Study information

Data Analysis 2 - 2025 entry

MODULE TITLEData Analysis 2 CREDIT VALUE15
MODULE CODEECM3441DA MODULE CONVENERDr Pikakshi Manchanda (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 12
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content
The primary role of a data analyst is to collect, organise, and study data to provide new business insights. You are responsible for providing up-to-date, accurate and relevant data analysis for the organisation. You are typically involved with managing, cleansing, abstracting and aggregating data across the network infrastructure. You will have a good understanding of data structures, software development procedures and the range of analytical tools used to undertake a wide range of standard and custom analytical studies, providing data solutions to a range of business issues. You will document and report the results of data analysis activities making recommendations to improve business performance. You need a broad grounding in technology solutions to be effective in your role.
 
Pre-requisite ECM3433DA Data Analysis 1
AIMS - intentions of the module
The aim of this module is to extend your skills in data analysis, encompassing more advanced statistical and modelling techniques to derive insights from large and small datasets, ways of communicating results to stakeholders, and practical knowledge of data quality and control issues.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Defining data requirements and perform data collection, cleansing, transforming, and data validation with the purpose of understanding or making conclusions from the data for business decision making purposes.
2. Find, present, communicate and disseminate data analysis outputs effectively and with high impact through creative storytelling, and tailoring the message for the audience. 
3. Visualise data to tell compelling and actionable narratives by using the best medium for each audience, such as charts, graphs and dashboards.
4. Perform routine statistical analyses and ad-hoc queries.
5. Use a range of analytical techniques such as data mining,  and modelling techniques to identify and predict trends and patterns in data.
6. Report on conclusions gained from analysing data using a range of statistical software tools.
7. Summarise and present results to a range of stakeholders making recommendations.
8. Analyse in detail large data sets using a range of industry standard tools and data analysis methods to derive inferences.
9. Interpret and apply the organisations data and information security standards, policies and procedures to data management activities.

Discipline Specific Skills and Knowledge

10. Understand the quality issues that can arise with data and how to avoid and/or resolve these.
11. Demonstrate an understanding of the processes involved in carrying out data analysis projects.
12. Understand how to critically analyse, interpret, and evaluate complex information from diverse datasets.  
13. Understand how data and analysis may exhibit biases and prejudice, how ethics and compliance affect data analytics work, and the impact of international regulations such as General Data Protection Regulation and the Data Protection Act 2018.
14. Recognise the importance of data governance, data security, and communications when working with data analytics methodologies.
15. Demonstrate understanding of how to use a range of appropriate data analysis techniques or processes.
16. Recognise the barriers that exist between data analysts and stakeholders to perform data analysis and how to avoid or resolve such barriers.
17. Extract and work with different kinds of data sources such as files, operational systems, databases, web services, open data, government data, news and social media data.
18. Recognise the importance of the domain context for data analytics.
19. Recognise the barriers that exist between data analysts and stakeholders to perform data analysis and how to avoid or resolve such barriers.

Personal and Key Transferable / Employment Skills and Knowledge

20. Communicate orally and in writing
21. Solve problems creatively
22. Think analytically and critically
23. Organise your own work
24. Work to a deadline
25. Make decisions
26. Commit to continuous professional development
SYLLABUS PLAN - summary of the structure and academic content of the module

Data storage (2 weeks)

•          NoSQL databases e.g., Hadoop; MongoDB
•          Unstructured data

Analysing data to derive inferences and to identify and predict trends and patterns (6 weeks)

•          Advanced statistical techniques
•          Machine learning and cognitive computing
•          Modelling techniques
•          Network analysis
•          Analysing large datasets
•          Advanced use of data analysis tools
•          Data visualisation; tables, charts and graphs; more sophisticated visualisation tools 

Communicating results (2 weeks)

•          Reporting on conclusions gained from analysing data
•          Summarise and present results to a range of stakeholders
•          Making recommendations

Quality and controls (2 weeks)

•          Quality issues that can arise with data; how to avoid and/or resolve
•          Security; applying the organisation’s data and information security standards, policies and procedures
•          Data protection
•          Other legal issues

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 22 Guided Independent Study 128 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 18 Online learning activity, including virtual workshops, synchronous and asynchronous virtual lectures and other e-learning.
Scheduled learning and teaching activities 2 Lectures
Scheduled learning and teaching activities 2 Group workshops
Guided independent study 128 Coursework, exam preparation and self-study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Contribution to class discussion N/A All Oral
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 60 Written Exams 40 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Modelling and visualisation exercise 60 3,000 words All Written
Written exam 40 2 hours 1, 5-6, 9-25 Written
         
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Modelling and visualisation exercise Modelling and visualisation exercise (60%) All Completed over summer with a deadline in August
Written exam Written exam (40%) 1, 4-6, 9-25 Referral/deferral period
       

 

RE-ASSESSMENT NOTES
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be deferred in the assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
 
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you may be required to sit a referral. The mark given for a re-assessment taken as a result of referral will be capped at 40%.
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

  • Witten, I. H., Frank, E., Hall, M. A., Data Mining: Practical Machine Learning Tools and Techniques, 3rd, Morgan Kaufmann, 2011, 978-0123748560.
  • Few S, Now You See it: Simple Visualization Techniques for Quantitative Analysis, 1st, Analytics Press, 2009, 978-0970601988.
  • Page, Scott E., The Model Thinker: What You Need to Know to Make Data Work for You, 2nd, Basic Books, 2019, 978-0465094622.
  • Luciano Ramalho, Fluent Python, 1st, O'Reilly Media, 2015, 978-1491946008.
  • Rosling, Hans, Factfulness, Sceptre, 2018, 978-1473637467.
  • Knaflic, Cole N., Storytelling with Data, 1st, Wiley, 2015, 978-1119002253.

ELE:

  • ELE.

Web based and Electronic Resources:

Other Resources:

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 30th September 2025 LAST REVISION DATE Wednesday 8th October 2025
KEY WORDS SEARCH Data Analysis

Please note that all modules are subject to change, please get in touch if you have any questions about this module.