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Business Intelligence and Data Mining Assignment Help

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COMP1615 Business Intelligence and Data Mining Assignment

Learning Outcomes -

LO1. Understand the theoretical underpinnings of BI and DM methodologies, architectures, techniques and algorithms.

Answer: A solid understanding of the theoretical underpinnings of Business Intelligence (BI) and Data Mining (DM) methodologies, architectures, techniques, and algorithms is essential for effectively harnessing the power of data. This includes familiarity with concepts such as data warehousing, data mining techniques (e.g., clustering, classification, association rules), and machine learning algorithms (e.g., decision trees, neural networks, support vector machines). By grasping these theoretical foundations, practitioners can make informed decisions about data collection, storage, analysis, and visualization, leading to valuable insights and informed decision-making. Additionally, a strong theoretical understanding enables individuals to evaluate the suitability of different BI and DM tools and techniques for specific business problems, ensuring optimal results.

LO2. Conduct an audit and analysis of the BI requirements of an organization and contribute to the planning of a BI project as part of a Knowledge Management.

Answer: Conducting a comprehensive audit and analysis of an organization's BI requirements is a critical step in the planning and implementation of a successful BI project. This involves identifying key stakeholders, understanding their data needs, assessing the current state of data management and analytics capabilities, and identifying potential gaps. By conducting a thorough analysis, organizations can develop a clear roadmap for their BI initiatives, ensuring that the project aligns with strategic objectives and delivers maximum value. Additionally, contributing to the planning of a BI project as part of a Knowledge Management initiative helps to integrate data-driven insights into the organization's overall knowledge base, fostering a culture of data-driven decision-making.

LO3. Critically appraise the Business process change requirements, and analyse/design, implement and evaluate the key elements of BI projects, including HCI, Business Reports and other aspects of building a successful BI system.

Answer: Critically appraising business process change requirements and analyzing, designing, implementing, and evaluating the key elements of BI projects is essential for building a successful BI system. This involves understanding how BI initiatives will impact existing processes, identifying potential challenges, and developing strategies to mitigate risks. Additionally, designing and implementing key elements such as Human-Computer Interaction (HCI), business reports, and data visualization tools is crucial for ensuring that the BI system is user-friendly, accessible, and delivers actionable insights. Evaluating the success of a BI project requires ongoing monitoring and analysis of key performance indicators (KPIs), user feedback, and the impact on business outcomes. By effectively addressing these aspects, organizations can maximize the value of their BI investments and drive data-driven decision-making.

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LO4. Critically evaluate and select appropriate DM facilities, algorithms/models and apply them and interpret and report the output.

Answer: Critically evaluating and selecting appropriate data mining (DM) facilities, algorithms/models, and applying them effectively is essential for extracting valuable insights from data. This involves understanding the strengths and limitations of different DM techniques, considering the specific characteristics of the data, and selecting the most suitable algorithms for the task at hand. Applying these techniques requires proficiency in data preparation, model building, and evaluation. Interpreting and reporting the output involves analyzing the results, drawing meaningful conclusions, and communicating the findings in a clear and concise manner. By following these steps, organizations can leverage DM to uncover patterns, trends, and anomalies within their data, enabling them to make informed decisions and gain a competitive advantage.

LO5. Critically appraise the design and implementation of a DM application/technology using test/sample but realistic data sets and modern tools.

Answer: Critically appraising the design and implementation of a data mining (DM) application/technology using test/sample but realistic data sets and modern tools involves a rigorous evaluation process. This includes assessing the appropriateness of the chosen algorithms and models for the specific data and problem, evaluating the quality of the data preprocessing steps, and examining the effectiveness of the implementation in terms of accuracy, efficiency, and scalability. Using realistic data sets helps to ensure that the DM solution is practical and applicable to real-world scenarios. Modern tools, such as Python libraries like scikit-learn and pandas, can streamline the development and evaluation process, providing a robust platform for building and testing DM applications. By conducting a thorough evaluation, organizations can identify potential areas for improvement and optimize the performance of their DM solutions.

