Modules

Module title: Data Analytics
SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SET11822
Module leader: Thomas Methven
School School of Computing
Subject area group: Software Engineering
Prerequisites

n/a
2017/8, Trimester 3, Online,
Occurrence: 001
Primary mode of delivery: Online
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Taoxin Peng
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
This online module will develop you as an independent learner. The online materials have been constructed in a way which will facilitate a structured order to your learning process.
The learning and teaching strategy of the programme ensures your development as a confident individual with high quality skills and attributes that are recognised and valued by you, employers and the wider community. This is achieved through:
• developing you as an independent learner;
• utilising formative and summative assessments, and formative feedback;
• placing learning and subject content within an international context where appropriate; and
• making use of relevant learning technologies.
The online module presence has been designed to provide a clear roadmap for students to work through the learning materials. Module specific materials designed to support your studies are detailed in the module descriptors. This include items such as:
• A module introduction/overview, including learning outcomes, and summary of key learning points.
• Units of Learning materials, (subject specific units).
• Recorded keynote lectures/guest speakers (where appropriate).
• Self-Assessment Questions.
• Reflective Exercises.
• Links to core module academic materials; book chapters / journal articles / case materials etc.
• Additional readings – electronic links to journal articles, chapters etc.
In addition to this, online students are provided with online support in the form of:
• Dedicated online administrators who will keep track of student progress and will help you if you are having any problems.
• A dedicated interactive database of frequently asked questions specific to the online learning environment.
• A regular ‘virtual office hour’ will be held where module staff will be available for contact with you.
You will be supported by the Global Online team who will provide general overall support, and by the module teams who will provide module-specific online material and discussion forums using a variety of communication technologies such as Moodle and Skype (LO 1-5). You will be encouraged to develop your learning through peer and tutor interaction via electronic communication. Self-study readings supported by in-class and online discussions hosted through the VLE will develop skills as independent learners (LO 1-5). Formative feedback in online quizzes will encourage independent (LO 1-5). The online lecture material will be enhanced by material from guest speakers and will be made available online. The material for the lab-based practical sessions (LO 1-5) 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. 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)
Online Lecture 12
Online Practical classes and workshops 24
Online Tutorial 15
Independent Learning Guided independent study 119
Online Guided independent study 40
Total Study Hours210
Expected Total Study Hours for Module210


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:

Core - M. R. BERTHOLD, C. BORGELT, F. HOPPNER AND F. KLAWONN (2010) GUIDE TO INTELLIGENT DATA ANALYSIS: SPRINGER-VERLAG, 1st ed.
Core - I. WITTEN AND E. FRANK (2011) DATA MINING: PRACTICAL MACHINE LEARNING TOOLS AND TECHNIQUES: MORGAN KAUFMANN, 3rd ed.
Core - T. MUNZNER (2014) VISUALIZATION ANALYSIS AND DESIGN: CRC PRESS, 1st ed.
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