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Session: 2022/23
Last modified: 26/03/2021 16:38:21
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Title of Module: Quantitative Data Analysis |
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Code: BUSN08046 |
SCQF Level: 8
(Scottish Credit and Qualifications Framework) |
Credit Points: 10 |
ECTS: 5
(European Credit Transfer Scheme) |
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| School: | School of Business & Creative Industries |
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| Module Co-ordinator: | Peter
McGuire |
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| Summary of Module |
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This module provides a comprehensive but digestible introduction to data analysis with examples from realistic scenarios.this module is structured in three parts. The first part explores what data is, how data can be colected and the different types of measurement. In part two the practicalities of preparing data for analysis are explored and the role of computers in data analysis is explored. Part three focuses on the analysis of the data by exploring how to describe and characterise data and how to test a simple hypothesis.
- This modules aims to introduce data analysis to student assuming no prior knowledge.
- The module is structured in three parts to introduce the student to the analysis of a data and the process followed
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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 understanding of what data is, how can be collected and what effect the measurement process has on the validity of the data
L2.
Demonstrate knowledge and understanding of the practicalities of preparing data for analysis and presentation
L3.
Demonstrate knowledge and understanding of data analytical perspectives, namely description, estimation and hypothesis testing |
| 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.
Demonstrate knowledge and understanding of the process of data analysis |
| Practice: Applied Knowledge and Understanding |
SCQF Level 8.
Apply knowledge and skills in realistics examples where data analysis becomes usefull in business decision making scenarios |
| Generic Cognitive skills |
SCQF Level 8.
Demonstrate an understanding of how tools and techniques can be applied in data analysis to inform decision making |
| Communication, ICT and Numeracy Skills |
SCQF Level 8.
Demonstrate an understanding of data analysis computer packages
Use ICT in the analysis of data relating to simple realistic scenarios |
| Autonomy, Accountability and Working with others |
SCQF Level 8.
Demonstrate the ability to work independently |
| 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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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 | 12 |
| Tutorial/Synchronous Support Activity | 6 |
| Laboratory/Practical Demonstration/Workshop | 0 |
| Independent Study | 82 |
| 100
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:
Essential resources, including textbooks, journals and online resources will be identified at the beginning of each delivery in the module handbook and made available via Moodle.
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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 | Management, Organisations & People |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | Management, Organisations & People |
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| Moderator | Gary Gillon |
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| External Examiner | Z Salimi |
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| Accreditation Details | |
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| Version Number | 1.03 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| Coursework |
| On-line test |
(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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The Equality, Diversity and Human Rights policy underpins student engagement. We aim to make UWS a fair and equal place to study an institution which addresses specific issues covering all aspects of equality, diversity and human rights. Where required module assessment will be adapted to meet student requirements.
http://www.uws.ac.uk/equality/
UWS Equality and Diversity Policy
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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