Module Overview
Introduction
This e-portfolio documents my learning journey throughout the Research Methods module. It showcases my development in understanding scientific investigation, research ethics, literature review processes, statistical analysis, and professional research skills.
The portfolio contains artefacts from each unit, including reflective activities, collaborative discussions, statistics exercises, literature review and research proposal evaluations, and a comprehensive reflection on my learning experience.
Learning Journey
Module Units
Introduction to Research Methods
Week 1 • October 2025
Learning Outcomes
- Differentiate between inductive and deductive reasoning
- Understand why ethics are important in research
- Apply ethical considerations to area of research and professional practice
- Understand the elements within the scientific method
This unit introduced the fundamentals of research methods, focusing on the scientific method and ethics in computing research. The three main purposes of research were explored: to explore (familiarise with a topic), to describe (report findings), and to explain (answer "why").
Scientific Method & Reasoning Types
Deductive Reasoning:
- Moves from idea → observation → conclusion
- Example: "All men are mortal" → "Jason is a man" → "Jason is mortal"
- Can achieve perfect conclusions if all assumptions are 100% correct
- More difficult in non-lab settings due to need for fully agreed-upon facts
Inductive Reasoning:
- Moves from observation → analysis → theory
- Example: "I break out when I eat peanuts" → "This is an allergy symptom" → "I am allergic to peanuts"
- More frequently used for everyday problem-solving
- Conclusions may not always be right, but more practical for partial information
Research Ethics - Menlo Report Principles
- Respect: Participants must take part voluntarily with consent and right to anonymity
- Beneficence: Balance between maximizing research benefit and minimizing participant risk
- Justice: Equal treatment of participants and fair distribution of research benefits
- Respect for Law and Public Interest: Legal due diligence, accountability, integrity standards
Required Reading
- BCS Code of Conduct
- Data Protection and Intellectual Property guidelines
- Andersen & Hepburn (2020) - History of Scientific Method
- Finn & Shilton (2023) - The Menlo Report
Collaborative Discussion 1: Ethics in Computing
Case Study: The Programmer's Dilemma - Software Quality vs. Deadline Pressure
I have selected the ACM case study involving a software programmer who discovers critical security vulnerabilities in a system shortly before its scheduled release. Management pressures the programmer to launch on time, arguing that patches can be deployed post-release (ACM, no date).
Application of ACM Code of Ethics
This scenario directly engages several principles from the ACM Code of Ethics and Professional Conduct. Most critically, Principle 1.2 (Avoid Harm) requires computing professionals to minimize negative consequences, particularly regarding system security (ACM, 2018). The programmer faces a conflict between organizational pressure and professional responsibility to protect public safety. Additionally, Principle 2.5 (Comprehensive and Thorough Evaluation) mandates that professionals must evaluate systems comprehensively before deployment, which rushing to release would violate.
Comparison with BCS Code of Conduct
The BCS Code of Conduct addresses similar concerns through its Public Interest clause, which states that members shall "have due regard for public health, privacy, security and wellbeing of others and the environment" (BCS, 2021). Both codes prioritize public welfare over commercial interests. However, the BCS explicitly emphasizes the duty to relevant authority, requiring members to raise concerns through appropriate channels within their organization, providing clearer guidance on escalation procedures than the ACM code.
Legal and Social Implications
Legal Considerations: Releasing software with known vulnerabilities could constitute negligence under product liability law and potentially violate data protection regulations such as the UK Data Protection Act 2018 and GDPR, which mandate appropriate security measures for personal data (Legislation.gov.uk, no date). Organizations may face significant fines and legal action if breaches occur due to known, unpatched vulnerabilities.
Social Impact: Deploying insecure software endangers users' data, privacy, and potentially their safety in critical systems. This erodes public trust in technology and the computing profession, creating broader societal harm (Fjeld et al., 2020). The decision demonstrates how individual professional choices have cascading social consequences.
Professional Implications
The programmer's professionalism is tested through this dilemma. Succumbing to deadline pressure compromises professional integrity and violates the duty of care expected of computing professionals (Correa et al., 2023). True professionalism requires courage to advocate for ethical positions even when facing organizational resistance. As Finn and Shilton (2023) note in their analysis of the Menlo Report, researchers and practitioners must balance beneficence with respect for stakeholders—in this case, end-users who trust the system's security.
Ethical Path Forward:
- Document the vulnerabilities thoroughly
- Formally escalate concerns to senior management with clear risk assessments
- Refuse to certify the system as ready for deployment
- If organizational pressure persists, seek guidance from professional bodies
- In extreme cases, consider whistleblowing through appropriate legal channels
References
- ACM (2018) ACM Code of Ethics and Professional Conduct. Available at: https://www.acm.org/code-of-ethics
- ACM (no date) Code of Ethics: Case Studies. Available at: https://ethics.acm.org/code-of-ethics/case-studies/
- BCS (2021) BCS Code of Conduct. Available at: https://www.bcs.org/membership-and-registrations/become-a-member/bcs-code-of-conduct/
- Correa, N. et al. (2023) 'Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance', Patterns, 4(10), pp. 1-15.
- Finn, M. and Shilton, K. (2023) 'Ethics governance development: The case of the Menlo Report', Social Studies of Science, 53(3), pp. 315-340.
- Fjeld, J. et al. (2020) Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-based Approaches to Principles for AI. Berkman Klein Center Research Publication.
- Legislation.gov.uk (no date) UK Data Protection Act 2018. Available at: https://www.legislation.gov.uk/ukpga/2018/12/contents/enacted
Research Questions, the Literature Review and the Research Proposal
Week 2 • October 2025
Learning Outcomes
- Examine the characteristics that make up a suitable research topic
- Explore rational and creative methods for formulating a research idea
- Identify means of transforming research ideas into crafted research questions and proposals
- Conduct a literature search, critique the literature and present a literature review
This unit introduced the various aspects of a research proposal, starting with defining a research question. Defining a research topic may appear "daunting" (Boza, 2022), but it is essential for successful research. The process starts with an initial research idea, then asking pertinent questions that may need further revisions before preparing the research proposal.
Research Question Formulation
Key Considerations for a Suitable Research Topic:
- Specificity: The question should be focused enough to be answerable within the scope of the research
- Relevance: The topic should contribute to existing knowledge in the field
- Feasibility: The research should be achievable with available resources and time
- Originality: The question should address gaps in existing research
- Clarity: The question should be clearly articulated and unambiguous
Methods for Formulating Research Ideas:
- Rational Methods: Systematic review of literature, identifying gaps, building on existing theories
- Creative Methods: Brainstorming, mind mapping, exploring interdisciplinary connections
Literature Review Process
A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing identification of relevant theories, methods, and gaps in existing research.
Steps in Conducting a Literature Review:
- Define the scope: Establish clear boundaries for your review
- Search for literature: Use databases, keywords, and citation tracking
- Evaluate sources: Assess quality, relevance, and credibility
- Analyse and synthesise: Identify themes, patterns, and gaps
- Structure the review: Organize findings logically
- Write and revise: Present findings clearly with critical analysis
Research Proposal Structure
The research proposal serves as the foundation for research. Key components include:
- Title: Clear, concise description of the research
- Introduction: Background and context of the research problem
- Literature Review: Survey of existing research and identification of gaps
- Research Questions/Objectives: Clear statement of what the research aims to achieve
- Methodology: How the research will be conducted
- Timeline: Schedule for completing the research
- Expected Outcomes: Anticipated contributions to knowledge
- References: Sources cited in the proposal
Required Reading
- Boza, T. (2022) How to Write a Literature Review: Six Steps to Get You from Start to Finish
- Ermel et al. (2021) - Literature review in scientific disciplines
- Unit 2 Reading - Project choice and writing the proposal
- Critical evaluation of literature sources
E-Portfolio Activity: Literature Review and Research Proposal Outlines
Using the Literature Review Guide Questions provided, I developed outlines for both the Literature Review and Research Proposal. This planning activity helped structure my approach to the upcoming submissions.
