportunity to demonstrate knowledge, understanding and the possession of subject-specific, cognitive and key skills.
The research skills material covered in the module is assessed via the end of semester computer based MCQ examination.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAR8085's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- MAR8085's past Exam Papers
General Notes
N/A
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The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.