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Session: 2022/23
Last modified: 29/03/2022 09:06:28
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Title of Module: Data Analytics |
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Code: LNDN08003 |
SCQF Level: 8
(Scottish Credit and Qualifications Framework) |
Credit Points: 20 |
ECTS: 10
(European Credit Transfer Scheme) |
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| School: | School of Business & Creative Industries |
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| Module Co-ordinator: | Alloysius
Edbulonu |
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| Summary of Module |
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This module is designed to provide students with an introduction to the statistical principles used in data analytics and their application using a suitable statistical package. The module begins by considering how graphical summaries and numerical summaries, such as mean, median, standard deviation and correlation, can be used to describe and understand data. The issue of data handling is then considered. The basic concepts of inferential statistics are discussed and the use of methods for understanding the statistical importance of differences in means and proportions are described.
Syllabus: An Introduction to R – data import, data manipulation; introduction to data handling; basic graphical methods and numerical summaries; writing simple reports of a data analysis; basic concepts of statistics (populations and sampling); confidence intervals for means and proportions; testing for differences in means and proportions; p-values
Data analysis is an international language using internationally recognised techniques developed and refined by statisticians and analysts across the globe. Mastery of the subject-specific learning outcomes, 1 to 4, will equip students to apply the theories and techniques of this module in a wide range of international contexts.
In compiling the reading list, consideration has been given to the range of texts that are available internationally and a selection of texts has been identified to complement the delivery of the material.
Examples with an international dimension are included in the module where appropriate.
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| Module Delivery Method |
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| Face-To-Face | Blended | Fully Online | HybridC | HybridO | Work-based Learning |
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Face-To-Face
Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.
Blended
A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations
Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.
HybridC
Online with mandatory face-to-face learning on Campus
HybridO
Online with optional face-to-face learning on Campus
Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.
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| Term(s) for Module Delivery |
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(Provided viable student numbers permit).
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| Term 1 | | Term 2 | | Term 3 | |
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| Learning Outcomes: (maximum of 5 statements) |
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On successful completion of this module the student will be able to:
L1.
Demonstrate knowledge and critical understanding of the underlying concepts and principles of data analytic techniques;
L2.
Demonstrate the capability to use a range of established techniques and a reasonable level of skill in the use of basic graphical and numerical summaries of data, confidence intervals and testing for means and proportions;
L3.
Select and deploy the concepts and principles in the use of data analytics;
L4.
Make appropriate use of a statistical package, including basic graphical and numerical summaries of data, and testing for means and proportions. |
| Employability Skills and Personal Development Planning (PDP) Skills |
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| SCQF Headings |
During completion of this module, there will be an opportunity to achieve
core skills in:
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| Knowledge and Understanding (K and U) |
SCQF Level 8.
Gain knowledge and understanding in problems relating to quantitative and qualitative information. |
| Practice: Applied Knowledge and Understanding |
SCQF Level 8.
Apply knowledge to solve problems relating to quantitative and qualitative information. |
| Generic Cognitive skills |
SCQF Level 8.
Appreciate how data analytics contribute to the development of an organisation’s business strategy. |
| Communication, ICT and Numeracy Skills |
SCQF Level 8.
Make effective use of IT facilities for solving problems;
Communicate straightforward arguments and conclusions reasonably accurately and clearly |
| Autonomy, Accountability and Working with others |
SCQF Level 8.
Manage their own learning and development; |
| Pre-requisites: |
Before undertaking this module the student should have
undertaken the following:
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Module Code:
| Module Title:
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| Other: | |
| Co-requisites | Module Code:
| Module Title:
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* Indicates that module descriptor is not published.
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| Learning and Teaching |
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| In line with UWS’ Curriculum Framework, providing a flexible and hybrid, student-centred and inclusive approach to learning and teaching, the module has been designed around the delivery of engaging, activity- and discussion-based workshops, nurtured by meaningful online content, including short videos, reading materials, quizzes, etc. This approach creates more flexibility for students, while also enhancing deeper learning through engagement with peers and teaching staff, both online and in the classroom. This is further supported by the assessment approach adopted, enabling students to develop both academic and employability-focused knowledge and skills within international business — all aligned to the overarching purpose and aims of the programme. |
Learning Activities
During completion of this module, the learning activities undertaken to
achieve the module learning outcomes are stated below:
| Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities) |
| Lecture/Core Content Delivery | 36 |
| Independent Study | 164 |
| 200
Hours Total
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**Indicative Resources: (eg. Core text, journals, internet
access)
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The following materials form essential underpinning for the module content
and ultimately for the learning outcomes:
Zuur, A. (2009) A Beginner’s Guide to R, New York: Springer
Mann, P. (2017) Introductory Statistics, 9th Edition, Wiley.
Details of further resources, including textbooks, journals and online resources will be identified at the beginning of each delivery in the module handbook and made available via VLE
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(**N.B. Although reading lists should include current publications,
students are advised (particularly for material marked with an asterisk*) to
wait until the start of session for confirmation of the most up-to-date
material)
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| Engagement Requirements |
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In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure |
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Supplemental Information
| Programme Board | Marketing, Innovation, Tourism & Events |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | Marketing, Innovation, Tourism & Events |
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| Moderator | TBC |
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| External Examiner | TBC |
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| Accreditation Details | |
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| Version Number | 1 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| computer-based quizzes to assess competence in the use of R (40%) |
| Report (60%) |
(N.B. (i) Assessment Outcomes Grids for the module
(one for each component) can be found below which clearly demonstrate how the learning outcomes of the module
will be assessed.
(ii) An indicative schedule listing approximate times
within the academic calendar when assessment is likely to feature will be
provided within the Student Handbook.)
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Assessment Outcome Grids (Footnote A.)
Footnotes
A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above
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Note(s):
- More than one assessment method can be used to assess individual learning outcomes.
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Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.
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| Equality and Diversity |
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UWS Equality and Diversity Policy |
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(N.B. Every effort
will be made by the University to accommodate any equality and diversity issues
brought to the attention of the School)
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