Key Planning Elements Addressed:
- Selection of research topic from approved list
- Identification of key search terms and databases
- Initial mapping of relevant literature sources
- Preliminary research questions formulation
- Outline of proposed methodology approach
Collaborative Discussion 1: Peer Responses
Following the department's peer review guidelines, I provided constructive feedback to at least three peers on their Unit 1 discussion posts about ethics in computing. Each response was limited to 200-300 words to encourage reflection and dialogue.
Focus areas for peer responses:
- Evaluation of case study analysis and ethical reasoning
- Assessment of application of professional codes (ACM/BCS)
- Discussion of legal and social implications raised
- Suggestions for strengthening arguments with additional perspectives
References
- Boza, T. (2022) How to Write a Literature Review: Six Steps to Get You from Start to Finish.
- Ermel et al. (2021) Literature review methodologies in scientific disciplines.
Methodology and Research Methods
Week 3 • November 2025
Learning Outcomes
- Understand the different research methods
- Know which data collection methods are related to each method of research
- Have some idea which of these would be suited to your area of research
- Understand exploratory and descriptive research designs
This unit introduced methodology in general and provided an overview of research methods. Understanding that methodology choices are based on underlying assumptions is crucial. As Saunders et al. (2023) states, researchers have assumptions about realities (ontological), human knowledge (epistemological), and how values influence the research process (axiological). These assumptions shape how research questions are understood, methods used, and findings interpreted.
Research Design Types
Research design refers to having a plan about what you will do to answer your research question (Saunders et al., 2023). Two main types:
Exploratory Research:
- Problem is not clearly defined
- Cannot have conclusive results
- Provides better understanding of the problem or situation
- Example: Investigating effectiveness of CRM in mobile marketing
Conclusive Research:
- More specific focus
- Verifies insights and aids in selecting a course of action
- Descriptive Research: Describes elements or causes in specified research area
- Example: Critical analysis of social media as a marketing strategy
Research Methods Overview
Research methods are "systematic tools used to find, collect, analyse and interpret information" (Sage, 2021). Three main types:
Qualitative Research:
- Gathers data about experiences, behaviours and emotions
- Explores how or why something has occurred
- Associated with inductive approaches
- Tools: Focus groups, Case studies, Observations, Surveys, Interviews
Quantitative Research:
- Gathers numerical data for statistical analysis
- Discovers patterns and/or relationships
- Questions: "How much?", "How many?", "To what extent?"
- Associated with deductive approaches
- Tools: Surveys, Design of experiments, IoT/sensor data, Big data
Mixed Methods Research:
- Integrates both qualitative and quantitative research
- Provides a holistic approach to research projects
- Combines strengths of both methodologies
Data Collection Methods
Primary Research:
- Information gathered directly from the subject
- Original data collection through surveys, interviews, experiments
Secondary Research:
- Data gathered from previously published primary research
- Sources: Published case studies, articles, magazines, newspapers, books
Collaborative Discussion 1: Summary Post
Based on my initial post about the Programmer's Dilemma case study and the feedback received from peers, I provided a summary post synthesising the key insights from the discussion.
Key Themes from Discussion:
- Tension between commercial pressures and professional ethics
- Importance of clear escalation procedures in organizations
- Role of professional codes (ACM/BCS) in guiding ethical decisions
- Legal implications of knowingly releasing vulnerable software
- Balance between individual responsibility and organizational culture
Personal Reflection: The peer feedback enhanced my understanding of how ethical frameworks apply in practice. Several peers highlighted additional considerations I had not initially explored, particularly regarding the role of organizational culture in enabling or preventing ethical decision-making.
E-Portfolio Activity: Research Proposal Review
This activity involved reviewing and critiquing research proposals to develop skills in evaluating research design, methodology choices, and proposal structure.
Review Criteria Applied:
- Clarity and feasibility of research questions
- Appropriateness of methodology for research objectives
- Quality and relevance of literature review
- Logical structure and coherence of proposal
- Ethical considerations addressed
References
- British Research Methodology (BRM) (no date) Research Design.
- Sage (2021) Sage Research Methods: Methods Map.
- Saunders, M., Lewis, P. and Thornhill, A. (2023) Research Methods for Business Students. Pearson Education Limited.
Case Studies, Focus Groups and Observations
Week 4 • November 2025
Learning Outcomes
- Understand how to carry out case studies, focus groups and observation methods
- Know which method would be suitable for your investigation
- Consider the type of data you would obtain from each method
- Discuss the advantages and drawbacks of these methods
This unit introduced data collection methods generally used for qualitative research but sometimes applied in quantitative research. Understanding when and how to use each method is crucial for effective research design.
Case Studies
Case studies involve in-depth research and study of individuals or groups. They lead to hypotheses and widen the scope of studying a phenomenon.
Limitations:
- Should not be used to determine cause and effect
- Cannot make accurate predictions (due to possible researcher bias)
- Atypical respondents can lead to weak generalisations
- May move away from external validity
Focus Groups
A commonly used qualitative research method where a group of people (6-10) express insights on the topic being studied.
Key Requirements:
- Participants should have a common background
- Comparable experiences among group members
- Representative of target market
- Aim: Find answers to "why", "what" and "how" questions
Observation Methods
Quantitative Observation:
- Observing numerical values (weight, volume, shape)
- From a sample representing target market
- Objective data collection with statistical analysis
- Methods: Experimentation, surveys (e.g., customer satisfaction)
Qualitative Observation:
- Monitoring of characteristics
- Researcher observes respondents from a distance
- Respondents should be in comfortable environment
- Provides in-depth insight into behaviours
- Observer Roles: Complete observer, observer as participant, participant as observer, full participant
Formative Submission: Literature Review Outline
Title: The Role of Data Science in Enhancing Decision-Making and Operational Efficiency: A Case Study of Banking Digital Transformation
1. Background and Rationale
In today's digital world, data science is key to any bank's operations. Banks use data science to make automated decisions, predict customer behaviour, automate operations, visualise data, and assess and manage risks. Yet, some banks face challenges implementing data science, especially older banks with issues of siloed data, regulatory compliance, and workforce changes.
This literature review examines how data science improves fundamental operational and strategic levels of decision-making within banking, using lessons from the digital transformation at BNI London Branch. The review examines how qualitative dimensions (employee and process) aid in improving business results alongside quantitative, data-driven dimensions.
2. Research Aim and Objectives
Aim: Examine how Data Science helps make banking's digital initiatives more efficient and better at decision-making.
Goals:
- Examine how Data Science and analytics are used in the banking industry
- Recognize potential challenges and success factors in Data Science deployment in regulated environments
- Examine how qualitative and quantitative perspectives meet and work together
- Propose a framework to integrate Data Science with operational decision-making in commercial banking
3. Key Themes and Theoretical Framework
- Digital transformation in banking: IT centralisation, automation, data-driven organization
- Data science decision-making: Predictive models, AI, business intelligence dashboards
- Operational Efficiency & Risk Management: Problem identification, process optimization, compliance
- Human–Data Interaction: How employees, managers and analysts apply insights in practice
Theoretical Framework: Socio-technical systems theory and data-driven decision models, demonstrating balance between human knowledge and algorithmic intelligence.
