module specification

CC5063 - Data Analytics (2022/23)

Module specification Module approved to run in 2022/23
Module title Data Analytics
Module level Intermediate (05)
Credit rating for module 15
School School of Computing and Digital Media
Total study hours 150
45 hours Scheduled learning & teaching activities
69 hours Guided independent study
36 hours Assessment Preparation / Delivery
Assessment components
Type Weighting Qualifying mark Description
Coursework 50%   Coursework apply data analytics knowledge and practical skills to solve real-world problems (2500 words report + a data
Unseen Examination 50%   2-hour unseen exam
Running in 2022/23

(Please note that module timeslots are subject to change)
Period Campus Day Time Module Leader
Autumn semester North Tuesday Morning

Module summary

This module introduces fundamental concepts and techniques of data analytics. The module covers descriptive statistics for exploratory data analysis, correlation analysis and linear regression model.  A substantial practical element is integrated into the module to enable students to apply data analytics techniques for real world data analytical problems.

The aims of this module are to enable students to:
• gain a thorough understanding of fundamental concepts of data analytics
• acquire knowledge of descriptive statistics, correlation analysis,  and linear regression  analysis
• have knowledge of and gain understanding of the data analytics lifecycle
• develop practical data analytical skills to resolve real world data analytical problems


• Fundamental concepts in data analytics
- Theories and hypotheses
- Population and samples
- Relationships and causality
- Data sets
- Reliability and validity
• Numeric and categorical variable analysis and visualisation with descriptive statistics
• Examining relationships between interval data with correlation analysis
• Modelling relationships of multiple variables with linear regression
• Data analytics lifecycle
• Data analytics case study

Learning OUtcomes LO1 - LO4

Balance of independent study and scheduled teaching activity

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:

- Lecture (1 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies

- Workshop (2 hour / week):
Practical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills with powerful interactive data analysis package.

- Blended learning:
Using the University’s VLE and online tools to provide deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

Students will be expected and encouraged to produce reflective commentaries on the learning activities and tasks that they carry out to complete their work.

Learning outcomes

On successful completion of this module the student should be able to:
[LO1]  demonstrate a clear understanding of the key fundamental concepts of data analytics
[LO2]  gain good knowledge and proficiency in data analytics with descriptive statistics, correlation analysis, and linear regression analysis
[LO3]  gain a clear understanding of the lifecycle of data analytics

[LO4]  apply data analytics knowledge and practical skills to solve unstructured/semi-structured real-world problems

Assessment strategy

The module will be assessed by a practical piece of coursework (50%) and a 2-hour unseen examination (50%).

The coursework is designed mainly to assess the practical aspects of the module. It will provide students with the opportunity to undertake research on current issues and practical techniques in data analytics and its effective application for real world data analytical problem. It will also enable students to apply their knowledge to a practical business problem, demonstrating their skills for problem-solving and critical thinking/evaluation [LO1, LO2, LO3, LO4].

The unseen examination will provide an opportunity for students to demonstrate their understanding of data analysis concepts and techniques and their ability to apply these techniques appropriately to the solution of given problems/scenarios [LO1, LO2, LO3]. The examination will test the students' retention, understanding and insight of material drawn from the module.


Reading list available at:

1. EMC Education Services (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 1st Edition, Wiley. [CORE]
2. Stephen A. Sweet and Karen A. Grace-Martin (2010) Data Analysis with SPSS: A First Course in Applied Statistics. 4th Edition, Pearson. 
3. Roxy Peck, Chris Olsen, I Jay L. Devore (2011) Introduction to Statistics and Data Analysis 4th Edition. Brooks/Cole.
4. Mohammed J. Zaki, Wagner Meira Jr. (2014) Data Mining and Data Analysis. Cambridge University Press.

Online resources
1. Data Mining Community -
2. London Datastore -
3. AnalyticBridge:A Data Science Central Community -