DATA ANALYTICS - 2021/2
Module code: MANM301
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
Prior to registering online, you must read this general information and all relevant additional programme specific information. By completing online registration, you acknowledge that you have read such content, and accept all such changes.
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
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|
|Coursework||INDIVIDUAL COURSEWORK 2500 WORDS||50|
|Examination||2 HOUR EXAMINATION (CLOSED BOOK)||50|
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
Overall student workload
Independent Study Hours: 106
Lecture Hours: 22
Laboratory Hours: 22
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 2021/2 academic year.