| Unit name | Programming in Business Analytics |
|---|---|
| Unit code | MGRCM0034 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Zhang |
| 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?
In traditional business analytics, Excel and other software are certainly convenient and often efficient for data analysis and visualisation. However, numerous limitations have been becoming more and more clear in recent years. Data volume and complexity grow exponentially, which calls for more sophisticated data processing tools such as programming languages. The most two popular of them, Python and R, will be taught in this unit.
How does this unit fit into your programme of study
This unit will introduce students to fundamental programming principles and concepts for business analytics. Students will use and combine them with the business knowledge from other units (such as Data Analytics in Business, and Modelling Analytics) to support them in solving business problems more efficiently.
An overview of content
This unit teaches students the fundamentals of programming languages in business analytics. Both Python and R will be introduced. Students will learn the basics, structure, functions, modules, simple case studies and applications of them to equip the ability to handle practice business problems.
How will students, personally, be different as a result of the unit
On completion of this unit, students will realise that knowledge of fundamental programming principles and concepts within the context of creating solutions for business analytics. They will be equipped with a working knowledge of business analytics tools to support descriptive analytics, predictive analytics and prescriptive analytics.
Learning Outcomes
ILO 1 – Understand the role of programming in Business Analytics
ILO 2 – Process data by using Python and R
ILO 3 – Write, test and debug procedural and functional programmes in Python and R
ILO 4 – Apply programming skills to solve simple real business problems
The unit will be delivered through a series of exercise lectures with computers (3 hours*10 weeks). Blackboard will be used to engage students with the unit content.
Tasks which help you learn and prepare you for summative tasks (formative):
Feedback to students is provided throughout the unit in the form of formative quizzes, feedback online and in the classroom from tutors in response to student discussions, questions, and ongoing work.
Tasks which count towards your unit mark (summative):
The summative assessment for this unit has both group (50% of the overall unit mark) and individual (50% of the overall unit mark) assessments.
Group (50%) (all ILOs covered)
Students will be working in groups to collect, prepare and analyse real data concerning business by using Python or R. They are expected to apply knowledge and skills learnt through this unit to support businesses in exploring problems and making operation strategies. Each group will submit one 2000-word project report and the original coding file.
Individual (50%) (all ILOs covered)
Individual assessment has elements.
Students are expected to summarise a 500-word individual reflection (20% of the overall unit mark) on the above group project. They are expected to discuss challenges and the ways to overcome them, as well as reflect on what they learned during the project.
Students are also expected to document a 1000-word report (30% of the overall unit mark) to describe how to use another programming language (Python or R, which is not applied in the group project) to solve the group project.
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 group component will be replaced by a task that involves individual reflection aligning with all ILOs (1-4), limited to 1,000 words (50%). This reflection task requires students to explain their understanding on the differences of applying Python and R in the new re-assessment dataset, as well as discuss both the opportunities and challenges they encountered in their original group assignment.
The second component entails individual coursework limited to 1,500 words (50%). One new real business dataset will be provided to students, and they are asked to analyse it using any programming language taught in this unit. Then, they need to document one report to show how they prepare, explore and analyse the data to support businesses in turning data into assets. This assessment will comprehensively address all the methodologies that have been extensively covered throughout the semester, aligning with all ILOs (1-4).
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. MGRCM0034).
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 assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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.
