CSC-10077 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2024/25

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

none

Barred Combinations

none

Description for 2024/25

This module provides an introduction to common techniques for exploring, summarising, and modeling data. The module develops transferable skills through solving problems, modeling, and using spreadsheets to handle quantitative information. Emphasis is placed on understanding the meaning behind the data and on the importance of the correct presentation of findings. Furthermore, it provides an introduction to common techniques for exploring, summarising, and modeling data. The module develops transferable skills through solving problems, modeling, and using spreadsheets to handle quantitative information. Emphasis is placed on understanding the meaning behind the data and on the importance of the correct presentation of findings.

The module follows the following aims:
1) To provide students with a foundation in calculus necessary and required for data science. It will include topics such as:
a) complex numbers
b) matrices and series
c) differentiation and integration.
2) to understand some of the more common statistical techniques, to encourage good practice, and to highlight common errors and misconceptions. Key to this module is to provide a differentiated learning framework for apprentices, some of whom may not have had significant mathematical and statistical education beyond Level 2 whilst others may have a level 3 or higher mathematics background.
Specifically, the module aims to develop:
a) a sound knowledge of mathematical concepts, skills, and techniques important in the use of data science.
b) confidence in applying mathematical and statistical thinking and reasoning in a range of new and unfamiliar contexts to solve real-life problems;
c) competency in interpreting and explaining solutions to problems in context;
d) fluency in procedural skills, common problem-solving skills, and strategies.

Intended Learning Outcomes

Analyse data sets by selecting and applying appropriate graphical techniques to summarize, present, and interpret findings.
: 1,2,3,4
Apply mathematical and statistical methodologies to address real-world problems, demonstrating the ability to translate theoretical concepts into practical solutions.
: 1,2,3,4
Evaluate and interpret the solutions to problems, considering their relevance, accuracy, and implications.: 1,2,4
Expand given functions into series and related functions, leveraging mathematical knowledge and techniques.: 3,4
Solve first and second-order ordinary differential equations (ODEs) using established analytical and numerical methods.
: 3,4
Conduct matrix operations, including the calculation of eigenvalues, eigenvectors, matrix decomposition, and other related processes for 2x2 and 3x3 matrices.: 4
Implement numerical methods for the accurate computation of integrals and derivatives.: 4

Study hours

36 hours of practical sessions during block release
36 hours of online lectures
200 hours of independent study
28 hours dedicated to completing coursework

School Rules

None

Description of Module Assessment