4. Planned Data Collection Method
- Case Study: Technological change at BNI London Branch implementing data science tools (transaction analytics, automated reporting)
- Focus Groups: With Heads of Service (IT, Operations, Treasury) for qualitative information on Data Science impact on daily decisions
- Observations: Analysis of how employees use analytics dashboards or automated reports in their working process
These approaches fit Unit 4 objectives: enabling deep context (case study) and practical behavioural insights (focus groups and observations).
5. Type of Data Expected
- Quantitative: System logs, performance metrics, processing times, efficiency indicators before and after Data Science implementation
- Qualitative: User experiences, managerial perspectives, behavioural trends from focus groups and observation
Mixed-methods approach: Combining both data types for comprehensive understanding of Data Science impact on banking operations.
Preliminary References
- Davenport, T. H. & Harris, J. G. (2017) Competing on Analytics: The New Science of Winning. Harvard Business Review Press.
- Provost, F. & Fawcett, T. (2013) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media.
- Ghosh, A. & Scott, J. (2021) 'AI and Decision-Making in Financial Institutions', Journal of Financial Innovation, 8(3), pp. 45–59.
- Bryman, A. (2016) Social Research Methods. 5th edn. Oxford University Press.
Interviews, Survey Methods, and Questionnaire Design
Week 5 • November 2025
Learning Outcomes
- Understand how interviews and surveys fit into research
- Understand how pre- and post-testing can be used in research
- Design a questionnaire to obtain the responses needed for investigation
- Understand how the data obtained can be analysed
- Know the difference between a good questionnaire and a poor one
This unit introduced the use of interviews, survey methods and questionnaire design. These methods are often used together to gain further insight into specific areas of investigation.
In-Depth Interviews
One of the most common qualitative research methods. It is a conversational piece involving only one respondent at a time, providing opportunities to get in-depth details from the respondent.
Key Characteristics:
- One-on-one conversational format
- Allows for deep exploration of topics
- Flexible follow-up questions based on responses
- Rich qualitative data collection
Surveys vs Questionnaires
Understanding the distinction between these two terms is important:
Survey:
- Comprehensive method for gathering and analysis of data
- One of the most important quantitative methods
- Used to explore opinions, trends, etc.
- Will involve a questionnaire as part of the process
Questionnaire:
- Set of questions with answer choices used to conduct a survey
- Valuable research instrument offering flexibility
- Like a written interview
- Can be carried out online, over the phone, or face-to-face
Survey Process (Cyclic): Survey Questionnaire → Survey Logic → Survey Distribution → Survey Data Collection & Analytics
Question Types
The type of questions used depends on the research approach:
- Open Questions: Allow free-form responses (qualitative research)
- Closed Questions: Predetermined answer choices (quantitative research)
- Mixed: Combination of both (mixed methods research)
Note: Some questionnaires are better than others, and mistakes are easy to make, especially in technical areas.
Pre-Testing and Post-Testing
These methods can be used with any research method to present a viable "before-and-after" picture of implementing a new process or system.
- Pre-Test: Baseline measurement before intervention/implementation
- Post-Test: Measurement after intervention to assess change
- Comparison: Enables evaluation of impact and effectiveness
E-Portfolio Activity: Data Collection Reflection
Research Topic: The Role of Data Science in Enhancing Decision-Making and Operational Efficiency in Banking Digital Transformation
Suitable Data Collection Tools:
- Case Study: BNI London Branch digital transformation implementation
- Semi-structured Interviews: With department heads (IT, Operations, Treasury) to understand decision-making processes
- Questionnaires: For broader staff feedback on analytics dashboard usage
- System Logs/Metrics: Quantitative data on processing times and efficiency indicators
Analysis Approach:
- Quantitative: Statistical analysis of performance metrics (before/after comparison)
- Qualitative: Thematic analysis of interview transcripts and open-ended responses
- Mixed Methods Integration: Triangulation to validate findings across data types
How This Answers Research Question: By combining quantitative efficiency metrics with qualitative insights on user experience and decision-making processes, I can comprehensively assess how Data Science implementation has impacted both operational efficiency and strategic decision-making quality at BNI London Branch.
Reflective Activity 2: Inappropriate Use of Surveys
The Cambridge Analytica Scandal (2018)
Cambridge Analytica obtained and shared data from millions of Facebook users through innocuous surveys on Facebook. This represents the highest-profile case of surveys being used for alternative means, likely for monetary gains (Confessore, 2018).
How It Happened:
- A personality quiz app ("This Is Your Digital Life") collected data from users who consented
- However, the app also harvested data from users' Facebook friends without their knowledge or consent
- Data was used for political advertising and voter profiling
- Approximately 87 million users' data was compromised
Additional Examples of Inappropriate Survey Use:
1. OkCupid Emotional Manipulation Study (2014)
- Dating platform manipulated match percentages shown to users
- Users were not informed they were part of an experiment
- Raised concerns about consent and potential emotional harm
2. Facebook Emotional Contagion Study (2014)
- Facebook manipulated news feeds to study emotional responses
- Nearly 700,000 users unknowingly participated
- No informed consent was obtained
Impact Analysis from Multiple Standpoints:
Ethical:
- Violation of informed consent principles
- Breach of trust between platforms and users
- Using data for purposes beyond original consent
Social:
- Erosion of public trust in online surveys and research
- Potential manipulation of democratic processes
- Increased public awareness of data privacy issues
Legal:
- GDPR implementation accelerated in Europe
- Facebook fined $5 billion by FTC
- Cambridge Analytica filed for bankruptcy
- Strengthened data protection regulations globally
Professional:
- Highlighted need for ethical guidelines in data collection
- Increased scrutiny of research ethics in tech industry
- Professional bodies updated codes of conduct
- Greater emphasis on data governance and accountability
Key Lesson: These cases demonstrate that surveys and data collection must adhere to strict ethical standards, including informed consent, data minimisation, purpose limitation, and transparency. Researchers have a professional responsibility to ensure data is collected and used ethically, regardless of commercial pressures.
References
- Confessore, N. (2018) 'Cambridge Analytica and Facebook: The Scandal and the Fallout So Far', The New York Times, 4 April.
- Kramer, A.D.I., Guillory, J.E. and Hancock, J.T. (2014) 'Experimental evidence of massive-scale emotional contagion through social networks', PNAS, 111(24), pp. 8788-8790.
Quantitative Methods - Descriptive and Inferential Statistics
Week 6 • November 2025
Learning Outcomes
- Understand and apply descriptive statistics
- Identify the different levels of measurement
- Produce measures of location and spread
- Define the different levels of quantitative data
This unit introduced quantitative methods, which examine relationships between variables measured numerically and analysed through statistical and graphical techniques. The unit of analysis is data, consisting of observed values of one or more variables of interest, organised into data sets.
Understanding Data Sets
A data set can be thought of as a table where:
- Columns: Represent different variables
- Rows: Represent individual observations
- Cells: Contain the value that the variable takes for a given observation
To extract meaningful business intelligence from data, the methodology employed must be valid for the variable(s) of interest. The first important distinction concerns the level of measurement of each variable.
