Assignments & Meeting Notes

Assignment 1: Summative Collaborative Discussion

The first assignment involves a discussion forum spanning over three weeks.

Objective: Critically evaluate the paradigm shift towards the convergence of data science, AI, and cybersecurity, drawing on Hero et al. (2023) and Tebout (2021):

  • Analyse the role of data as the central driver for innovation in AI and cybersecurity.
  • Reflect on the implications of emerging trends for data management, security, and enterprise strategy.
  • Demonstrate critical awareness of the ethical, social, and professional responsibilities in managing data pipelines and large-scale datasets.

Key Deliverables:

  • Five forum posts; Initial post, three peer responses and one Summary post

Reflection: This assignment helped me understand the strategic role of data as both an asset and a vulnerability in digital ecosystems. By critically engaging with Hero et al. (2023) and Tebout (2021), I realised that while convergence creates new possibilities for intelligent, proactive cybersecurity, it also magnifies challenges in scalability, bias, compliance, and explainability. Through peer engagement, I recognised the importance of collaboration and multiple perspectives in evaluating emerging technologies—what looks like an opportunity from one perspective (automation, speed) can appear as a risk from another (loss of control, overreliance on AI). Overall, the activity strengthened my ability to analyse complex socio-technical systems, to balance innovation with governance, and to reflect on my role as a future data professional who must manage legal, ethical, and professional responsibilities when designing and operating data pipelines.

Grade: Merit

Assigment 2: London Road Traffic Data Analysis

Objective: Critically evaluate the London road traffic statistics to understand congestion patterns, current challenges, and potential analytical approaches. Design a strategic framework for managing and analyzing large-scale traffic data using an EDA model, preparing the foundation for future analytics in Assignment 3. Explore the role of Business Intelligence (BI) tools and enterprise systems in processing multi-source datasets and supporting informed decision-making.

Steps:

  • Review and critically assess the London road traffic statistics report, focusing on congestion and related issues.
  • Research and evaluate EDA models suitable for large-scale traffic datasets.
  • Examine data architecture options, including data warehouses, data lakes, and cloud solutions, and assess their suitability for enterprise-scale traffic data.
  • Develop a rationale for design choices, including table selection, concatenation approaches, and analytics tool adoption, considering stakeholder needs and business objectives.

Key Deliverables:

  • Documented EDA framework tailored to traffic data, detailing intended analytical approaches
  • Proposed enterprise data architecture, justifying the use of warehouses, lakes, or cloud solutions.
  • Rationale report explaining design choices for data tables, integration methods, and analytics tools, with reference to business and stakeholder requirements.

Reflection: Through Assignment 2, I strengthened my ability to strategically plan enterprise-level data solutions by evaluating optimal architectures and selecting suitable analytical tools without yet performing the analysis. I gained critical insight into Business Intelligence systems, including reporting platforms, dashboards, and EDA models, and how they support effective decision-making and data visualization. The assignment reinforced the importance of aligning data strategies with business objectives, considering stakeholder requirements, resource efficiency, and the potential to generate actionable insights. Additionally, it enhanced my professional development by building confidence in assessing large-scale datasets, designing end-to-end analytical strategies, and clearly documenting the rationale for each design choice. This preparation laid a solid foundation for implementing the data analysis in Assignment 3, ensuring that all strategic, architectural, and compliance considerations are fully integrated.

Grade: Merit

Assigment 3: Enterprise Data Report - Executive Summary and Implementation

Building upon the design from Assignment 2, this task focuses on applying the data analysis strategy developed in Assignment 2

Objective: Generate actionable insights on congestion patterns, traffic trends, and anomalies. Utilize Business Intelligence tools and EDA frameworks to communicate findings effectively to stakeholders

Steps:

  • Perform data cleaning, normalization, and integration across multiple traffic data sources.
  • Apply exploratory data analysis techniques to identify congestion hotspots, temporal trends, and anomalies.
  • Develop interactive dashboards and visualizations using Python libraries and BI tools..
  • Document methodology, rationale, and insights for stakeholder communication and future reference.

