CS4S767 - Data Mining 01 Sep 2022 - 31 Aug 2028 | Version 3
Associated Module Information
| Module Code: | CS4S767 | ||
|---|---|---|---|
| Module Title: | Data Mining | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Computer Science | ||
| Module Leader: | Andrew Ware, Ieuan Griffiths | ||
| Module Team: | Shiny Verghese, Christopher Tubb, Peter Parody, Abigail Peters, Robert Berry | ||
| First Intended Intake: | SEP 2018 | Final Year of Intake: | |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100956 - programming | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 3 |
|---|---|
| Valid From | 01 Sep 2022 |
| Valid To | 31 Aug 2028 |
Module Aims
Appreciating the value of data mining in solving real-world problems by conveying foundational concepts of data mining, big data, and data analytics.
Knowledge of key concepts, algorithms, and techniques commonly used in data mining and big data tools for collection and analysis of data sets.
Ability to apply these tools to real-world problems.
Content Summary
Introduction, basic concepts and motivation.
Data pre-processing: handling missing values, basic data transformations, quality of data.
Data warehousing
Rule induction; decision trees; naïve Bayesian probability; neural networks.
Classification using, e.g., decision tree induction, Naïve Bayesian classification, K-means clustering,
Outlier detection, post-processing.
Social impact and trend of data mining.
Application areas; Practical examples of relevant applications.
Fundamental Terminology & Concepts of Big Data: Big data types, Big data sources.
Characteristics of Data in Big Data Environments.
Dataset Types in Big Data Environments.
Fundamental Analysis and Analytics.
Machine Learning.
Data Visualisation.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 24 |
| Independent Study | 80 |
| Directed Study | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Display knowledge of different data mining and Big Data tasks and appropriate models/algorithms evaluating these with respect to their accuracy. |
| LO2 | Demonstrate the ability to apply data mining and Big Data concepts in appropriate contexts. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Practical Written Work 2 | Apply suitable techniques to a data set and draw conclusions from the results. | 0 | 2000 | 50 | No | 40 |
| Asynchronous Assessment | Practical Written Work 1 | Choose and justify appropriate methods to handle different types of data sources. | 0 | 2000 | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Practical Written Work 2 | ✔ | ✔ | |
| Practical Written Work 1 | ✔ | ✔ | |