PRACTICAL BUSINESS ANALYTICS - 2020/1
Module code: COMM053
Module Overview
In today’s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data.
Module provider
Computer Science
Module Leader
TAMADDONI NEZHAD Alireza (Computer Sci)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 8
Lecture Hours: 20
Laboratory Hours: 18
Module Availability
Semester 1
Prerequisites / Co-requisites
None
Module content
Indicative content includes:
- Business Data Science, Big Data and Business Analytics
- Business Intelligence (BI), Decision Support Systems (DSS) and Data Warehouses (DW)
- Data Mining Life Cycle & Cross Industry Standard Process for Data Mining (CRISP-DM)
- Data Preparation
- Introduction to Machine Learning Tasks
- Association Rule Mining
- Clustering Algorithms
- Decision Tree Learning
- Regression
- Model Evaluation
- Visualisation
- Project Management
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | COURSEWORK I (GROUP) | 50 |
| Examination | 2 HOUR EXAM | 50 |
Alternative Assessment
Coursework I (group) - Implement a Business Analytics Solution based on a case study
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module’s intended learning outcomes described above.
Thus, the summative assessment for this module consists of:
- A group project based on a case study. The students will need to analyse the business problem, determine the business objectives, relevant performance metrics to measure them and provide the business requirements for a business analytics solution and then implement and evaluate it. This will address LO1 to LO5 .
- An unseen 2 hours exam addressing LO1 to LO3.
Formative assessment and feedback
Self-assessment lab exercises will be provided in SurreyLearn. Feedback is also given during the class discussions and lab sessions and as part of the feedback provided for the group project.
Module aims
- The aim of this module is to introduce students to Business Analytics from a practical point of view.
- Students will also learn about related concepts such as Data Mining Life Cycle, Machine Learning Algorithms, Model Evaluation and Data Visualisation.
- Students will learn about applications of Business Analytics through case studies and practical examples in lab sessions and coursework.
- Students will be using industry standard tools such as SPSS Modeller and Watson Analytics.
Learning outcomes
| Attributes Developed | ||
| 001 | Analyse business objectives and the choice of performance metrics to measure them and translate these into Key Performance Indicators | KCP |
| 002 | Understand and describe different data mining techniques (e.g. classification, clustering, regression, etc) and how these can be applied to different real-world problems | KT |
| 003 | Analyse a given business problem and provide a well-reasoned rationale for the choice of tools and techniques | KCPT |
| 004 | Implement and evaluate a business analytics solution for a given scenario and justify the approach | KCPT |
| 005 | Appreciate the importance of team-work when carrying out the above activities | PT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
help students achieve the intended learning outcomes of the module through in-class discussions and hands-on exercises in the lab sessions and via the coursework.
The learning and teaching methods include:
- 20 hours of lectures with class discussion
- 18 hours of lab classes
- 8 hours of workshop and presentations
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COMM053
Programmes this module appears in
| Programme | Semester | Classification | Qualifying conditions |
|---|---|---|---|
| Data Science MSc | 1 | Compulsory | A weighted aggregate mark of 50% is required to pass the module |
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.