Course overview
This course aims to provide a comprehensive understanding of data science and its applications in business, covering topics such as data pipeline, ethics, descriptive and visual analytics, data wrangling, statistical inference, supervised and unsupervised learning, and deep learning. Through exploration of the value of data science in modern business, considerations of data security and ethics, and hands-on exercises in data analysis techniques, students will gain the skills necessary to harness the power of data, make informed decisions, and drive business success. By delving into various facets of data analytics, including generative AI, regression analysis, and deep learning, students will be equipped to tackle real-world challenges and leverage data-driven insights to optimise business strategies and operations.
Course learning outcomes
- Explain the nature and role of business intelligence and analytics in decision-making, actionable insights, and contributing to the delivery of business value and competitive advantage in modern organisations.
- Apply the CRISP-DM framework in a realistic analytic task.
- Compare and evaluate key data analytics methods, be able to discuss the pros and cons of those methods; apply this knowledge in writing analytic plan.
- Compare and evaluate key data analytics methods, be able to discuss the pros and cons of those methods; apply this knowledge in writing analytic plan.
- Summarise the results and recommendations in a report and defend the report in discussion.
- Use modern analytics software tools to solve real world problems.