ADVANCED MATHEMATICS AND COMPUTING B - 2024/5

Module Overview

This module builds further on the mathematical and computing skills that you developed previously. As before, there is a strong emphasis on critical thinking, problem solving and becoming more independent as a learner. A number of advanced topic areas will be introduced in both the mathematics and computing components. These two module components are equally weighted at 50% each. There are a total of 11 advanced mathematics lectures with associated tutorials and 11 computer laboratory sessions with associated tutorials.The main topic areas covered by the mathematics component are; matrices & vectors, complex numbers and calculus. Associated geometrical concepts are introduced in all of these topic areas. In the computing component you will learn to use Python and associated packages as a language for implementing a variety of interesting and challenging processes. An emphasis will be placed on the process of abstraction and implementation, with process design considerations at holistic and atomic levels.Your progress on the module is assessed in 12 separate units of assessment (6 for mathematics and 6 for computing.)

Module provider

Sustainability, Civil & Env Engineering

Module Leader

HARRISON Richard (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 3

Module cap (Maximum number of students): 58

Module Availability

Semester 2

Prerequisites / Co-requisites

ENG0011 (Mathematics A), ENG0018 (Computing laboratory) and ENG0019(Advanced Mathematics and Computing A). Co-requisites: ENG0012 (Mathematics B) These are compulsory modules for all students.

Module content

Topics in pure mathematics, applied mathematics and computational methods, including the application of calculus, probability, statistics and matrices. Data processing techniques and visualisation, computational (numerical) modelling and problem solving using Python. You will apply simple programming constructs to manipulate and present numerical and non-numerical data. The Python packages, NumPy, Pandas, Matplotlib and SciPy will be introduced and used and there will be a basic introduction to machine learning with Sci-kit.

Assessment pattern

Alternative Assessment

Not applicable

Assessment Strategy

 The module has two equally weighted (50%) assessment components reflecting the balance of the mathematics and computing elements.

Mathematics

The assessment strategy for the mathematics components is designed to provide you with the opportunity to demonstrate

(i)  knowledge of relatively advanced mathematical concepts and rules

(ii) the development of critical thinking skills in interpreting and solving a variety of problems, in different contexts

(iii) an appropriate and accurate application of the mathematical techniques to a given problem.

Continuous in-semester assessment: you will attempt 5 x sets of online questions at fortnightly intervals. The questions will be MCQ/short answer. 

End of module examination: you will attempt structured questions in an invigilated paper based examination, covering academic content/scenarios not previously assessed in the continuous assessment. The end of module exam will examine all learning objectives for the mathematics component.

Computing

The assessment strategy for the computing components is designed to provide you with an opportunity to demonstrate

(i) you have grasped more advanced processing techniques using Python

(ii) you have developed critical thinking skills in interpreting and solving a variety of problems, in different contexts

(iii) you can apply simple processing strategies/algorithms

(iv) you can select and apply appropriate mathematical methods to a particular computational problem.
 

Continuous in-semester assessment: you will attempt 5 x sets of online questions at fortnightly intervals. The questions will be MCQ/short answer. 

Coursework task: you will carry out a coursework task covering multiple learning objectives based on and extending the work in the lab worksheets. There will be a practical submission (Python code file) together with a short written report. Time guidance for coursework is 10 hours. The single coursework component will assess all learning objectives not already assessed in the continuous assessment.
 

Summative assessment summary
The short online mathematics tests are always set on Friday's on even numbered teaching weeks; 2, 4, 6, 8 and 10.

The short online computing tests are always set on Friday's on odd numbered teaching weeks; 3, 5, 7, 9 and 11.

Computing coursework is typically set in week 5 and submitted in week 10.

The end of module mathematics exam is in the semester 2 exam period.

Formative ‘assessment’ is ongoing throughout the semester through work on tutorial questions in mathematics and laboratory worksheets.

 

Feedback from formative assessment is provided orally on a one-to-one basis and to the whole group in tutorial/problems classes. Fully worked solutions to mathematics tutorial problems will be provided via SurreyLearn.

Feedback will be provided on the continuous online assessment via Surreylearn.

 

Module aims

• Reinforce and extend existing mathematical knowledge.
• Develop competency in applying some relatively advanced mathematical concepts.
• Develop critical thinking and problem solving skills in mathematical and computational processes.
• Introduce some of the wider symbolic language of mathematics.

Learning outcomes

Methods of Teaching / Learning

Mathematics component:

The teaching and learning strategy is designed to familiarise you with mathematical concepts and techniques, supported by extensive use of examples and applications; you will be engaged in the solution of problems and application of techniques in tutorials/problems classes.

The learning and teaching methods include:

• Lectures (1 hr/week, for 11 weeks) to introduce new concepts and techniques and provide illustrative examples and applications.

• Tutorial classes (1 hr/week for 11 weeks) for the development of skills in problem solving, using problems sheets. Assistance is given both at individual level, and for the group on common areas of difficulty.

• Guided self-study to cover certain topics, in order to develop your independent learning skills.

• Problem sheets of examples for technique selection and skills development.

• Independent learning approx. 5 hrs/week

Computing component:

The teaching and learning strategy is designed to facilitate your practical and critical thinking skills development in a challenging problem solving context involving a variety of concepts from mathematics, statistics, data analysis, modelling and computing. The processes, concepts and techniques, are reinforced in a “hands on” manner using dedicated laboratory worksheets designed to be used at the computer. You will be engaged in practical and theoretical tasks as well as critical thinking/problem solving as you work through each laboratory worksheet.
 

• Computer laboratory practicals


• Computer laboratory tutorial/practical


• Support sessions


• Independent learning and completing lab worksheets

 

 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ENG0020

Other information

Foundation Year programmes are committed to developing students with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, Resourcefulness and Resilience. This module is designed to develop knowledge and skills in the following:

 

Digital capabilities: You will develop skills and confidence in using a variety of software tools. You will become conversant with working within the university VLE (SurreyLearn) through dedicated training sessions which are reinforced with embedded activities in specific modules (which may use different aspects of Surreylearn functionality, particularly for different assessment styles.) Further applications that you will develop competency in for producing academic output include using MS Office tools such as Word and Excel. You will be introduced to the Python programming language and Jupyter notebook.

 

Employability: you will learn to use industry standard software packages such as MS Office suite and Python (an industry standard for data processing and machine learning) as well as some of the Python packages.

 

Global and cultural capabilities: The student cohort has a diverse spectrum of social and cultural backgrounds. You will be encouraged to work together, particularly in lab and tutorial sessions where you will gain exposure to different points of view, approaches and experiences.

 

Resourcefulness and resilience: The module is designed in such a way as to encourage and support the progressive development of independent thinking and resourcefulness through scaffolded activities and assessments. You will be exposed to challenging, authentic scenarios which invariably lead to setbacks and frustration. You will be encouraged to reflect and fault find and to question your strategy if the outcome of a problem-solving process is not as expected. You will learn how to seek verification of your output through independent research or peer collaboration and how to respond constructively to formal and informal feedback.  

 

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.