Core Module Information
Module title: Data Analytics

SCQF level: 09:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET09120
Module leader: Taoxin Peng
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

Module Code SET07106, SET08120
Module Title Mathematics for Software Engineering, Database Systems
Examples of Equivalent Learning A mathematics course covering algebra and statistics
A database systems course covering relational modelling

Description of module content:

Data Preparation – Data collection, feature generation and data selection.
Data Pre-processing – data quality, data cleaning, data integration
Data Analysis – techniques of analysing data, such as correlation, regression, forecasting, classification, clustering, including a variety of machine learning methods that are widely used in data mining
Post processing – data visualisation, interpretation, evaluation
Tools used in this module include Weka, OpenRefine, or R.


Learning Outcomes for module:

Upon completion of this module you will be able to
LO1: Understand the concepts and process of data analysis
LO2: Understand and critically evaluate modelling methods/techniques in Data Analysis
LO3: Demonstrate understanding of visualisation and psychology of vision fundamentals
LO4: Apply data analysis algorithms to datasets to conduct data analysis
LO5: Critically interpret and evaluate results generated by analysis techniques

Full Details of Teaching and Assessment
2022/3, Trimester 1, FACE-TO-FACE, Edinburgh Napier University
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MERCHISTON
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Taoxin Peng
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
Students will be expected to do self-oriented research and provide a critical analysis and evaluation of much of the theories behind these subjects (LOs 1, 2, 3). Teaching will concentrate on the critical analysis of that information and on practical exercises (LOs 3-5). Students are expected to spend a large proportion of their time doing comprehensive reading and practice. Tutorial and practical materials are well organised and selected for enhancing students’ understanding of the theories/principles covered.

Formative Assessment:
To support formative feedback, the Software Engineering subject group utilise a lab based teaching approach across their provision. During these lab sessions, staff will discuss and evaluate student progress and provide feedback on how well they are progressing with their work. All modules in the subject group also require students to demonstrate their coursework on submission to provide further formative feedback on how the work could be improved.

Summative Assessment:
Assessment will comprise two independent components, practical coursework 1 and practical coursework 2.. Fundamentals and visualization practical skills will be tested in component 1 (LOs 1, 3, 5), while fundamentals and practical skills in data mining will be assessed by the component 2 (LOs 1, 2, 4, 5).

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 24
Face To Face Tutorial 6
Face To Face Practical classes and workshops 18
Independent Learning Guided independent study 152
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Practical Skills Assessment 35 1, 3, 5 7 HOURS= 20.00, WORDS= 000.00
Practical Skills Assessment 65 1, 2, 4, 5 13 HOURS= 40.00, WORDS= 000.00
Component 1 subtotal: 35
Component 2 subtotal: 65
Module subtotal: 100

Indicative References and Reading List - URL:
Contact your module leader