Module Descriptors
DATA ANALYSIS
COCS50732
Key Facts
Digital, Technology, Innovation and Business
Level 5
15 credits
Contact
Leader: Tharaka Ilayperuma
Hours of Study
Scheduled Learning and Teaching Activities: 3
Independent Study Hours: 147
Total Learning Hours: 150
Pattern of Delivery
- Occurrence C, Stoke Campus, UG Semester 2
- Occurrence G, Stoke Campus, UG Semester 1
- Occurrence H, Stoke Campus, UG Semester 3 to UG Semester 1
Sites
- Stoke Campus
Assessment
- Coursework - individual essay 1500 words weighted at 50%
- Artefact - 1500 words demonstrating the concepts developed in the research paper weighted at 50%
Module Details
Module Learning Outcomes
1. DEMONSTRATE SYSTEMATIC UNDERSTANDING OF THE PRINCIPLES AND CONCEPTS AT THE FOREFRONT OF PROFESSIONAL PRACTICE INVOLVED IN DATA MINING AND DATA WAREHOUSING.
Analysis
Knowledge and Understanding
Learning
2. CRITICALLY EVALUATE THE VARIOUS MECHANISMS USED TO ENSURE DATA QUALITY, DISCUSSING THE LIMITS AND RANGE OF APPLICABILITY OF ANY CONCLUSIONS DRAWN.
Communication
Reflection
Analysis
3. SELECT THE MOST SUITABLE DATA MINING TECHNIQUES FOR SOLVING SPECIFIC PROBLEMS AND CRITICALLY EVALUATE THEIR STRENGTHS AND LIMITATIONS IN CERTAIN APPLICATIONS.
Enquiry
Problem Solving
Analysis
Knowledge and Understanding
Learning
2. CRITICALLY EVALUATE THE VARIOUS MECHANISMS USED TO ENSURE DATA QUALITY, DISCUSSING THE LIMITS AND RANGE OF APPLICABILITY OF ANY CONCLUSIONS DRAWN.
Communication
Reflection
Analysis
3. SELECT THE MOST SUITABLE DATA MINING TECHNIQUES FOR SOLVING SPECIFIC PROBLEMS AND CRITICALLY EVALUATE THEIR STRENGTHS AND LIMITATIONS IN CERTAIN APPLICATIONS.
Enquiry
Problem Solving
Module Additional Assessment Details
COURSEWORK length 3000 WORDS weighted at 100%.
(Learning outcomes 1, 2 and 3)
Individual essay 50% weighted (1500 words) researching current issues concerning the use of data within organisations
Artefact 50% weighted (1500 words) demonstrating the concepts developed in the research paper
Module Indicative Content
Basic concepts of data mining and warehousing
Enhancing the quality of data
Probability and statistics in data science
Data exploration and visualization
Data ingestion, cleaning and transformation
Introduction to machine learning
Introduction to supervised and unsupervised machine learning
Enhancing the quality of data
Probability and statistics in data science
Data exploration and visualization
Data ingestion, cleaning and transformation
Introduction to machine learning
Introduction to supervised and unsupervised machine learning
Module Learning Strategies
The module is essentially a practical one delivered via electronically distributed learning material.
Online material will be used to deliver the academic and technical content of the module. The tutorials will help you put the practical side of design, construction, testing and documentation into a computer science context and enable you to work on the assignment from week 1.
Online material will be used to deliver the academic and technical content of the module. The tutorials will help you put the practical side of design, construction, testing and documentation into a computer science context and enable you to work on the assignment from week 1.
Module Texts
Pierson, K (2015) Data Science for Dummies, EISBN-13 9781119327646
Module Resources
None
Module Special Admissions Requirements
Must have attended a Course Briefing Day and be approved by the Course Leader