| Unit name | Data Analytics in Business |
|---|---|
| Unit code | EFIMM0141 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Essien |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
This unit provides a comprehensive introduction to business data analytics, covering fundamental concepts and methodologies from basic to advanced levels. It emphasises practical application in business contexts, addressing big data, descriptive, predictive, and prescriptive analytics, as well as data mining and machine learning techniques. Students learn statistical and machine learning methods such as regression, classification, and clustering for effective data exploration, interpretation, and forecasting. The focus is on developing a deep conceptual understanding of business analytics principles, laying a technical foundation for utilising advanced analytics tools. By the end, students are equipped to recognise and leverage opportunities in the dynamic field of business analytics.
How does this unit fit into your programme of study
This unit is a crucial part of your study program, bridging foundational knowledge and advanced application in the field of business analytics. In your MSc Business Analytics programme, it serves as a core unit, enabling you develop key disciplinary competencies and technical know-how. It offers depth and context to the core curriculum and enhances interdisciplinary understanding. The unit provision of a rich blend of conceptual theories and practical analytics tools ensures its relevance across various future careers, preparing you for diverse analytical challenges in your analytical journey. As a foundational module, it is indispensable for students seeking to gain a deeper understanding of the rapidly evolving domain of business data analytics.
An overview of content
This unit introduces students to basic, intermediate and advanced business data analytics concepts, blending theory with practical application. The unit provides a rich combination of analytics concepts and methodologies including descriptive, prescriptive and predictive analytics. Key topics include data analytics techniques in Python, algorithm selection, and data visualisation strategies. Emphasis is placed on a holistic understanding of business analytics, backed by real-world case studies and interactive workshops, to foster a deep conceptual grasp of the subject and its practical utility in business environments.
How students will be personally different as a result of the unit
Upon completing this unit, students are expected to develop enhanced cognitive and practical skills. They should be able to demonstrate a robust understanding of data analytics, fostering critical thinking and analytical proficiency. Students will be able to apply advanced data analytics techniques to real-world scenarios, significantly boosting their problem-solving capabilities. This transformative learning experience will refine their approach to data-driven decision-making, preparing them for dynamic business environments and instilling confidence in their professional competencies.
Learning outcomes
On completion of this unit, students will be able to:
ILO 1: Discuss the concepts and methods of data analytics using relevant and appropriate terminologies.
ILO 2: Design a data analytics project with a critical assessment of the data mining process and techniques involved in collecting, managing and modelling actionable data.
ILO 3: Use a range of descriptive analytics techniques to discover, visualise and interpret patterns in a large amount of data.
ILO 4: Apply predictive analytics to predict future outcomes and model scenarios to address a range of business problems.
ILO 5: Evaluate and communicate insights derived from data to a critical audience and make them effective in actual business decision-making.
The unit will be taught in 10*3-hour lectorial sessions. The class will be highly interactive with analytical exercises, guest talks, discussions on case studies and other activities. Students will be directed to a wide range of academic papers and industrial reports to collect the latest information about data analytics techniques and practices. In practical sessions, students will be offered hands-on analytical exercises. Web-based learning support and electronic resources will be provided.
Tasks which help you learn and prepare you for summative tasks (formative):
Tasks which count towards your unit mark (summative):
Group Assignment (40% of the overall unit mark):
The group assignment has two equal parts: a report and a presentation. It covers all learning outcomes (ILO 1, 2, 3, 4, & 5). Teams work on a fictional business's data analytics project. Each team member takes on a different role (Data Scientist, Business Strategist, Data Engineer, and Visualization Expert). Together, they design and execute the project, demonstrating their understanding of data mining techniques. Each team submits a 1,500-word report resembling a management consultancy report, describing the project's approach, challenges, group reflection and results. Afterward, teams present their findings and recommendations in a 10-minute presentation, mimicking a real-world boardroom scenario.
Individual Role-based Assignment (60% of the overall unit mark):
Derived from the group project, each student will write a 2,000-word essay from the perspective of their assigned role in the group project, integrating academic research. It covers learning outcomes ILO 1, 2, 3 & 4. This essay will detail the importance of their role in the project, challenges faced, methodologies used, and insights from relevant academic literature. The below roles are assigned:
When assessment does not go to plan
When a student fails the unit and is eligible to resubmit, failed components will be reassessed on a like-for-like basis. The resit assessment consists of two components.
Firstly, a 60% individual report (1,500 words) based on a data analytics project, utilizing a dataset and scenario provided by the instructor. It is a re-assessment of the original individual role-based assignment. [ILOs 1, 2, 3 and 4].
Secondly, there is a 40% individual reflective report (500 words). This component serves as a re-assessment of the original group assignment. [ILO 5].
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EFIMM0141).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