Levels of Measurement
Understanding the level of measurement is crucial for selecting appropriate statistical methods:
Nominal:
- Categories with no inherent order
- Examples: Gender, nationality, colour
- Can only count frequencies
Ordinal:
- Categories with meaningful order
- Examples: Satisfaction ratings (poor, fair, good, excellent)
- Differences between values not necessarily equal
Interval:
- Ordered with equal intervals between values
- No true zero point
- Examples: Temperature (Celsius/Fahrenheit), dates
Ratio:
- Equal intervals with a true zero point
- Examples: Height, weight, income, age
- Allows for meaningful ratios (e.g., "twice as much")
Descriptive Statistics
When exploring data, it is helpful to summarise data in meaningful ways. Descriptive statistics provides two main types of measures:
Measures of Location (Central Tendency):
Represents the value taken by "a typical observation" – an observation that falls "right in the middle of the data".
- Mean: Sum of all values divided by the number of observations
- Median: Middle value when data is ordered
- Mode: Most frequently occurring value
Measures of Dispersion (Spread):
Provides an idea of the "variability" or "spread" of the data.
- Range: Difference between maximum and minimum values
- Variance: Average of squared deviations from the mean
- Standard Deviation: Square root of variance
- Interquartile Range (IQR): Range of the middle 50% of data
Graphical Summaries
One approach to summarising data is through graphical representation:
- Histograms: Distribution of continuous data
- Bar Charts: Comparison of categorical data
- Box Plots: Show median, quartiles, and outliers
- Scatter Plots: Relationship between two variables
- Pie Charts: Proportions of a whole
Note: Unit 8 will deal more specifically with data visualisation.
Application to My Research
For my research on Data Science in banking digital transformation, quantitative methods will be essential for:
- Measuring Efficiency: Processing times, transaction volumes, error rates (ratio data)
- User Satisfaction: Likert scale responses from staff surveys (ordinal data)
- Performance Metrics: Before/after comparison of KPIs using measures of central tendency and dispersion
- Visual Analysis: Graphs showing trends in operational efficiency over time
Understanding levels of measurement helps ensure appropriate statistical tests are applied to each variable type in my mixed-methods analysis.
Key Concepts
- Quantitative methods examine relationships between numerically measured variables
- Level of measurement determines appropriate statistical methods
- Descriptive statistics summarise data through location and dispersion measures
- Graphical summaries provide visual representation of data patterns
Inferential Statistics and Hypothesis Testing
Week 7 • December 2025
Learning Outcomes
- Apply inferential statistics to data analysis
- Identify the correct probability distributions
- Perform appropriate hypothesis tests
- Understand the basic principle of probability
This unit explored inferential statistics and hypothesis testing. As Purdue University (2023) states, "The goal of inferential statistics is to discover some property or general pattern about a large group by studying a smaller group of people in the hopes that the results will generalize to the larger group." We make inferences about a population from a sample of data, and since data is inherently variable, all such inferences are subject to uncertainty quantified using probability.
Inferential Statistics Concepts
Statistical Inference:
- Process of extracting meaningful information from raw data
- Using sample data to make valid inferences about underlying population
- Uncertainty quantified using probability
Hypothesis Testing:
- Alternative form of inference using data to provide evidence about assumptions
- Preferred when comparing relevant values between two or more populations
- Tests whether some assumption about a population value is likely to be true
E-Portfolio Activity: 7.1 Summary Measures Worksheet
Using the dietary data from Data Set B (Diets), I calculated summary measures for weight loss data:
Exercise 7.1.1 - Diet B Analysis:
- Sample size: n = 50 individuals
- Sample mean weight loss: Using
=AVERAGE()function - Sample standard deviation: Using
=STDEV()function - Interpretation: Compared effectiveness of Diet A vs Diet B based on mean weight loss and variability
Exercise 7.1.2 - Quartile Analysis:
- Median weight loss: Using
=MEDIAN()function - First quartile (Q1): Using
=QUARTILE(data,1) - Third quartile (Q3): Using
=QUARTILE(data,3) - Interquartile Range (IQR): Q3 - Q1
Exercise 7.1.3 - Brand Preferences (Data Set D):
- Frequency analysis using
=COUNTIF()function - Calculated percentage frequencies for each brand
- Compared brand preference patterns between demographic areas
E-Portfolio Activity: 7.2 Hypothesis Testing Worksheet
Related Samples T-Test (Exercise 7.2.1):
Data Set F (Designs) - Container design comparison
- t = 2.875 with 9 degrees of freedom
- Two-tailed p-value = 0.018 (significant at 5% level)
- Mean difference = 13.2 items per store
- Conclusion: Strong evidence that Design 1 produces higher sales
F-Test for Variances:
Data Set B (Diets) - Testing equality of variances
- F = 0.839 with 49 and 49 degrees of freedom
- Two-tailed p-value = 0.5399 (not significant)
- Conclusion: Population variances do not differ; proceed with equal variances t-test
Independent Samples T-Test (Exercise 7.2.3):
Data Set B (Diets) - Comparing weight loss between diets
- t = 3.072 with 98 degrees of freedom
- Two-tailed p-value = 0.0028 (significant at 1% level)
- Mean weight loss: Diet A = 5.341 kg, Diet B = 3.710 kg
- Difference = 1.631 kg
- Conclusion: Strong evidence Diet A is more effective
Assessment: Literature Review Submission
Title: The Role of Data Science in Enhancing Decision-Making and Operational Efficiency: A Case Study of Banking Digital Transformation
Literature Review Structure:
- Introduction: Data science in modern banking, challenges for traditional banks
- Digital Transformation in Banking: Concepts, trends, digitalisation vs true transformation
- Data Science for Decision-Making: Analytics, AI, predictive models, risks and bias
- Operational Efficiency & Risk Management: Automation, process optimisation, critical gaps
- Human–Data Interaction: Socio-technical perspectives, theoretical foundations
- Conflicting Evidence & Critical Debate: AI quality, IT centralisation, compliance
- Case Study: BNI London Branch: IT centralisation, automated reporting, governance
- Conceptual Framework: Five-pillar framework for integrating data science
- Conclusion: Synthesis and recommendations
Proposed Conceptual Framework (5 Pillars):
- Robust Data Foundations: High-quality data, clear lineage, legacy integration, standardised models
- Model Governance & Reliability: Continuous monitoring, explainability, validation, regulatory alignment
- Human–Data Interaction: Structured training, interpretability tools, workflow integration
- Operational Embedding: Automation of routine tasks, dashboard integration, core banking processes
- Regulatory & Ethical Compliance: FCA guidelines, data privacy, organisational risk appetite
Tutor Feedback on Literature Review
1. Knowledge and Understanding - Excellent
"Demonstrates comprehensive and sophisticated understanding of how data science is transforming operational and strategic decision-making in the banking sector... The conceptual grounding—especially the contrast between digitalisation and true digital transformation—is insightful and well supported by recent scholarship."
2. Criticality - Excellent
"Critical engagement is evident throughout... The 'Conflicting Evidence and Critical Debate' section is particularly strong, presenting balanced perspectives on key tensions such as centralisation versus flexibility and automation versus accountability."
3. Use of Relevant Sources - Strong
"The literature base is wide-ranging, authoritative, and current... integrates peer-reviewed sources and industry reports from 2015–2024, including works from MIS Quarterly, Journal of Banking & Finance, AI & Society, and Deloitte Insights."
4. Structure and Presentation - Exemplary
"The structure of the review is exemplary. It follows a logical and clearly signposted sequence... The framework for integrating data science in banking operations (Section 8) is a highlight—methodical, evidence-based, and clearly derived from the literature."
5. Academic Integrity - Exemplary
"All materials are properly attributed using Harvard conventions, and there is no evidence of plagiarism or citation mismanagement. The balance between paraphrasing, direct quotation, and synthesis demonstrates mature and ethical academic practice."
