KV5039 - Data Mining

What will I learn on this module?

This module will give you an essential foundation to focus on extraction of implicit and potentially useful information form data which is used in commercial, scientific and other application areas. The module combines both theoretical and practical approaches, so you have the skills to tackle problems in various realistic business settings.

This module introduces the trends, tools, and current developments in the area of data mining and its practical applications. The module covers key background for critical understanding of data mining including relevant professional, ethical, social and legal aspects. It covers topics such as labelled and unlabelled data, supervised and unsupervised learning, algorithms and techniques for feature extraction, data for data mining, outlier detection, topic modelling and prediction of complex unstructured data sets, and dealing with large volumes of data. The module is primarily concerned with the data mining of real-world environments (e.g., datasets from businesses, industries) and to uncover patterns, find anomalies and relationships that can be used to make prediction about future trends.

How will I learn on this module?

This module includes a combination of methods to support learning, including lectures and computer assisted workshops allowing you to put the theory from lectures into practice, and independent learning. Topics will normally be introduced in lectures and explored through real world examples and practical exercises (helping you develop the knowledge and understanding needed) and guided learning activities. You will be encouraged to develop independent learning skills to explore further in the subject area. For example, by reading the latest research (reports/papers), which will be directed in your lectures/seminar material.

How will I be supported academically on this module?

You will be given advice and feedback on your work and progress during the timetabled classes.

The module team will guide and support you in the lectures and workshops practical sessions and provide you feedback on your work. During the lectures, you will be required to conduct interactive activities based on the lecturers’ guidance. In addition, the module’s eLP (electronic learning portal) / blackboard is used to provide extensive support materials.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

What will I be expected to achieve?

Knowledge & Understanding:
KU1. Demonstrate wide knowledge, and critical understanding, of essential – and developing - concepts, principles, theories, techniques and technologies related to computing, computer science, and data science.
KU2. Make evident a broad knowledge and understanding of techniques and tools for the specification of requirements, analysis, design, implementation, testing and management of computing and data science systems.
KU3. Demonstrate a critical understanding of user, professional, ethical, social, legal and economic issues and risks surrounding the design, development, operation and maintenance of computing and data science systems.

Intellectual / Professional skills & abilities:
IPSA1. Demonstrate critical computational thinking and its relevance to data, statistics and everyday life.
IPSA2. Analyse, design, build, test and manage computing and data science applications, using a range of tools, and using a software engineering approach

How will I be assessed?

Formative assessment: Exercises provided and carried out within practical classes and workshops will build up to form a basis of the summative assessment. Feedback will be given during these practical classes and workshops and/or through discussions via email/blackboard forum.

Summative assessments: A written assignment (4000 words) comprising extraction of data for a certain scenario or scenarios by using appropriate data mining techniques to uncover patterns and find anomalies and relationship (100%) and will test MLOs KU1, KU2, KU3, IPSA1 and IPSA2.

Feedback: You will be given detailed feedback on the assignment clearly identifying both the weaknesses and strong points of the work.

Pre-requisite(s)

N/A

Co-requisite(s)

N/A

Module abstract

The aim of this module is to provide you with the knowledge and practical skills for data mining of data. The module combines both theoretical and practical approaches so that you will have the skills to tackle problems in various realistic business settings. This module includes a combination of methods to support learning, including lectures and computer assisted seminars allowing you to put the theory from lectures into practice. Topics will normally be introduced in lectures and explored through real world examples and practical exercises (helping you develop the knowledge and understanding needed) and guided learning activities. You will be encouraged to develop independent learning skills to explore further in the subject area.

Course info

UCAS Code G415

Credits 20

Level of Study Undergraduate

Mode of Study 3 years full-time or 4 years with a placement (sandwich)/study abroad

Department Computer and Information Sciences

Location City Campus, Northumbria University

City Newcastle

Start September 2024 or September 2025

Fee Information

Module Information

All information is accurate at the time of sharing. 

Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.  

Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.

 

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