The "Time Series Data Analysis" module is designed to provide students with a deep understanding and analytical skills in time series data. This module will guide students through the fundamental concepts, theories, and practical applications of time series data in various fields. By the end of this course, students will be able to identify and analyze patterns and trends in time series data and use statistical methods and machine learning techniques for forecasting.
A. Demonstrate a thorough understanding of the fundamental concepts and analysis methods of time series data, such as trends, seasonality, and autocorrelation, as well as techniques for data visualization and decomposition. B. Use statistical software and programming languages, such as R or Python, to perform time series analysis, demonstrating proficiency in implementing models and interpreting results. C. Apply statistical and machine learning techniques, such as ARIMA, exponential smoothing, and neural networks, to analyze and forecast time series data, evaluating model performance and accuracy. D. Applying time series analysis techniques to real-world case studies and practical exercises and deriving actionable insights.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. The module will be delivered in a combination of lectures, seminars and labs. Lectures will introduce students to the academic content. Seminars and labs will be used to expand the students understanding of lecture materials. In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading. This module will utilize open-source artificial intelligence projects in conjunction with course content to help students achieve better learning outcomes. By integrating advanced AI technologies, we aim to improve the efficiency of teaching and interaction, while fostering greater student autonomy and flexibility in learning.