| CODE | SOR3213 | ||||||
| TITLE | Foundations of Data Analysis and Statistical Learning Methods | ||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||
| EQF/MQF LEVEL | 6 | ||||||
| ECTS CREDITS | 5 | ||||||
| DEPARTMENT | Statistics and Operations Research | ||||||
| DESCRIPTION | The study-unit will provide foundations to core statistical concepts and methods. It will cover the following topics: - Familiarisation with R programming language; - Descriptive Statistics and Data Illustrations; - Hypothesis Testing; - Regression Models; - Generalised Linear Models; - Introduction to Bayesian Statistics; - Clustering Analysis Techniques; - Supervised Statistical Learning Methods, including Artificial Neural Networks, Tree-Based Learners, and Support Vector Machines; - Time Series Analysis and Recurrent Neural Networks. Study-Unit Aims: - To introduce students to core statistical concepts and data analysis techniques, including descriptive statistics, hypothesis testing, regression models, and generalized linear models; - To develop student proficiency in the R programming language as a primary tool for data manipulation, visualization, modeling, and reproducible analysis, supported by hands-on exercises using real-world datasets; - To build foundational understanding of Bayesian statistical inference, including the formulation of priors, likelihoods, and posterior distributions for data-driven decision-making; - To equip students with practical skills in supervised statistical learning, including artificial neural networks, tree-based models, support vector machines, and ensemble learning methods such as bagging, boosting, and random forests; - To introduce core unsupervised learning approaches, including clustering techniques and key concepts in dimensionality reduction; - To provide an introductory understanding of time series analysis, covering classical statistical models and modern deep-learning approaches such as recurrent neural networks, with emphasis on applied implementation. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain the core concepts of descriptive and inferential statistics, including the theoretical basis of common statistical tests; - Recognize key conceptual features of R for statistical computation and data analysis; - Interpret statistical outputs conceptually and explain their meaning in context; - Describe the principles of Bayesian inference, including prior, likelihood, and posterior relationships; - Explain the fundamentals of various statistical learning methods and their roles within supervised and unsupervised learning; - Discuss key time series concepts and classical forecasting methods. 2. Skills: By the end of the study-unit the student will be able to: - Manipulate, clean, and visualize datasets in R using reproducible workflows; - Select, apply, and justify appropriate statistical tests, regression approaches, and generalized linear models for real-world data; - Implement and interpret Bayesian inference procedures in R, including updating beliefs and analyzing posterior distributions; - Build and evaluate supervised learning models, including decision trees, ensemble methods, support vector machines, and neural networks; - Apply clustering algorithms and dimensionality-reduction techniques to explore and summarize complex datasets; - Conduct time series analysis and forecasting, including fitting classical models (e.g., ARIMA) and implementing recurrent neural networks for sequential data; - Perform hyper-parameter optimization of the different statistical learning methods, where relevant. Main Text/s and any supplementary readings: View reading list |
||||||
| STUDY-UNIT TYPE | Lecture | ||||||
| METHOD OF ASSESSMENT |
|
||||||
| LECTURER/S | Monique Borg Inguanez Mark A. Caruana Fiona Sammut Monique Sciortino David Paul Suda |
||||||
|
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2026/7. It may be subject to change in subsequent years. |
|||||||