WM160-15 Applied Maths I
Introductory description
As digital technology systems undergo constant evolution, the utilisation of advanced mathematical and statistical tools proves instrumental in facilitating progress from foundational enhancements to achieving excellence. This module provides the foundation for the use of mathematical and statistical concepts to solve a myriad of data-related problems. Providing a robust foundation, the module describes the concepts essential for comprehensive data analysis, data visualisation, data interpretation and effective data modelling.
Module aims
This module aims to equip students with the mathematical knowledge and skills to analyse, model and solve problems in digital and information systems. Students will apply key mathematical and statistical concepts to address challenges associated with data, enhancing their capabilities in both problem-solving and decision-making contexts.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
- Introduction to data.
- Sampling techniques.
- Descriptive statistics.
- Probability concepts.
- Random variables.
- Statistical distributions (Continuous and Discrete).
- Hypothesis testing.
- Regression and correlation.
Learning outcomes
By the end of the module, students should be able to:
- Identify mathematical and statistical methods for problem solving and data modelling and discuss the quantitative results. [CITP: 2.1.9]
- Apply a range of statistical tools and techniques to solve problems in various systems. [AHEP:4 - C1]
- Explain the results of quantitative data analysis and interpret them in a meaningful way for decision making. [CITP: 2.1.9]
- Demonstrate practical skills in implementing statistical analyses through the utilization of applicable software packages. [AHEP:4 - C1]
Indicative reading list
- R. Peck, Statistics: Learning from Data, Cengage (2024), ISBN: 0357758293, 9780357758298.
- W. J. DeCoursey, Statistics and Probability for Engineering Applications with Microsoft Excel, Newnes (2003), ISBN: 0750676183, 9780750676182.
- C. A. Gorini, Master Math: Probability, Course Technology Cengage Learning (2012), ISBN: 1435456564, 9781435456563
- C. Chatfield, Problem Solving: A Statistician's Guide, CRC (2017), ISBN: 1482224208, 0429158750, 9781482224207, 9780429158759.
- S. L. Weinberg, D. Harel, S. K. Abramowitz, Statistics using R : An Integrative Approach, Cambridge University Press (2021), ISBN: 9781108719148.
View reading list on Talis Aspire
Subject specific skills
This module covers KSBs from new standard mapping. Apprentices are expected to gain/improve on the following:
- Principles of data management and data analysis for digital and technology solutions (K13).
- Determine and use appropriate data analysis techniques. For example, Text, Statistical, Diagnostic or Predictive Analysis to assess a digital and technology solutions (S11).
- A strong work ethic and commitment in order to meet the standards required (B1).
- Clear mathematical communication.
- Use of software to support decision making.
Transferable skills
Apprentices are expected to gain/improve on the following:
Problem-solving; critical thinking; communication; professionalism;
Study time
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (13%) |
| Seminars | 10 sessions of 1 hour (7%) |
| Online learning (independent) | 8 sessions of 1 hour (5%) |
| Other activity | 2 hours (1%) |
| Private study | 50 hours (33%) |
| Assessment | 60 hours (40%) |
| Total | 150 hours |
Private study description
- Pre-module work given on Moodle to encourage flipped learning approach.
- Self-guided study: revision on module contents, solution of additional excercises, and supplementary materials.
- Study and advanced use of software packages.
- Teams/forum for discussing queries with course peers and tutor (asynchronous).
Other activity description
Online support session to help struggling students.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group D3
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Post Module Assignment | 60% | 36 hours | Yes (extension) |
|
This assessment includes mathematical and statistical discussion and analysis based on given data, incorporating equations, formulas, figures, tables, and screenshots to present the obtained results. A structured proforma, a predefined document, guides students to provide detailed and organized responses to specific questions or prompts within designated sections. |
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| Examination | 40% | 24 hours | No |
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This assessment is designed to evaluate proficiency in mathematical and statistical techniques to solve problems. |
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Feedback on assessment
Verbal cohort-level feedback for in-module element (class test).
Written individual feedback for post-module element.
Post-requisite modules
If you pass this module, you can take:
- WM260-15 Applied Maths - II
Courses
This module is Core for:
- Year 1 of DWMS-H655 Undergraduate Digital and Technology Solutions (Cyber) (Degree Apprenticeship)
- Year 1 of DWMS-H652 Undergraduate Digital and Technology Solutions (Data Analytics) (Degree Apprenticeship)
- Year 1 of DWMS-H653 Undergraduate Digital and Technology Solutions (Network Engineering) (Degree Apprenticeship)
- Year 1 of DWMS-H654 Undergraduate Digital and Technology Solutions (Software Engineering) (Degree Apprenticeship)