Key Deliverables:

  • Interactive dashboards and visualizations demonstrating key traffic insights.
  • Documentation of analytical methodology, model evaluations, and strategic recommendations.

Reflection: In Assignment 3, I implemented the enterprise data analysis strategy developed in Assignment 2 on the London road traffic dataset. I performed end-to-end data processing, including cleaning, transformation, and integration of multiple traffic data sources, ensuring high-quality inputs for analysis. Using an EDA framework, I explored congestion patterns, temporal trends, and anomalies, applying statistical techniques and visualizations to extract actionable insights. I developed interactive dashboards using Power BI, leveraging skills I had gained during my bachelor’s thesis, which allowed me to communicate complex traffic data effectively to stakeholders. Throughout the assignment, I critically evaluated analytical models and visualization approaches, optimizing both resource efficiency and clarity of insights. This project strengthened my ability to manage large-scale datasets, implement data pipelines, and deliver business-relevant analytics, consolidating both theoretical strategies and practical skills in enterprise data science.

Grade: Distinction

Meeting Notes

Week 5–7 Discussion Forum – Digital Transformation

Posted an initial evaluation of the article (200+ words), highlighting the role of DXPs, organizational culture, and implementation risks. Responded to three of my peers and summararized perspectives in a final post.

Unit 6: Formative Evaluation Task – Data Valuation Model

Task: Critically evaluate A Review of Data Valuation Approaches and Building and Scoring a Data Valuation Model (Fleckenstein et al., 2023).

Action: Assessed dimensional, market-based, and economic valuation models; identified strengths, limitations, and practical applicability for organizational decision-making.

Unit 12: Discussion Forum – ERP Conceptual Model

Designed an ERP conceptual model and commented on model enhancements for ERP 2.0 including hybrid data storage, real-time analytics, collaborative processes, BI integration, and compliance mechanisms.

To see further work I produced in this module please jump to my github.

Reflection & Action Plan

Reflection

This module was generally not too difficult for me, as I had prior experience through my bachelor’s thesis on reporting systems. This prior exposure allowed me to quickly grasp concepts underlying Business Intelligence tools, data integration, and reporting dashboards. I was able to build on my existing knowledge to critically evaluate data pipelines, enterprise architectures, and exploratory data analysis frameworks for managing complex datasets like the London road traffic statistics. The module helped reinforce the importance of aligning data preparation and analysis with stakeholder objectives and business goals. Through the practical application of BI tools and dashboard development, I strengthened my ability to communicate insights effectively to different audiences, combining visualization, statistical analysis, and narrative. I also gained a deeper appreciation for data compliance, governance, and the ethical responsibilities inherent in managing enterprise-scale datasets. Overall, the module enhanced my professional development ethos in data science, particularly in the areas of critical evaluation, system management, and stakeholder-focused analytics, while reinforcing skills I had previously cultivated during my bachelor’s thesis.

Action Plan

Develop Advanced Data Manipulation Skills
Enhance Data Visualization & Storytelling
Explore Real-World Datasets
Apply Learnings to Professional Development

Learnings Acquired

Stakeholder-Centric Data Management: Gained insight into aligning data sources, preparation methods, and analysis outputs with business objectives and stakeholder needs.

Data Pipeline Expertise: Developed critical awareness of the lifecycle of large-scale datasets, including ingestion, cleaning, transformation, storage, and visualization within BI systems.

Enterprise System Design:Learned to evaluate the architecture, tools, and technologies needed to manage and analyze complex traffic datasets effectively.

Critical Application of EDA: Applied statistical and visualization techniques to identify congestion patterns, trends, and anomalies, demonstrating the ability to support business decision-making with data-driven evidence.

Ethical, Legal, and Professional Awareness: Strengthened understanding of compliance requirements, data governance policies, and ethical considerations in managing traffic and mobility datasets.

Analytical Communication: Enhanced the ability to communicate complex insights through dashboards, reports, and visualizations that inform strategic and operational decisions.Demonstrated ability to create comprehensive theoretical concepts for Configuration Management Databases tailored to organizational needs. Skilled in developing data models, identifying configuration items (CIs), and defining relationships and attributes.