Statistics (Theory and Methods) MSc

Program Overview

The MSc Statistics (Theory and Methods) at Imperial College London provides students with a comprehensive understanding of statistical theory and methods. Core modules cover applied statistics, computational statistics, and probability for statistics. Students select optional modules from various fields, including statistical finance, biostatistics, and data science, tailoring their studies to their interests. The program culminates in an extensive research project, where students apply their knowledge to solve real-world problems. Graduates are highly sought after in various sectors, including banking, finance, IT, technology, and education.

Program Outline

Degree Overview:

• Objective: To provide students with a deep understanding of statistical methods and their underlying theory.
• Description: This MSc program offers training in both theoretical and applied statistics, exploring how statistical reasoning and methods are used across various employment sectors.
Students will conduct an independent research project, applying research techniques learned throughout the course.
• Specialization: This is one of six Statistics streams available at Imperial.
Students may also consider the General stream or specialist streams in Applied Statistics, Biostatistics, Data Science, and Statistical Finance.

Outline:

• Core Modules:
• Applied Statistics:
Examines statistical modeling and regression, applying it to real-world problems and data.
• Computational Statistics: Covers computational methods crucial in modern statistics, teaching students to implement and apply these methods confidently.
• Fundamentals of Statistical Inference: Explores Bayesian and frequentist approaches to statistical inference, enabling students to select and justify appropriate methods for hypothesis testing.
• Probability for Statistics: Assesses key concepts of probability theory, teaching students how to define random variables, vectors, and their distribution functions.
• Optional Modules:
• Group A (Choose at least two modules):
• Advanced Simulation Methods:
Provides a basic understanding of simulation, its uses, limitations, and applicable models.
• Bayesian Methods: Compares frequentist and Bayesian approaches to statistical analysis, teaching students to apply appropriate Bayesian data analyses.
• Multivariate Analysis: Introduces multivariate analysis, exploring concepts like covariance matrix and multivariate normal distribution.
• Nonparametric Statistics: Evaluates the strengths and weaknesses of nonparametric methods as flexible alternatives to parametric modeling.
• Biostatistics: Teaches students how to analyze biomedical data and design and analyze clinical trials.
• Data Science: Familiarizes students with common data scientific methods and their uses and misuses.
• Deep Learning (7.5 ECTS): Explores the building blocks of deep learning models and how to design network architectures for specific applications.
• Introduction to Statistical Finance: Explores fundamental concepts of quantitative finance and statistical methods used to analyze financial data.
• Mathematical Foundations of Machine Learning: Covers the mathematical foundations and practical applications of machine learning and deep learning algorithms.
• Statistical Genetics and Bioinformatics: Emphasizes handling high-dimensional datasets through the analysis of genetics and bioinformatics data.
• Stochastic Processes: Studies stochastic processes and tools from stochastic analysis that provide the mathematical foundations for option pricing theory.
• Survival Models (7.5 ECTS): Explains the importance of survival models in actuarial work and explores key actuarial applications.
• Time Series Analysis (7.5 ECTS): Examines discrete time stochastic processes and associated computational algorithms and approaches.
• Statistics Research: Students complete an extensive research project full-time between May and September, focusing on statistical theory and methods.
This includes working with a faculty member on a state-of-the-art research problem aligned with their interests.

Assessment:

• Modules: 67% of the overall assessment is based on modules.
• Research Project: 33% of the overall assessment is based on the research project.
• Assessment Methods:
• Assessed coursework/tests
• Enhanced coursework assessments
• Oral presentation
• Written examinations
• Written project

Teaching:

• Methods:
• Lectures
• Tutorials
• Practicals
• Modern statistical computing skills
• Oral presentation and assessment
• Practical computational sessions
• Problem classes
• Research seminars
• Virtual learning environment
• Faculty: Students benefit from dedicated one-to-one support from world-class faculty and the Statistics Clinics or the Centre for Doctoral Training in Modern Statistics and Machine Learning.
• Unique Approaches:
• Hands-on, dynamic, and unconventional teaching and learning.
• Focus on in-person problem-solving activities and classes, including summer research project poster presentations.

Careers:

• Potential Paths:
• Further study of statistics
• Career in statistical finance
• Opportunities:
• Graduates are highly sought after in various sectors, including banking and finance, accountancy, education, IT, and technology.
• Skills Developed:
• Programming
• Problem-solving
• Critical thinking
• Scientific writing
• Project work
• Presentation

Other:

• Accreditation: The MSc in Statistics is accredited by the Royal Statistical Society.
Graduates can apply for the Graduate Statistician professional award.
• Industry Events: Careers in Statistics Events bring industry experts from various application areas, including AI, finance, pharma, and sports.
• Mentoring and Supervision: Students receive supportive and insightful mentoring and supervision from world-class faculty.
• Computing Skills: Students become fluent in common data science computing languages like Python, R, Pyspark, Stan, and TensorFlow.
• Summer Research Project: Students can shape their careers by choosing from over 15 elective modules in term 2 and participating in the summer research project.

| Home fee | Overseas fee | | ----------- | ----------- | | £20,500 | £34,350 |