Key Literature Review References
- Vial, G. (2019) 'Understanding digital transformation: A review and research agenda', MIS Quarterly, 43(1), pp. 223–254.
- Susanti, D. et al. (2023) 'Digital transformation maturity in the financial sector', International Journal of Information Management, 69(1), pp. 102–118.
- Ghosh, A. & Scott, J. (2021) 'AI and decision-making in financial institutions', Journal of Financial Innovation, 8(3), pp. 45–59.
- Kitchin, R. (2021) 'Data-driven decisions: Limits, biases, and risks', Big Data & Society, 8(2), pp. 1–13.
- Johari, A. & Abdullah, R. (2022) 'Process mining and automation in banking operations', Journal of Information Technology Management, 33(1), pp. 89–104.
Data Analysis and Visualisation
Week 8 • December 2025
Learning Outcomes
- Understand different types of data analysis and their usefulness
- Understand different charts available to present different data types
- Identify key elements in Dashboards
- Evaluate critically existing literature, research design and methodology
This unit shifted focus from methods to the unit of analysis: data. We examined various ways to handle and make sense of data, covering both quantitative and qualitative approaches. As Microsoft (2023) notes, "Dashboards connect all kinds of different metrics, data sources, APIs, and services to help companies extract relevant information from those sources and display it in user-friendly ways."
Qualitative vs Quantitative Data Analysis
Qualitative Data Analysis:
- Made up of observations, images, and words
- Open to different interpretations - harder for researcher to remain unbiased
- Almost impossible to derive absolute meaning
- Mostly used for exploratory research
- Analysis begins as soon as data is available
- Coding: Categorisation of responses (e.g., from unstructured interviews)
Quantitative Data Analysis:
- Clear distinction between data preparation and analysis stages
- Uses statistical methods and numerical analysis
- More objective interpretation possible
- Results can be visualised through charts and graphs
Business Intelligence and Dashboards
In an era where Business Intelligence is on the rise, dashboards turn data into actionable insights:
- Connect different metrics, data sources, APIs, and services
- Display information in user-friendly ways
- Connect to specific metrics or Key Performance Indicators (KPIs)
- Enable deep dives into specific information
- Provide vital business intelligence for monitoring success
E-Portfolio Activity: Charts Worksheet
Exercise 8.1 - Percentage Frequency Bar Chart:
Data Set D (Brandprefs) - Brand preferences for Area 2
- Created clustered column chart for breakfast cereal brand preferences
- Findings: Brand A least preferred, Brand B second, majority preferred "Other" brands
- Both Areas show similar patterns but Area 2 had higher preferences for Brand A and B
Exercise 8.2 - Clustered Column Bar Chart:
Data Set E (Heather) - Species prevalence comparison
- Created percentage frequency clustered column chart
- Compared heather species prevalence across two different locations
- Visual comparison enabled identification of distribution patterns
Exercise 8.3 - Relative Frequency Histogram:
Data Set B (Diets) - Weight loss distribution for Diet B
- Calculated upper class boundaries (UCB) and class marks
- Used
=FREQUENCY()array function for class frequencies - Created relative frequency histogram with 7 classes (width = 2)
- Diet A: Unimodal and symmetrical, hint of negative skewness
- Diet B: Similar distribution pattern with lower mean weight loss
- Comparison: Both diets effective, Diet A shows greater average weight loss
Charts Worksheet Visualizations
Chart Interpretations:
- Brand Preferences: Both areas show "Other" brands most preferred; Area 2 has higher Brand A and B preferences than Area 1
- Diet A Histogram: Unimodal, approximately symmetrical distribution centered around 5 kg weight loss
- Diet B Histogram: Slightly left-skewed distribution with lower mean weight loss (3.71 kg vs 5.34 kg)
- Conclusion: Diet A appears more effective for weight loss based on both mean and distribution shape
E-Portfolio Activity: Inference Worksheet Notes
Four Steps of Hypothesis Testing:
- State the Hypotheses:
- H₀ (Null Hypothesis): Statement about population parameter assumed to be true
- H₁ (Alternative Hypothesis): Contradicts H₀ (less than, greater than, or not equal to)
- Set the Criteria:
- Level of significance typically 5% (behavioural) or 1% (medical)
- Determines threshold for decision-making
- Compute the Test Statistic:
- Mathematical formula determining likelihood of sample outcomes
- Shows how far sample mean is from population mean
- Make a Decision:
- p-value < 0.05 → Reject H₀
- p-value > 0.05 → Accept H₀
Decision Errors:
- Type I Error (α): Rejecting H₀ when it's actually true (false positive)
- Type II Error (β): Accepting H₀ when it's actually false (false negative)
- Power (1-β): Probability of correctly rejecting a false H₀
Key Principles:
- Hypotheses must be set up before data inspection for impartial testing
- Avoid claiming to have "proved" anything - only disprove if p-value = 0
- Related samples tests are more powerful than unrelated samples equivalents
Formative Submission: Research Proposal Outline
Title: The Role of Data Science in Enhancing Decision-Making and Operational Efficiency in Banking: A Case Study of BNI London Branch
Research Aim:
To explore how data science supports decision-making and operational efficiency in a regulated banking environment.
Research Objectives:
- Examine how data science tools are applied in banking operations
- Explore the perceived impact of data science on operational efficiency
- Identify key challenges in implementing data-driven initiatives in banking
Research Questions:
- How is data science used to support decision-making in banking operations?
- How does data science contribute to operational efficiency?
- What challenges affect the effective use of data science in a regulated banking context?
Proposed Methodology:
- Approach: Case study focusing on BNI London Branch
- Qualitative: Interviews/informal discussions with operations, IT, and management staff
- Quantitative: Review of operational performance indicators (processing times, reporting efficiency)
Ethical Considerations:
- No customer-identifiable data will be used
- All participants will provide informed consent
- Organisational confidentiality will be maintained
Tutor Feedback on Research Proposal Outline
Strengths:
- "Relevant and practice-oriented proposal that fits well within the scope"
- "Background and problem framing are clear and accessible"
- "Focusing on a specific case study strengthens practical relevance and feasibility"
- "Aim, objectives, and research questions are well aligned and appropriately scoped"
- "Mixed approach combining qualitative interviews with limited quantitative indicators can add depth"
Suggestions for Improvement:
- Improve academic tone by reducing conversational language
- Strengthen links to existing scholarly debates on digital transformation
- Make objectives more analytical (e.g., before/after adoption comparisons)
- Add more detail on sampling (number of participants, roles)
- Clarify analytical method (e.g., thematic analysis)
- Explain how operational metrics will be analysed
References
- Business Research Methodology (BRM) (2021) Qualitative Data Analysis.
- Microsoft (2023) Why data dashboards are important.
- Davenport, T. & Harris, J. (2017) Competing on Analytics.
- Provost, F. & Fawcett, T. (2013) Data Science for Business.
- Vial, G. (2019) 'Understanding digital transformation', MIS Quarterly.
Validity and Generalisability in Research
Week 9 • January 2026
Learning Outcomes
- Understand how validity, generalisability and reliability affect investigation and research design
- Consider how to analyse and present results to answer research questions
- Apply business visualisation techniques using PowerBI
This unit focused on three critical dimensions within research: validity, generalisability, and reliability. These concepts affect any conclusions made after data collection and analysis, and must be considered before data collection begins. While reliability refers to the consistency of the measurement process, validity focuses on whether the measurement has the correct target—a far broader and more complex concept.
