Module title: Data Analytics

SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET11122
Module leader: Thomas Methven
School School of Computing
Subject area group: Software Engineering
Prerequisites

n/a

2018/9, Trimester 3, Blended,
Occurrence: 001
Primary mode of delivery: Blended
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Taoxin Peng
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
You will attend for one day per month during term time for intensive face-to-face lectures, workshops, tutorial and computer-based practical sessions (LO 1-4). This will be further supported by online material and discussion forums using a variety of communication technologies such as Moodle and Skype. You will be encouraged to develop your learning through peer and tutor interaction, either face to face or through electronic communication. Self-study readings supported by in-class and online discussions hosted through the VLE will develop skills as independent learners (LO 1-4). Formative feedback in online quizzes will encourage independent learning through data collection, analysis, synthesis, as well as skills in developing sound argument (LO 1-4). The lecture programme will be enhanced by material from guest speakers and will be made available online. The material for the lab-based practical sessions will be made available online with a support forum

Students will be expected to do self-oriented research and provide a critical analysis and evaluation of much of the theories behind these subjects throughout the semester, but particularly in preparation before the full day sessions (LOs 1, 2). 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 (all LOs). Practical materials are organised and selected for enhancing students’ understanding of the theories/principles covered. Practical exercises are expected to be completed in students’ own time after the full day sessions. Required software tools need to be downloaded on their own machine.


Formative Assessment:
Assessment is by means of a continuous assessment only covering all LOs. The assessment comprises two pieces. The first coursework requires students to apply data understanding and data cleaning techniques to a given dataset to understand and clean it (LOs 1 and 5). It is worth 20% of the module assessment. The deliverable will be a short report, including a description of the way it is done and a discussion of the data preparation, together with a set of cleaned data. The second coursework includes two elements. The first one requires students to use what they have learnt to apply relevant methods/techniques to a given dataset to find and examine interesting patterns (LOs 1-3). The second requires students to use what they have learnt to critically assess and evaluate their findings and the methods/techniques applied (LOs 3-5). The deliverable will be a report including the above two elements. This coursework is worth 80% of the module assessment.
Feedback on progress will be given during on-campus practical sessions. Also to support students’ self-learning, on-line surgeries will be provided, one hour per week.


Summative Assessment:
n/a

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 6
Face To Face Practical classes and workshops 15
Online Tutorial 12
Online Lecture 6
Online Practical classes and workshops 12
Independent Learning Guided independent study 109
Independent Learning Guided independent study 40
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Project - Practical 20 1,5 7 HOURS= 12, WORDS= 0
Project - Practical 80 1,2,3,4,5 15 HOURS= 28, WORDS= 0
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Description of module content:

The aim of this module is to enable you to develop a deep understanding of the fundamentals of data analytics, and to give you opportunities to practise a set of popular data analytical tools. Topics covered include:

*Data Pre-processing – data quality, data cleaning, data preparation
*Data Analytics – techniques of analysing data, such as classification, association, clustering and visualisation, including a variety of machine learning methods that are widely used in data mining

* Post processing – data visualisation, interpretation, evaluation

This module will use tools such as OpenRefine, Weka and Tableau for standard and structured data

The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools and practical skills in deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.


Learning Outcomes for module:

On completion of this module, students will be able to:
LO1: Critically understand the concepts and process of data analytics
LO2: Critically evaluate methods/techniques in data analytics
LO3: Apply data analytics algorithms to datasets to conduct data analysis and visualisation, by using data analytical tools
LO4: Critically interpret and evaluate results generated by analytical techniques
LO5: Investigate current research topics in data analytics

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