Academic Handbook BSc (Hons) Applied Digital and Technology Solutions (online)
NCHNAL6123 Data Driven Decision Making Course Descriptor
Last modified on March 7th, 2023 at 3:59 pm
| Course Title | Data Driven Decision Making | Faculty | EDGE, Innovation Unit (London) |
| Course code | NCHNAL6123 | Teaching Period | This course will typically be delivered over a 12-week period. |
| Credit points | 30 | Date approved | March 2021 |
| FHEQ level | 6 | ||
| Compulsory/ Optional |
Compulsory for Data Analyst Specialism | ||
| Pre-requisites | None | ||
| Co-requisites | None | ||
Course Summary
This course is designed to provide an in-depth focus on data-driven decision making in organisations. It examines the models, tools, techniques, and theory of data-driven decision making that can improve the quality of business leadership decisions through solution-based case studies.
Course Aims
- Expose students to the theory of data-driven decision making.
- Introduce students to communicating effectively using data with scenario based assignments.
- Focus on the role of leadership in decision making.
- Encourage students to improve their presentation skills in a professional setting.
Learning Outcomes
On successful completion of the course, students will be able to:
Knowledge and Understanding
| K1c | Examine the models, tools, techniques, and theory of data-driven decision making that can improve the quality of decision making. |
| K2c | Practice building mental models of what data, analyses, and decision making would look like in specific business settings, based on case studies and other course material. |
| K3c | Define appropriate business objectives and questions, research and articulate findings, translate the findings into information and insight. |
Subject Specific Skills
| S1c | Discuss the challenges and potential risks inherent in evidence-based analytics and develop critical thinking skills around them. |
| S2c | Develop understanding of clear definitions of metrics and appropriate KPIs. |
Transferable and Professional Skills
| T1ci | Design and deliver presentations, reports, and recommendations that effectively translate technical results/data solutions and are coherent and persuasive to different audiences. |
| T1cii | Display an advanced level of technical proficiency in written English and competence in applying scholarly terminology, so as to be able to apply skills in critical evaluation, analysis and judgement effectively in a diverse range of contexts. |
| T2c | Use and communicate with evidence-based reasoning. |
Teaching and Learning
This is an e-learning course, taught throughout the year.
This course can be offered as a stand alone short course.
Teaching and learning strategies for this course will include:
- On-line learning
- On-line discussion groups
- On-line assessment
Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).
Students are required to attend and participate in all the formal and timetabled sessions for this course. Students are also expected to manage their self-directed learning and independent study in support of the course.
The course learning and teaching hours will be structured as follows:
- Learning and teaching (12 days x 8 hours) = 96 hours
- Independent study = 204 hours
Indicative total learning hours for this course: 300 hours
Assignments (see below) will be completed as part of private study.
Assessment
Formative
Students will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide students with developmental feedback.
Summative
| AE | Assessment Type | Weighting | Online submission | Duration | Length |
| 1 | Report | 70% | Yes | – | 4,000 words +/- 10%, excluding data tables |
| 2 | Written Assignment | 30% | Yes | – | 1,500 words +/- 10%, excluding data tables |
All summative assessments will be assessed in accordance with the assessment aims set out in the programme specification.
Feedback
Students will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Students will also attend a formal meeting with their Mentor. These reviews will monitor and evaluate the learner’s progress.
Feedback is provided on summative assessment and is made available to the student either via email, the VLE or another appropriate method.
Indicative Reading
Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.
Books
- Bartlett, R., (2013), A Practitioner’s Guide to Business Analytics, McGraw-Hill Education
Journals
Students are encouraged to consult relevant journals on data driven decision making.
Electronic Resources
Students are encouraged to consult relevant electronic resources on data driven decision making.
Indicative Topics
- Decision Making
- Defining Metrics
- Leadership
| Title: NCHNAL6123 Data Driven Decision Making
Approved by: Academic Board Location: Academic Handbook/Programme specifications and Handbooks/ Undergraduate Online Programmes/Applied BSc (Hons) Digital & Technology Solutions/Course Descriptors |
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| Version number | Date approved | Date published | Owner | Proposed next review date | Modification (As per AQF4) & category number |
| 3.0 | December 2022 | January 2023 | Dr Yu-Chun Pan | June 2026 | Category 3: Change to Teaching and Learning Strategy; Change to English Proficiency Learning Outcome
Category 1: Corrections/clarifications to documents which do not change approved content or learning outcomes |
| 2.1 | August 2022 | August 2022 | Scott Wildman | June 2026 | Category 1: Corrections/clarifications to documents which do not change approved content or learning outcomes |
| 2.0 | January 2022 | April 2022 | Scott Wildman | June 2026 | Category 3: Changes to Learning Outcomes |
| 1.0 | March 2021 | – | Scott Wildman | March 2026 | |