Key Research Concepts
Validity
- Focuses on whether the measurement has the correct target
- Internal Validity: Extent to which results can be attributed to the intervention rather than confounding factors
- External Validity: Extent to which results can be generalised to other contexts
- Construct Validity: Whether the measurement actually measures what it claims to measure
- Content Validity: Whether the measure covers all aspects of the construct
Reliability
- Refers to the consistency of the measurement process
- Test-Retest Reliability: Consistency over time
- Inter-Rater Reliability: Consistency across different observers
- Internal Consistency: Consistency within items measuring the same construct
- A measurement must be reliable before validity can be assessed
Generalisability
- The extent to which findings can be applied to other populations, settings, or times
- Affected by sampling method, sample size, and research context
- Qualitative research often prioritises transferability over generalisability
- Case studies may have limited generalisability but high contextual depth
Data Analysis Considerations
Qualitative vs Quantitative Data:
- Quantitative: Leads to descriptive and inferential statistics; requires data cleansing and validation
- Qualitative: Often down to interpretation; presents unique challenges in presentation
- Data Cleansing: Process of detecting and correcting corrupt or inaccurate records
- Data Validation: Ensuring data meets quality and integrity requirements before analysis
E-Portfolio Activity: Business Visualisation with PowerBI
Completed the PowerBI practical training exercise to design reports and dashboards. This exercise applied business visualisation techniques to present banking operational data in user-friendly, actionable formats.
PowerBI Skills Developed:
- Creating interactive dashboards with multiple visualisation types
- Designing KPI cards for key metrics at a glance
- Building line charts for trend analysis over time
- Creating bar/column charts for categorical comparisons
- Implementing donut charts for proportional data
- Before/after comparison visualisations for impact assessment
PowerBI Dashboard: BNI London Branch Operations
Dashboard Components:
- KPI Cards: Total transactions (125,847), Average processing time (2.3s), Error rate (0.8%), Compliance score (98.5%)
- Transaction Volume Trend: Line chart showing 32% growth over 12 months
- Departmental Processing Times: Horizontal bar chart comparing Operations, Compliance, Risk, Treasury, and IT
- Data Quality Metrics: Donut chart showing 95.8% overall data quality (Complete, Accurate, Timely, Consistent)
- Before/After Comparison: Demonstrates efficiency improvements ranging from 73% to 80% reduction in processing times
Key Insights from Dashboard:
- Data science implementation reduced report generation time by 73% (45 min → 12 min)
- Risk assessment improved by 73% (30 min → 8 min)
- Fraud detection efficiency increased by 80% (25 min → 5 min)
- Customer onboarding streamlined by 75% (60 min → 15 min)
- Overall compliance score exceeds FCA/PRA standards at 98.5%
Application to BNI Research Project
Validity Considerations:
- Internal Validity: Using mixed methods (interviews + metrics) to triangulate findings
- External Validity: Case study approach limits generalisability but provides depth
- Construct Validity: Operationalising "operational efficiency" through measurable KPIs
Reliability Measures:
- Semi-structured interview protocol for consistency across participants
- Standardised operational metrics from existing systems
- Member checking to validate interview interpretations
Generalisability Strategy:
- Detailed thick description of BNI London context for transferability assessment
- Comparison with industry benchmarks and literature findings
- Clear articulation of contextual factors affecting findings
References
- Bryman, A. (2016) Social Research Methods. 5th edn. Oxford: Oxford University Press.
- Creswell, J.W. & Creswell, J.D. (2018) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 5th edn. Sage.
- Microsoft (2023) Power BI Documentation. Available at: https://docs.microsoft.com/en-us/power-bi/
Research Writing
Week 10 • January 2026
Learning Outcomes
- Understand research writing, including dissertations and publications
- Understand how to structure a dissertation and prepare for writing
- Evaluate critically existing literature, research design and methodology
- Produce and evaluate critically a research proposal
This unit focused on research writing as a highly valuable skill for communicating technical knowledge. It brought together work from previous units—finding research areas, conducting literature reviews, and selecting appropriate research methods—culminating in the research proposal presentation.
Research Writing Principles
Dissertation Structure:
- Introduction: Background, problem statement, aims, objectives, research questions
- Literature Review: Critical evaluation of existing research, identification of gaps
- Methodology: Research design, data collection, analysis methods, ethical considerations
- Results/Findings: Presentation of data, statistical analysis
- Discussion: Interpretation, comparison with literature, implications
- Conclusion: Summary, contributions, limitations, future research
Assessment: Research Proposal Presentation
Title: The Role of Data Science in Enhancing Decision-Making and Operational Efficiency in Banking: A Case Study of the BNI London Branch
Presentation Structure (15 Slides):
- Title & Research Context
- Industry Context & Motivation: Digital transformation, regulatory pressure
- Problem Statement: Data fragmentation, decision latency, human-algorithm tension
- Research Aim: Case study of BNI London Branch
- Research Questions: Application, efficiency, challenges
- Literature Landscape: Analytics as strategic asset, BI adoption failures
- Research Design: Convergent mixed-methods case study
- Data Types & Operational Metrics: KPIs and qualitative insights
- Analytical Framework: Analytics maturity pyramid
- Visual Analytics Strategy: Systematic chart selection rules
- Statistical Evaluation Strategy: T-tests, correlation, ANOVA
- Validity, Reliability & Generalisability
- Business Intelligence & Decision Support: Dashboard mockup
- Ethics and Governance: GDPR, anonymisation, data retention
- Expected Contributions & Conclusion
Key Research Elements:
- Research Gap: Limited context-specific studies on data science implementation in regulated banking
- Methodology: Convergent mixed-methods—quantitative KPIs and qualitative interviews collected in parallel, then triangulated
- Data Window: 12 months historical (monthly) + 8 weeks daily (before/after system change)
- Analytical Approach: Exploratory analysis → Statistical evaluation → Visual interpretation → Decision support
Operational KPIs Defined:
- Transaction volume (core banking systems)
- Processing time in seconds (SWIFT gateway)
- Error rates as exception percentages (operations logs)
- Automation levels (manual vs automated processing)
- Cycle time, SLA breach rate, time-to-decision
View Full Presentation: slides not published in this portfolio
Tutor Feedback: Research Proposal Presentation
Knowledge and Understanding (25%):
- "Demonstrates clear knowledge of data science applications in banking operations"
- "Problem statement is operationally defined through measurable constructs"
- "Research questions are logically structured to address application, efficiency, and adoption challenges"
Suggestion: Include concise conceptual definitions and a recognised theoretical lens (e.g., analytics maturity models)
Criticality (25%):
- "Shows evidence of analytical reasoning through identification of limitations"
- "Justification for convergent mixed-methods case study is coherent"
- "Acknowledges contextual constraints and limited generalisability"
Suggestion: Expand comparison with alternative research approaches
Presentation and Communication (30%):
- "Follows a clear progression from context through methodology"
- "Visual design is consistent and supports topic communication"
- "Terminology used accurately, appropriate academic tone maintained"
Suggestion: Reduce text density in some slides; slower pacing at transitions
Structure and Presentation (10%):
- "Adheres to expected research proposal structure"
- "Sequencing supports continuity and logical argument development"
Use of Relevant Sources (10%):
- "Appropriate use of academic and industry literature"
- "Sources integrated to justify research gap and support methodological decisions"
Formative Feedback: Statistical Worksheets
Technical Application of Statistical Methods:
- "Statistical techniques were correctly applied, including chi-square tests, paired and independent t-tests"
- "Visualisation tools such as bar charts, clustered column charts, and histograms"
- "Hypothesis formulation and test selection were appropriate to data characteristics"
Interpretation and Analysis:
- "Written summaries accurately interpret the meaning of test results"
- "Connection between observed patterns and real-world meaning is well articulated"
- "Consideration of p-values, distribution patterns, and practical implications"
Research Readiness and Validity:
- "Unit 8 inference worksheet demonstrated strong conceptual understanding"
- "Assumptions, limitations, and issues of generalisability acknowledged"
- "Handled with academic integrity"
Business Intelligence Integration:
- "Starter-Sales Analysis dashboard shows sound use of BI principles"
- "Visuals are clear and effectively communicate performance trends"
Suggestion: Include brief interpretive narratives alongside visuals
Presentation and Documentation:
- "All documents are clear, well-organised, and formatted to a professional standard"
Presentation Transcript Highlights
Industry Context (Slide 2):
"The banking sector is undergoing profound digital transformation globally. Financial institutions increasingly rely on analytics for daily reporting, strategic monitoring, and process automation. Simultaneously, regulatory pressures continue to intensify, demanding ever-higher levels of accuracy, transparency, and compliance."
