CSC-10054 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2020/21

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2020/21

This module develops a range of calculus techniques which students can then apply to problem solving. It will assist students with the transition from the methods-based approach of Level 3 mathematics to the higher levels of understanding and rigour expected for degree level data science, where relevant techniques need to be selected rather than being stated in the problems tackled. It begins with a consideration of first and second order differential calculus . This is followed by a study of matrix algebra and its application. The remainder of the module covers complex numbers and further techniques of integral calculus.

To provide students with a foundation in calculus necessary required for data science. This will include topics such as: complex numbers, matrices, series, differentiation and integration.

Intended Learning Outcomes

use relevant methods to solve problems and communicate their solutions accurately and reliably with structured and coherent arguments;: 1
expand a given function into a series and use this to find approximate values of the function;: 1
recognise and solve a variety of first and second order ordinary differential equations using appropriate methods;: 1
compute eigenvalues and eigenvectors of 2 x 2 and 3 x 3 matrices, applying this theory to reduce appropriate matrices to diagonal form;: 1
compute transposes, determinants and inverses of 3 x 3 matrices;: 1
use mathematical techniques in differentiation and integration to solve a given problem.: 1

Study hours

Online Lectures - 24 hours
Block release tutorials - 8 hours
Online Problems Activities - 12 hours
Completion of assessed problem sheets - 20 hours
Private study - 86 hours

School Rules

None

Description of Module Assessment