DAT7001 : Data Handling and Decision Making - Analyse Methods of Auditing Data Holdings - Computer Science Assignment Help

Assignment Task :

Assignment Brief
As part of the formal assessment for the programme you are required to submit a Data Handling and Decision Making report. Please refer to your Student Handbook for full details of the programme assessment scheme and general information on preparing and submitting assignments.

Learning Outcomes (LO):
After completing the module, you should be able to:
1. Analyse methods of auditing data holdings and gap identification.
2. Critically analyse theoretical and core processes of data manipulation and mining.
3. Utilise and evaluate basic statistical concepts.
4. Appreciate ethical issues and their importance to data handling and decision making.
5. Develop a practical ability with data analysis and data mining methods to analyse and interpret data sets.
6. Make recommendations based upon the findings from data analysis.

Assignment Part 2: Data-Driven Decision Support
Data-driven decision support encompasses a range of the most essential processes of data analytics, including data preparation and integration, modelling using statistical and/or machine learning techniques, and data presentation. The aim of this activity is to empower the organisational decision-making with statistically tested and systematically evaluated decision options. These options can be ranked using inferential models, such as forecasting, prediction and/or classification.
In Part 1 of this assignment, you have identified a case study – an organisation or project of your choice, as well as various data sources and datasets available to it, and an important organisational decision. In this part, you have an opportunity to demonstrate how this specific decision formulated in Part 1 in the context of your chosen case study can be supported with data analytics.

Task 2.1
Discuss data preparation process, including
• Explanation of data collection, filtering and integration procedures.
• Analysis of data representativeness.
• Statement on generalisability and limitations of the integrated dataset.
 
Task 2.2
Perform data modelling, which should specifically demonstrate:
• Selection and justification of the inferential and/or machine learning models, most relevant to the objectives of your case study.
• Application of statistical tools such as Excel, SPSS and/or Weka, to your model and reporting on the initial outcomes of your modelling.
• Explanation what the decision in question should be, based on these outcomes.

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