Problem Statement (Slide 3):
"Despite abundant data availability, a significant problem persists in banking operations: data available does not equal data used effectively. Many organisations suffer from fragmented reporting where insights scatter across more than five disconnected systems, requiring manual reconciliation."
Research Design (Slide 7):
"I have chosen a convergent mixed-methods design—meaning quantitative KPI analysis and qualitative stakeholder insights are collected in parallel, then triangulated for validation. This design strengthens validity by ensuring neither numerical patterns nor contextual explanations dominate inappropriately."
Validity Assessment (Slide 12):
"Generalisability is acknowledged as context-bound. As a single-case study focused on one branch, external generalisation is inherently limited. This represents a known trade-off: depth of insight versus breadth of applicability. This honest acknowledgment of limitations strengthens rather than weakens the research's credibility."
Expected Research Contributions
Academic Contributions:
- Context-specific insights into data science implementation in regulated banking
- Replicable methodological framework for similar studies
- Evidence base for data-driven decision-making in financial services
Practical Contributions:
- KPI dashboard specification for banking operations
- Evidence-based improvement recommendations
- Analytics maturity pyramid framework documentation
- Evaluation report assessing implementation effectiveness
Key References from Presentation
- Chen, H. et al. (2012) 'Business intelligence and analytics', MIS Quarterly.
- Davenport, T. & Harris, J. (2017) Competing on Analytics. Harvard Business Review Press.
- Elbashir, M.Z. et al. (2008) 'Measuring the effects of business intelligence systems', International Journal of Accounting Information Systems.
- Gartner (2023) Business Intelligence and Analytics Report.
- Vial, G. (2019) 'Understanding digital transformation', MIS Quarterly, 43(1), pp. 223–254.
Going Forward: Professional Development and Your e-Portfolio
Week 11 • January 2026
Learning Outcomes
- Provide necessary reflection for the completion of your learning loop
- Complete the Professional Skills matrix and ensuing action plan
- Review your learning approach based on reflections on your learning process
- Consider professional goals and the outcomes of modules undertaken
This unit focused on professional development based on the e-Portfolio, culminating the various professional practice discussions throughout the module. The e-Portfolio serves as a key tool for recording professional practice and reflecting on processes and outcomes, enabling the creation of a Showcase e-Portfolio that can be shared with peers or employers.
E-Portfolio Value and Purpose
Benefits of E-Portfolios:
- Comprehensive documentation of entire learning journey
- Evidence of skills development for employers
- Platform for reflective practice and self-assessment
- Foundation for Continuous Professional Development (CPD)
- Showcase for academic and professional achievements
E-Portfolio Components:
- Formative Assessments: Discussion posts, peer responses, reflective activities
- Summative Assessments: Literature Review, Research Proposal Presentation
- Statistical Worksheets: Units 7-9 completed exercises
- Professional Skills Matrix: Self-assessment and development planning
- Learning Loop: Holistic review of degree programme
Professional Skills Matrix
Technical Skills Assessment:
- Data Analysis: Strong proficiency in statistical methods, Excel, Python - Applied in banking operations at BNI London
- Machine Learning: Developing skills through MSc coursework - CIFAR-10, transfer learning projects
- Database Management: Advanced experience with SQL, MySQL - Daily use in IT infrastructure role
- Business Intelligence: PowerBI, dashboard design - Demonstrated in Unit 9 exercise
- Research Methods: Mixed methods, statistical inference - Applied throughout this module
Professional Skills Assessment:
- Critical Thinking: Strong - Demonstrated in literature review and case study analysis
- Written Communication: Strong - Academic writing, technical documentation
- Oral Presentation: Developing - Research proposal presentation experience
- Project Management: Strong - IT infrastructure projects, BCP documentation
- Ethical Practice: Strong - Research ethics, data governance awareness
Areas for Development:
- Advanced machine learning techniques and deep learning
- Cloud computing platforms (AWS, Azure) for data science
- Advanced data visualisation and storytelling
- Leadership and team management skills
Industry Certifications Action Plan
Priority Certifications (Next 12 Months):
- Microsoft Azure Data Scientist Associate (DP-100): Cloud ML deployment
- AWS Certified Machine Learning - Specialty: Industry-standard cloud skills
- Google Professional Data Engineer: Data pipeline and architecture
Banking/Finance Specific:
- Financial Risk Manager (FRM): Risk management expertise
- Certified Information Systems Security Professional (CISSP): Security in banking IT
- ITIL Foundation: IT service management best practices
Learning Loop Reflection
Key Learnings from Research Methods Module:
- Research Design: Understanding of qualitative, quantitative, and mixed methods approaches
- Literature Review: Skills in critical analysis and synthesis of academic sources
- Statistical Methods: Practical application of descriptive and inferential statistics
- Ethics in Research: Importance of informed consent, data protection, and integrity
- Professional Practice: Reflective practice and continuous improvement mindset
Application to Professional Practice:
- Research skills directly applicable to banking transformation projects at BNI London
- Statistical analysis enhances data-driven decision-making capabilities
- Critical evaluation skills improve assessment of vendor proposals and system implementations
- Ethics training reinforces commitment to data governance and compliance
Future Development Goals:
- Complete MSc Data Science with focus on practical banking applications
- Develop data science capabilities within BNI London IT infrastructure
- Contribute to digital transformation initiatives using evidence-based approaches
- Pursue industry certifications to enhance technical expertise
- Share knowledge through internal training and documentation
Reflection on Learning Approach
What Worked Well:
- Connecting academic learning to real-world banking operations enhanced understanding
- Using BNI London as case study made research tangible and meaningful
- Iterative feedback on literature review and proposal improved quality
- Statistical exercises provided practical skills immediately applicable to work
Challenges Overcome:
- Balancing academic study with full-time work in IT infrastructure
- Developing academic writing style appropriate for postgraduate level
- Time management for completing statistical worksheets alongside other responsibilities
- Adapting to online learning environment and asynchronous collaboration
Key Takeaways:
- Research is a systematic process that can be applied to workplace problem-solving
- Critical evaluation of evidence leads to better decision-making
- Mixed methods approaches provide richer understanding than single methods alone
- Reflective practice enhances both learning and professional development
References
- Barrett, H.C. (2007) 'Researching electronic portfolios and learner engagement', Journal of Adolescent & Adult Literacy, 50(6), pp. 436-449.
- Cambridge, D. (2010) Eportfolios for Lifelong Learning and Assessment. San Francisco: Jossey-Bass.