LO6. Integrate intelligent and DM elements into a BI systems development project.

Answer: Integrating intelligent and data mining (DM) elements into a BI systems development project can significantly enhance the value and capabilities of the final solution. By incorporating techniques such as machine learning, natural language processing, and predictive analytics, organizations can gain deeper insights, automate processes, and make more accurate predictions. This can lead to improved decision-making, increased efficiency, and enhanced customer satisfaction. When integrating these elements, it is essential to carefully consider the specific business objectives, data availability, and technical expertise within the organization. By effectively integrating intelligent and DM components, organizations can create BI systems that are not only powerful but also adaptable and capable of evolving to meet future needs.

Specification - You are a consultant in a Business Intelligence (BI) solutions company, seeking to identify industry opportunities for novel BI solutions. The company develops BI solutions that incorporate aspects of data warehousing, data mining, and dashboard design. After securing some venture capital, the company wishes to invest in a particular type of business domain and invites your expert advice, submitted in the form of a report which outlines:

A. The industry domain (e.g. retail, financial services, logistics, transport, gaming, health, education, etc.) that you believe the company should tap into, on the basis of current trends, demands and opportunities.

B. A review (based on evidence from the literature and past research) of current Business Intelligence technologies and solutions. This review should also support your chosen domain as a good investment for the future.

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As part of your consultancy, you are also required to develop and demonstrate a BI application (that you will choose to deliver) that will bring benefits to potential clients in the chosen industry area. To that end, your report should also include: The functional design of your application (using suitable diagrams and models of your choice).

C. Data models including data warehouse schemas.

D. A discussion of data mining algorithms that have been used in the prototype and a brief discussion of future approaches that could be used.

E. The actual data presentation (i.e. visualisation) used.

F. A critical discussion on the working prototype of your BI Application.

Deliverables - A brief self-recored video as demonstration (approximately 5-10 minutes) of what you have achieved. In the video, you need to demonstrate the required features of your BI solution and any further features you are planning to do. The minimum requirement is that you need to show the dataset and data schema and discuss answers for the following three questions:

Question 1 - What have you been doing? ( This includes identifying the domain (e.g. sport, retail, financial, health ...etc)).

Question 2 - Why have you implemented this BI solution? (This includes identifying questions you are looking to answer and the main aim of you BI solution).

Question 3 - How did you implement your BI solution? (This includes the tools, software, technologies, data mining algorithms and visualisations you use for implementation).

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If you are going to use PowerPoint slides, you can record your presentation using PowerPoint (just a suggestion), please check this link on how to use PowerPoint recorder. You can also record your screen that displays your database, the tools used for data mining and visualisations while talking through your solution.

A report (a single PDF file) consisting of all the following parts -

A. A short introduction to motivate your chosen domain.

B. A literature review of related work (including competitive solutions). This should include references and brief descriptions of any related work, emphasize any works on the same dataset.

C. Description of the data set that you would like to analyse with a justification of why this data set is worth analysis.

D. An Entity Relationship Diagram (ERD) of the conceptual design and the relational schema developed to store the data in a data warehouse schema.

E. Discussion and evaluation. Write between 400 and 500 words evaluating the system that you have produced. Be specific and justify any statements you make. Just saying things like 'my system is well designed" without justifying the statement will not gain you any marks. Your evaluation should include, but need not be limited to, the following aspects of your system:

i. Data Quality Extraction, Transformation and Loading process (ETL)

ii. Data warehouse design

F. Conclusions and any directions for future work

H. Appendix containing any code you used

H. A self-assessment form

Requirements - You can assume that any operational data is stored in a relational format and so your data warehouse must also do the same. You can use ORACLE, MySQL, or SQL Server to store the data. The requirements are such that you must provide some data mining features on some of the data. This can be integrated using R (or Weka). Finally, you must provide a dashboard for your BI tool. This dashboard can be created in any tool you wish. Special consideration will be given to visualisation solutions that are web accessible (i.e. those that are hosted and serve HTML5 for example). There are 3 marks going for web enabled visualisations.

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