DATA ANALYTICS - 2022/3
Module code: MANM301
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
This module is the science of examining raw data in order to support businesses and organisations in their decision making. On one hand this module looks at relationships of entities in databases using the Structured Query Language to extract relevant information efficiently. On the other hand it introduces unstructured data concepts. Special focus is given to Big Data providing knowledge, analysis and practical skills to gain additional business and customer insights. Fundamental statistical techniques to extract the essential management information are shown. This module is the foundation for further investigations and a cornerstone of the programme.
Surrey Business School
GROVER Vikas (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G300
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 95
Lecture Hours: 22
Laboratory Hours: 20
Guided Learning: 2
Captured Content: 11
Prerequisites / Co-requisites
The module will focus on a selected set of areas in Big Data. Please find below an indicative set of topics:
- Big Data (Hadoop/MapReduce)
- Unstructured data – social media information
- Database design and ER
- Statistical methods for data analysis
- Data visualisation
- SQL, OLAP
|Assessment type||Unit of assessment||Weighting|
The assessment strategy is designed to provide students with the opportunity to demonstrate
In order to achieve the threshold standard for the award of credits for this module, the student must meet the following criteria (related to the learning outcomes):
analysis and manipulate data;
improve the quality of Big Data;
present knowledge in a suitable format for the target audience;
demonstrate evidence of background reading and research of the academic and practitioner literature relevant to big data and data analysis.
Thus, the summative assessment for this module consists of:
Two hours unseen exam paper
One piece of project based coursework
Formative assessment and feedback
Formative assessment will be provided during computer laboratory sessions.
- Explore data analytics tools to extract knowledge from datasets
- Analyse, manipulate, and visualise data using state-of-the-art techniques
- Explore data mining techniques to extract knowledge from datasets
|001||Demonstrate the knowledge discovery process||KP|
|002||Analyse and visualise data using a methodical analytical approach||KP|
|003||Familiarity to be able to apply important data mining algorithms and techniques||CPT|
|004||Apply state-of-the-art methods and tools to build classification and predictive models||CPT|
|005||Demonstrate the ability to communicate and provide resulting information to the management for decision making||KCPT|
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:
The teaching and learning strategy is designed to: cultivate an understanding of the complex process of big data analysis and understanding of methodical approach of data analysis.
This module is delivered as a programme of lectures and lab classes. Web-based learning support and electronic resources will be provided.
The learning and teaching methods include:
2 hour lecture per week x 11 weeks
2 hour labs per week x 11 weeks
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.
Upon accessing the reading list, please search for the module using the module code: MANM301
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 2022/3 academic year.