Statistics (Data Science) MSc

Program Overview

The MSc Statistics (Data Science) program at Imperial College London provides a comprehensive understanding of data science techniques and their applications. Students gain proficiency in modern statistical computing languages, develop critical thinking and problem-solving skills, and complete a substantial research project. The program prepares graduates for careers as data scientists, statisticians, and other data-driven professionals in various industries. Graduates are well-equipped to pursue further study or enter a rewarding career in data science.

Program Outline

Degree Overview:

• Objective: This MSc program aims to equip students with a comprehensive understanding of data science techniques and their applications in various settings, including scientific, governmental, industrial, and commercial environments.
• Program Description: The program provides a thorough exploration of statistical methods used in data science, covering both theoretical foundations and practical applications.
Students will gain proficiency in modern statistical computing languages and learn to implement and apply these methods confidently. The program also emphasizes the development of critical thinking, problem-solving, and communication skills, preparing students for further study or a career in data science.
• Accreditation: The MSc in Statistics is accredited by the Royal Statistical Society, allowing graduates to apply for the professional award of Graduate Statistician.

Outline:

• Program Structure: The program is delivered full-time over one year and consists of core modules, optional modules, and a substantial research project.
• Core Modules:
• Applied Statistics:
This module focuses on statistical modeling and regression, applying these concepts to real-world problems and data.
• Computational Statistics: Students will engage with computational methods essential in modern statistics, learning to implement and apply them effectively.
• Fundamentals of Statistical Inference: This module explores Bayesian and frequentist approaches to statistical inference, enabling students to select and justify appropriate methods for hypothesis testing.
• Probability for Statistics: This module covers key concepts of probability theory, including random variables, vectors, and their distribution functions.
• Optional Modules: Students choose two optional modules from a list of over 15 options, each worth 5 ECTS unless otherwise stated.
Some modules are worth 7.5 ECTS. The available modules include:
• Advanced Simulation Methods
• Advanced Statistical Finance
• Bayesian Methods
• Big Data
• Biostatistics
• Data Science
• Deep Learning (7.5 ECTS)
• Introduction to Statistical Finance
• Mathematical Foundations of Machine Learning
• Multivariate Analysis
• Nonparametric Statistics
• Statistical Genetics and Bioinformatics
• Stochastic Processes
• Survival Models (7.5 ECTS)
• Time Series Analysis (7.5 ECTS)
• Statistics Research: The program culminates in a full-time research project undertaken between May and September.
Students explore an area of data science, working with a faculty member on a cutting-edge research problem aligned with their interests.

Assessment:

• Assessment Methods: The program utilizes a combination of assessment methods, including:
• Assessed coursework/tests
• Enhanced coursework assessments
• Oral presentations
• Written examinations
• Written projects
• Assessment Weighting:
• 67% of the overall assessment is based on modules.
• 33% of the overall assessment is based on the research project.

Teaching:

• Teaching Methods: The program employs a variety of teaching methods, including:
• Lectures
• Tutorials
• Practicals
• Problem classes
• Research seminars
• Virtual learning environment
• Modern statistical computing skills sessions
• Oral presentation and assessment
• Practical computational sessions
• Faculty: The program is taught by world-class faculty members who provide dedicated one-to-one support through Statistics Clinics and the Centre for Doctoral Training in Modern Statistics and Machine Learning.
• Unique Approaches: The program emphasizes hands-on learning, dynamic and unconventional teaching, and collaborative learning experiences.
Students are encouraged to build a career network and engage directly with faculty.

Careers:

• Potential Career Paths: Graduates of the MSc Statistics (Data Science) program are well-prepared for a wide range of careers in data science, including:
• Data Scientist
• Statistician
• Machine Learning Engineer
• Data Analyst
• Quantitative Analyst
• Actuary
• Research Scientist
• Opportunities: The program provides students with highly transferable skills, including programming, problem-solving, critical thinking, scientific writing, project work, and presentation, making them highly sought after in various sectors, including:
• Banking and finance
• Accountancy
• Education
• IT and technology
• Healthcare
• Government
• Research
• Outcomes: The program aims to equip students with the knowledge, skills, and experience necessary to succeed in a data-driven world.
Graduates are well-positioned to pursue further study or enter a rewarding career in data science.

Other:

• Program Highlights:
• The program offers a wide range of elective modules, allowing students to tailor their studies to their specific interests and career goals.
• Students have the opportunity to participate in Careers in Statistics Events, where they can network with industry experts from various application areas, including AI, finance, pharma, and sports.
• The program provides a supportive and inclusive learning environment, with no dress codes or other formal barriers.
• Program Focus: The program emphasizes in-person problem-solving activities and classes, such as summer research project poster presentations.
• Program Goal: The program aims to help students shape their own careers in the future of statistics.

• Home fee: £20,500
• Overseas fee: £34,350

Payment Terms:

• Students can choose to pay their fees in installments.
• Tuition fee refunds are available under certain circumstances.
• A postgraduate early payment discount is available.
• Postgraduate application deposits are required.