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
Subject area group: Software Engineering

Module Code SET07106, INF08104
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

2019/0, Trimester 1, Face-to-Face,
Occurrence: 001
Primary mode of delivery: Face-to-Face
Location of delivery: MERCHISTON
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 a formal examination and one set of practical coursework. Fundamentals and theories will be tested in the exam (LOs 1, 2, 3), while the practical skills will be assessed by the coursework (LOs 3, 4, 5).

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Tutorial 6
Face To Face Practical classes and workshops 18
Independent Learning Guided independent study 150
Face To Face Centrally Time Tabled Examination 2
Face To Face Lecture 24
Total Study Hours200
Expected Total Study Hours for Module200

Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Practical Skills Assessment 060.00 3, 4, 5 12 HOURS= 040.00, WORDS= 000.00
Centrally Time Tabled Digital Examination 040.00 1, 2, 3 14/15 HOURS= 002.00, WORDS= 000.00
Component 1 subtotal: 60
Component 2 subtotal: 40
Module subtotal: 100

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
Web Analytics
Tools used in this module include Weka, OpenRefine, Tableau. 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: Understand technologies in web analytics
LO4: Apply data analysis algorithms to datasets to conduct data analysis and visualisation
LO5: Critically interpret and evaluate results generated by analysis techniques

Indicative References and Reading List - URL:

Please contact your Module Leader for details
Click here to view the LibrarySearch.