Data Science: Extented Responses

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Discuss how enterprise computing systems meet the needs of different types of enterprises through data science applications.

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Enterprise computing systems support different types of enterprises by collecting, analysing, and using data to improve operations. For example, a retail business can use sales data to understand customer preferences and manage stock levels. A logistics company might use real-time tracking data to improve delivery routes. Data science enables these systems to process large volumes of data quickly, find patterns, and support decision-making. By using predictive analytics, enterprises can also forecast trends and make strategic plans.

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Evaluate how the use of innovative technologies, such as AI and machine learning, has improved enterprise computing systems.

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Innovative technologies like AI and machine learning have transformed enterprise computing by automating processes, improving accuracy, and enabling smarter decisions. For example, chatbots powered by AI provide 24/7 customer support, while machine learning models predict customer behaviour and improve marketing strategies. These technologies reduce human error and save time, though they may also raise concerns about job displacement and ethical use. Overall, the benefits in efficiency and data handling often outweigh the challenges when implemented responsibly.

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Explain the importance of securely collecting, storing, and manipulating data in enterprise systems.

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Secure data practices are critical in enterprise systems to protect sensitive information, maintain customer trust, and comply with legal requirements such as the Privacy Act. Data breaches can result in financial loss and damage to reputation. Enterprises use encryption, access control, and regular audits to ensure data security. Manipulating data correctly (e.g., cleaning and validating data) also ensures it is accurate and useful for analysis. Overall, these steps ensure the integrity and reliability of the system.

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Analyse the role of data in supporting decisions in an enterprise computing system.

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Data plays a central role in enterprise decision-making by providing evidence-based insights. Data collected from sales, customer interactions, and operations helps enterprises understand trends, identify problems, and test solutions. Using tools like dashboards or predictive models, managers can make informed decisions that improve performance and profitability. For instance, real-time analytics might show which products are selling well, helping businesses adjust marketing efforts accordingly.

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Justify the use of data visualisation tools in the development of enterprise computing systems.

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Data visualisation tools like Tableau or Power BI are essential in enterprise computing systems as they make complex data easier to understand. They allow users to see patterns and trends through charts and graphs, which supports faster and better decision-making. These tools also help non-technical staff interpret data, making the system more user-friendly and improving collaboration across teams. Their visual nature also aids communication with stakeholders and clients.

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Explain how social, ethical, and legal issues affect the design of enterprise systems using data science.

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Enterprise systems must consider privacy laws (legal), data misuse or discrimination (ethical), and public concerns (social). For example, using biased data in hiring algorithms can lead to unfair outcomes. Legally, systems must follow the Australian Privacy Principles. Socially, users expect transparency about how their data is collected and used. Failing to address these issues can damage an organisation’s reputation and lead to legal penalties.

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Evaluate the effectiveness of using cloud computing for storing enterprise data.

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Cloud computing is effective for storing enterprise data due to its scalability, cost-efficiency, and accessibility. Enterprises can store large datasets without needing expensive infrastructure. It also allows remote access, improving collaboration. However, concerns include data security, internet dependence, and compliance with data laws. When managed well with proper encryption and backups, the advantages generally outweigh the drawbacks.

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Explain how enterprise systems can be designed to meet the needs of different audiences.

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Enterprise systems should be user-friendly, accessible, and customisable to meet different user needs. For example, dashboards for managers should show KPIs, while staff may need interfaces for data entry. Using user feedback, prototyping, and interface testing can ensure the system suits various users. Designing with accessibility in mind ensures inclusivity for users with disabilities or language needs.