Modules

Core Module Information
Module title: Data Analytics
SCQF level: 09:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SET09420
Module leader: Taoxin Peng
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

N/A
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.
This module will teach a mixture of state of the art scripting, visualisation, data mining and cleaning tools, such as R or Python, OpenRefine and Weka.
Learning Outcomes for module:

LO1: Critically reflect on the concepts and process of data analysis
LO2: Critically evaluate modelling methods/techniques in data analytics
LO3: Apply data analysis algorithms to datasets to conduct data analysis and visualisation
LO4: 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:
The student must be employed as a Graduate Level Apprentice to complete this module. The students taking this module are primarily based in and around Edinburgh. The delivery is monthly day release, face to face. For a module this equates to 2 hours per week of lectures, supported by online materials.
This module adopts a blended approach within formative laboratory-based practicals, discussion tutorials and seminars, and lectures. Practical instruction is supported with virtual learning environment (VLE) resources aimed at reinforcing some of the principles discussed in lectures and tutorials, with further directed study also provided.
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). Teaching will concentrate on the critical analysis of that information and on practical exercises (LOs 3, 4). 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 GA students will come in for a day per month, to ensure they have direct contact with the module leader and their peers. During these day sessions, staff will discuss and evaluate student progress and provide feedback on how well they are progressing with their work. This will be through a mixture of taught materials, tutorial sessions, and lab work where appropriate.

Summative Assessment:
Assessment will comprise two independent components, practical coursework 1 and practical coursework 2. The two pieces of coursework will be designed to ensure that students can reflect on how the tools and techniques taught in this module could be used within their work place. Fundamentals and visualization practical skills will be tested in component 1 (LOs 1, 3, 4), while fundamentals and practical skills in data mining will be assessed by the component 2 (LOs 1, 2, 3, 4).

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, 3,4 7 HOURS= 20.00, WORDS= 000.00
Practical Skills Assessment 65 1, 2, 3, 4 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
 

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