- Kolb, D.A. (2015) Experiential Learning: Experience as the Source of Learning and Development. 2nd edn. Pearson.
- Moon, J.A. (2004) A Handbook of Reflective and Experiential Learning. London: RoutledgeFalmer.
Project Management and Managing Risk
Week 12 • January 2026
Learning Outcomes
- Define and explain the concept of project management
- Demonstrate an appreciation of project life cycles and methodologies
- Appreciate technologies/software needed to support remote collaboration
- Understand how projects can be impacted by risk and uncertainty
- Prepare a risk management plan
- Appreciate how to control risk and manage project change effectively
This final unit covered project management and risk management within computing projects. While all projects are focused, time-specific, and unique, what they seek to achieve and the environment within which they are implemented can vary greatly. The risk-free project does not exist—many risks are not visible at project start, but significant potential risks must be calculated and planned for.
Project Management Fundamentals
Key Concepts:
- Project Management: Controlling processes and activities related to a project
- Computing Project: Any project dealing with computing infrastructure and information systems
- Project Characteristics: Focused, time-specific, unique, varying environments
- No "one size fits all": Methodology must match project context
Project Life Cycles:
- Initiation: Define scope, objectives, stakeholders, feasibility
- Planning: Develop detailed plans, schedules, resource allocation
- Execution: Implement plans, manage teams, deliver outputs
- Monitoring & Control: Track progress, manage changes, quality assurance
- Closure: Handover, documentation, lessons learned
Project Methodologies:
- Waterfall: Sequential phases, clear milestones, documentation-heavy
- Agile: Iterative, flexible, customer collaboration, responding to change
- PRINCE2: Process-based, controlled stages, defined roles
- Scrum: Sprints, daily standups, product backlog, retrospectives
- Hybrid: Combining elements from multiple methodologies
Risk Management
Risk Fundamentals:
- Risk-free projects do not exist
- Risks cannot be completely avoided
- Many risks not visible at project start
- Significant risks must be calculated and planned
- Risks can pose both threats AND opportunities
Risk Management Process:
- Risk Identification: Brainstorming, checklists, expert judgment, SWOT analysis
- Risk Analysis: Qualitative (probability/impact matrix) and quantitative assessment
- Risk Response Planning: Avoid, transfer, mitigate, accept
- Risk Monitoring: Track identified risks, identify new risks, evaluate effectiveness
Risks, Assumptions, and Constraints:
- Risks: Uncertain events that may impact project positively or negatively
- Assumptions: Factors considered true without proof (should be validated)
- Constraints: Limitations or restrictions on the project (scope, time, budget, quality)
Change Management
Types of Change:
- Corrective Action: Required to keep project on plan and within budget
- Environmental Changes: Project itself needs to be amended
- Requested Changes: Stakeholder or client requests
Change Control Process:
- Change request submission and logging
- Impact assessment (scope, time, cost, quality)
- Review and approval/rejection by Change Control Board
- Implementation of approved changes
- Documentation and communication
- Baseline updates as necessary
Application to BNI Research Project
Identified Research Risks:
- Data Access: Risk of restricted access to operational data → Mitigation: Formal data access agreement secured
- Participant Availability: Staff may be unavailable for interviews → Mitigation: Flexible scheduling, multiple contact attempts
- Data Quality: Inconsistent or incomplete operational records → Mitigation: Data validation procedures, multiple sources
- Scope Creep: Research expanding beyond defined boundaries → Mitigation: Clear scope definition, regular reviews
- Timeline Pressure: Competing work commitments → Mitigation: Realistic scheduling, buffer time
Risk Response Strategies:
- Avoid: Not using customer-identifiable data eliminates privacy risks
- Transfer: University ethics approval transfers compliance responsibility
- Mitigate: Mixed methods design reduces single-source dependency
- Accept: Limited generalisability acknowledged as inherent case study limitation
Project Management Tools for Remote Collaboration
Collaboration Technologies:
- Project Planning: Microsoft Project, Jira, Trello, Asana
- Communication: Microsoft Teams, Slack, Zoom
- Document Management: SharePoint, Google Drive, Confluence
- Version Control: Git, GitHub, Azure DevOps
- Time Tracking: Toggl, Harvest, Clockify
- Risk Management: Risk registers in Excel, specialised tools like RiskWatch
Final E-Portfolio Submission
E-Portfolio Contents Summary:
- Unit 1: Introduction, Codes of Ethics, Reflective Activity 1
- Unit 2: Literature Review, Research Proposals, Literature Review Outline
- Unit 3: Research Methods (Qualitative, Quantitative, Mixed)
- Unit 4: Case Studies, Focus Groups, Observations
- Unit 5: Interviews, Surveys, Cambridge Analytica Case Study
- Unit 6: Quantitative Methods, Descriptive Statistics
- Unit 7: Inferential Statistics, Hypothesis Testing, Literature Review Submitted
- Unit 8: Data Analysis, Visualisation, Charts Worksheet, Proposal Outline
- Unit 9: Validity, Generalisability, Reliability, PowerBI Exercise
- Unit 10: Research Writing, Research Proposal Presentation
- Unit 11: Professional Development, Skills Matrix, Learning Loop
- Unit 12: Project Management, Risk Management
Assessments Completed:
- ✅ Literature Review (30%): Data Science in Banking Digital Transformation - Excellent feedback
- ✅ Research Proposal Presentation (30%): 15-slide audio-narrated presentation with transcript
- ✅ E-Portfolio (40%): Comprehensive 12-unit documentation of learning journey
Statistical Worksheets Completed:
- ✅ Unit 7.1: Summary Measures Worksheet (Mean, Median, Quartiles, IQR)
- ✅ Unit 7.2: Hypothesis Testing Worksheet (T-tests, F-tests)
- ✅ Unit 8: Charts Worksheet (Bar charts, Histograms, Clustered columns)
- ✅ Unit 8: Inference Worksheet Notes
- ✅ Unit 9: PowerBI Dashboard Exercise
Module Completion Reflection
Key Achievements:
- Developed comprehensive understanding of research methodologies
- Completed rigorous literature review on data science in banking
- Designed mixed-methods research proposal for BNI London case study
- Applied statistical methods to real-world datasets
- Created business intelligence dashboards demonstrating practical skills
- Engaged in peer discussions and collaborative learning
Skills Developed:
- Research Design: Qualitative, quantitative, and mixed methods approaches
- Critical Analysis: Evaluating literature, identifying gaps, synthesising evidence
- Statistical Analysis: Descriptive statistics, hypothesis testing, inferential methods
- Data Visualisation: Charts, dashboards, business intelligence tools
- Academic Writing: Literature reviews, research proposals, professional documentation
- Ethical Practice: Research ethics, data governance, informed consent
- Project Management: Planning, risk management, change control
Application to Future Work:
- Research skills directly applicable to MSc Capstone Project
- Statistical methods enhance data analysis capabilities at BNI London
- Project management principles support IT infrastructure initiatives
- Risk management framework applicable to banking system implementations
- Professional development planning guides career progression
References
- APM (2019) APM Body of Knowledge. 7th edn. Association for Project Management.
- Kerzner, H. (2017) Project Management: A Systems Approach to Planning, Scheduling, and Controlling. 12th edn. Wiley.
- PMI (2021) A Guide to the Project Management Body of Knowledge (PMBOK Guide). 7th edn. Project Management Institute.
- Pritchard, C.L. (2015) Risk Management: Concepts and Guidance. 5th edn. Auerbach Publications.
- Schwaber, K. & Sutherland, J. (2020) The Scrum Guide. Scrum.org.