The module explores and investigates the critical theoretical concepts in the discipline of data science, starting from data collection and then gradually leading to the stage where data is transformed into knowledge to support business decisions. This involves equipping me with the practical skills required to be able to analyse data sets, design and implement a solution using suitable programming languages, such as Python. The module is designed not only to equip me with an understanding of concepts and key knowledge, but also to make me aware of the ethical and professional responsibilities of the data science professional. The module introduces the historical, architectural and practical perspectives of the computer science discipline, and also enables me to engage with, experience and envision the current, as well as future, developments in data science.

 

Learning Outcomes

I will be able to:

  • Demonstrate a critical awareness of roles and responsibilities (legal, ethical, social and professional) of a Data Science professional
  • Critically analyse architecture, design, development methodology, querying and the lifecycle of managing large-scale datasets
  • Distinguish between and critically reflect on the solutions of various data analytics approaches which support business decision making process
  • Apply and evaluate critically the various methods, tools, technologies and success factors applied to a data science project in order to develop an effective plan and delivery of solutions to a business problem

Artefacts and Feedback

Click bold text to open up project

  • Collaborative Discussion 1 Summary: Discussion on the emergence of data science as a subject area, data as the main driver and engine for data science, Artificial Intelligence and Cybersecurity while highlighting the opportunities, limitations, and challenges.
    •  

    While the feedback i received applauds my analytical depth and engagement, it also points towards areas needing further exploration, particularly in addressing and offering solutions to identified challenges. To enhance my project, next time i will consider incorporating a deeper exploration of data handling challenges, including bias and anomaly detection, with potential solutions or mitigation strategies, a discussion on industry standards in data science for cybersecurity and how to address the lack of such standards and consideration of privacy challenges in IoT and how these can be balanced against global anomaly detection needs.

     
  • Collaborative Discussion 2 Summary: Review your (or an) organisation’s IT Code of Conduct.
    •  

    Reflecting on the feedback received, I realized the importance of clarity and depth in discussing complex topics. The feedback helped me refine my arguments and provided new perspectives on how to address online confidentiality and consent issues. This process highlighted the value of peer contributions in enhancing understanding and improving professional practices.

     
  • Data Analytics Report: Analyzing the 2013-14 National Survey for Wales
    •  

    The report demonstrated a solid understanding of the transportation system in Wales and identified relevant datasets. The organization and presentation of the report were clear and structured. There was a need for more detailed analysis of the dataset and modeling of data requirements. The report could have included more charts and graphs to highlight patterns and correlations within the data.

     
  • End of Module Assignment - Data Analytics Implementation: Undertake the analysis according to your chosen UML design using Python libraries to map up your UML implementation
    •  

    The work is deemed unsatisfactory due to poor referencing and a weak connection between the analysis and implementation reports. More references are needed to support the research and enhance the credibility of the work.

Reflections and Meeting Notes

Click bold text to open up project

  • Collaborative Discussion 1 Summary: Throughout this project on the convergence of data science, AI, and cybersecurity, I gained comprehensive insights into the evolving landscape of technology and its implications. My learning journey was structured around analyzing various scholarly articles, synthesizing opinions, and engaging in discussions with peers. This process enlightened me on the multifaceted roles of data as the cornerstone of technological advancement and the ethical considerations inherent in its manipulation.
    •  
  • Collaborative Discussion 2 Summary: In my final post, I incorporated the feedback by providing more detailed examples and additional references. I agreed with peers that confidentiality and consent can be achieved online with proper measures and elaborated on how MDM and quality training can enhance the IT Code of Conduct. I also emphasized the role of feedback in improving business practices, citing Meyer-Kalos et al. (2024) and Gussek et al. (2024) on the benefits of client feedback.
    •  
  • Data Analytics Report: I was puzzled by the need for more charts as this was an initial report before the analysis, i felt focusing on my SQL database and understanding the problem was the main focus. Though, after reflection, it makes sense to do basic analysis at this stage. Overall, the effort was satisfactory but could benefit from deeper analysis and better data representation with use of graphs.
    •  
  • End of Module Assignment - Data Analytics Implementation: The feedback highlighted areas where my work met expectations and areas needing improvement. I demonstrated a satisfactory understanding of the context but failed to integrate sufficient references, weakening the connection between my analysis and implementation. The presentation was acceptable, but the limited use of references affected the overall quality. I was suprised by this due to several references being used in my report. Though i can see where i need to use more references in my analysis adn conclusion to potentially streghten it. This was humbling as i felt my project was done well and grew from my data analytics report, but all i can do is improve and push to not make the same mistake in the remainder of the masters

Professional Skills Matrix learnt and Action Plan

 

Skills Gained or Enhanced:

  • Analytical Skills: Enhanced ability to dissect and understand complex information from various sources, identifying underlying patterns and implications.
  • Critical Thinking: Improved capacity to evaluate arguments, identify biases, and consider ethical implications, fostering a balanced and informed perspective.
  • Collaboration: Strengthened ability to engage with peers, absorb diverse viewpoints, and integrate feedback constructively.
  • Communication: Refined my ability to articulate complex ideas clearly and persuasively, both in written form and during discussions.
  • Enhancing IT Code of Conduct: Implementing detailed best practices and MDM to ensure data protection and efficient communication.
  • Improving Client Relations: Using client feedback to identify areas for improvement and building trust through transparent and responsive practices.
  • Python Programming: Strong skills in data pre-processing, database integration, and visualization using Python libraries.
  • SQL: Proficient in using SQL for data management and querying.
  • Report Writing: Demonstrated satisfactory report writing skills but need to improve referencing and clarity.

Action Plan:

  • Continuous Learning: To keep abreast of the latest developments in data science, AI, and cybersecurity, I plan to regularly read scholarly articles and industry reports. This will ensure my knowledge remains current and comprehensive.
  • Ethical Considerations: Integrate ethical considerations into all future projects by adopting frameworks and guidelines that emphasize fairness, transparency, and accountability in data handling and AI applications.
  • Networking and Collaboration: Engage more actively in professional networks and forums related to data science and cybersecurity. This will enhance my collaborative skills and provide access to a broader range of perspectives and insights.
  • Communication Skills: Continue to refine my ability to communicate complex ideas effectively by seeking opportunities for public speaking and writing, such as conferences, workshops, or blog posts.
  • Improve Referencing: Enhance the use of academic references in future projects. Conduct a thorough literature review. Integrate relevant references to support arguments. Ensure proper citation of all sources.