This module is not currently running in 2026 to 2027.
In today's business context, there is a wealth of data available but many businesses currently struggle to transform that data into useable information to businesses can use to make meaningful decisions. We'll learn to extract insights from historical data through interactive visual analytics, communicating complex information effectively and gain hands-on experience in using data to drive positive change in business, environment and society. We'll also explore advanced techniques for pattern recognition, and forecasting, providing the ability to foresee future outcomes and trends. We'll explore real-world case studies from diverse industries, offering a deep understanding of how data analytics is integral to sectors like supply chain management, marketing, healthcare, and finance. By the end of this module, you'll be equipped with the skills and knowledge to transform data into actionable insights and predictions, making an impact on any organziation that you work at.
Lecture 16, PC Lab 24, Independent Study 105, Assessment Preparation 55
Individual Report (1000 words) worth 30%.
Individual Report (2500 words) worth 70%.
Reassessment Method: 100% Written Assessment – Individual Report – 2,500 Words
On successfully completing the module, students will be able to:
1. Understand and implement systematic approaches to leverage data for improving business performance.
2. Create impactful visualizations and dashboards for data-driven storytelling tailored to business audiences
3. Use and evaluate participatory methods in identifying data requirements, structuring complex problems, and ensuring stakeholder uptake of data intelligence solutions.
4. Understand the main methods and predictive analytic techniques to handle a variety of business problems, offering innovative solutions that drive informed decision-making and foster a competitive edge in the business landscape.
5. Apply regression analysis and forecasting techniques to characterise relationships among business variables, identify patterns in data, predict future trends, and communicate these insights effectively to guide strategic decision-making.
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