Core Module Information
Module title: Data Analytics

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

Module code: SOE11154
Module leader: Matthew Smith
School The Business School
Subject area group: Management
Prerequisites

There are no pre-requisites for this module to be added

Description of module content:

The module aims at introducing students to the new possibilities opened up by the digital revolution and how these can be translated into the field of global logistics. You will be exposed to several data analytic techniques, including data cleaning, data visualisation, and dashboard development (in R) with a focus on application to global logistics and sustainability. More specifically the module will cover aspects such as:
(i) Introduction to Data Analytics: understanding the big data landscape; (ii) Data Processing; (iii) Data Visualisation: telling a story; (iv) Analytical Tools: Descriptive, Predictive, Prescriptive and Cognitive; (v) Simulation/Network Analysis; (vi) Practical Issues: Dashboard Development

Learning Outcomes for module:

LO1: Critically discuss relevant methods, tools, and techniques in data analytics in a business context.

LO2: Develop a critical understanding of the practical limitations of descriptive, predictive, prescriptive, and cognitive analytical tools.

LO3: Apply practical skills for data analytics in R.

LO4: Critically reflect on the contribution of data analytics for different industry sectors.

Full Details of Teaching and Assessment
2022/3, Trimester 1, FACE-TO-FACE,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: CRAIGLOCKHAR
Partner:
Member of staff responsible for delivering module: Matthew Smith
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
You will be provided with responsive, engaging and interactive learning materials which will include a general introduction to the topic and how to study the module, together with core academic theory relating to the topic.

These will outline the main topics to be considered in the course, introducing you to the main concepts in data analytics and developing an understanding of the tools available to apply data analytics in practice. You will also be directed to a variety of electronic sources including e-books, e-journals and other web-based resources, to support your learning. There are also a number of Open Moodle pages available to provide additional resources and support for data analytics and related topics, such as the Futures and Analytics Research (FAR) Hub, Sustainability Knowledge Hub, and the Social Network Analysis (SNA) Hub. Each unit will engage you in the learning process, enable you to develop the key issues further and encourage integration of reading material (LO1-4). The online materials will encourage you to reflect upon your experiences and learning. The module will allow you to develop hands on experience of using data analytics in R.

To support your learning, you will have access to module specific materials which will comprise of the following: A module introduction/overview, including learning outcomes, and summary of key learning points; eleven units of learning - an introductory/module overview unit, followed by ten subject specific unit. Lectures, supported by computer lab workshop activities will facilitate the development of key skills such as communication, reflective learning and critical thinking. The lab workshops will also involve a number of activities, where you work on developing data analytics and coding problems in a group.

Scholarship skills are developed through the research necessary for completion of the assessment together with adopting an appropriate writing style, referencing and the synthesis of information from a variety of sources.


Formative Assessment:
Formative assessment takes place throughout the weeks of attendance. This will involve completing worked example of coding activities in the computer labs, often in small groups. A weekly reflective workbook to be completed by students on a weekly basis, noting what they found most challenging about the worked example activity and how they overcame these challenges.

Summative Assessment:
Component 1, 50%:
2,000 word essay; LO 1, 2,
Identify a problem within a relevant topic that a firm may face and critically discuss how data analytics could be used to address this problem. The essay should include a discussion of issues relevant to data required, appropriate analytical tools and potential challenges.

Component 2, 50%:
30 minute Oral Presentation; LO 3, 4; 20%
Group presentation of a dashboard that students have developed collaboratively in R. Students will be assessed on the presentation content and the quality of the dashboard itself, along with peer assessment (peer-assessment weighting of 30% of this element of component 2).

1,000 reflective essay; LO 2, 4; 30%
Reflective essay based on oral presentation activity and assessment.



Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 18
Face To Face Practical classes and workshops 22
Independent Learning Guided independent study 160
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Essay 50 1, 2 8 , WORDS= 2000
Oral Presentation 20 3, 4 12 HOURS= 00.00
Essay 30 2,4 14/15 , WORDS= 1000
Component 1 subtotal: 50
Component 2 subtotal: 50
Module subtotal: 100

Indicative References and Reading List - URL:
Data Analytics