Actuarial Science and Data Analytics MSc

Program Overview

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The Actuarial Science and Data Analytics MSc program combines traditional actuarial theory with modern data analysis methods. It equips graduates with the skills to navigate data-driven environments and pursue careers in risk, data science, and finance. The program emphasizes hands-on learning, flexibility, and industry relevance, preparing students for success in a rapidly evolving job market.

Program Outline

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Degree Overview:

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Objective:

This program aims to equip students with the analytical and statistical skills necessary to navigate an increasingly data-driven economic environment alongside traditional actuarial theory and practice. It combines rigorous training in core actuarial principles with modern data analysis methods and prepares graduates for a wide range of careers in risk, data science, and finance.

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Description:

The Actuarial Science and Data Analytics MSc program delves into areas such as financial engineering, asset and liability modeling, advanced machine learning, financial data analytics, neural networks and deep learning, and computational statistics with R. It emphasizes a hands-on approach, requiring students to apply their acquired knowledge to real-world case studies throughout the curriculum.

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Distinguishing Features:

• Focus on Data Science: This program sets itself apart by integrating advanced data science and actuarial training, equipping graduates with valuable expertise in both domains.
• Flexibility and Personalization: The MSc allows students to tailor their studies through elective modules, accommodating their specific career aspirations and interests.
• Industry Relevance: The program incorporates modern topics and software packages widely used in contemporary industries, ensuring graduates remain at the forefront of the data-driven world.
• Renowned Faculty: Students learn from highly accomplished researchers and industry experts, gaining valuable insights and perspectives from experienced professionals.

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Outline:

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Structure:

The program comprises four compulsory modules and four elective modules spread across two semesters, culminating in a dissertation project.

• Actuarial Risk Management II (15 credits): This module delves deeper into applying risk management principles to specific financial products, focusing on areas like contracting and pricing.
• Time Series Analysis for Business (15 credits): This module equips students with the necessary skills for analyzing business-relevant time series data through statistical methods and interpretation of their findings using Python.
• Machine Learning with Python (15 credits): This module introduces the fundamentals of machine learning, exploring various algorithms, their application in Python, and their practical implementation in real-world scenarios using case studies.

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Elective Modules (choose four):

• Storing, Manipulating and Visualizing Data (10 credits): This module equips students with the expertise to efficiently store, manage, and visually represent data using modern IT tools.
• Topics in Scientific Computing (10 credits): This module delves into the fundamental principles and practical applications of computational methods using modern techniques like numerical solutions and the digital epidemiology environment.
• Advanced Machine Learning (10 credits): This module further expands on machine learning concepts, covering advanced techniques like clustering, dimensionality reduction, matrix completion, and autoencoders.
• Financial Data Analytics (10 credits): This module focuses on employing data analytics specifically within the finance domain, covering topics like volatility surface management, yield curve evolution, and FX volatility/correlation management.
• Neural Networks and Deep Learning (10 credits): This module dives into the intricacies of neural networks and deep learning, exploring modern architectures, their application, and implementation using Python and PyTorch.
• Computational Statistics with R (10 credits): This module concentrates on the practical aspects of statistical inference and learning for smaller data samples using the statistical software R.

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Dissertation Project (60 credits):

The project allows students to apply their acquired knowledge and research skills to investigate a specific topic relevant to actuarial science and data analytics, culminating in a written dissertation showcasing independent research and insights.

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Assessment:

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Methods:

• Formal examinations: Traditional written examinations assess students' understanding of theoretical concepts covered in each module.
• Coursework assignments: Throughout the program, various coursework assignments reinforce learning by requiring students to apply acquired skills to practical problems and simulations.
• Project dissertation: This extensive project involves in-depth exploration of a chosen topic culminating in an independently completed dissertation document reflecting original research.

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Criteria:

• Module assessments: Each module's assessment weightage contributes to the overall final mark, typically consisting of a combination of examination and coursework scores.
• Project dissertation: The dissertation carries a significant weight (33%) within the overall assessment, reflecting its in-depth nature and originality.

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Feedback Mechanisms:

• Ongoing, constructive feedback is provided throughout the program, enabling students to refine their understanding and performance.

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Teaching:

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Teaching Methods:

• The program employs a blend of traditional lectures and interactive seminars to effectively transmit knowledge and promote active learning.
• Practical sessions provide hands-on experience with data analysis tools and methodologies.
• The dissertation project fosters independent research skills and provides opportunities for individual guidance and interaction with supervisors.

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Faculty:

• The program benefits from the expertise of renowned academic staff with extensive research and industry experience.
• Visiting experts from relevant fields further enrich the learning experience through their specialized insights.

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Unique Approach:

• The program emphasizes active learning through real-world case studies and practical assignments.
• Students receive personal guidance and support through interactions with academic advisors and tutors.
• The program prioritizes continuous feedback and improvement throughout the learning process.

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Careers:

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Program Aims:

• This program strives to prepare graduates for thriving careers as:
• Professional actuaries in various industries.
• Data analysts or scientists within financial, insurance, or consulting organizations.
• Risk specialists capable of mitigating and managing risk across different sectors.
• Researchers specializing in actuarial science, data science, or related fields.

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Career Outcomes:

• Upon successful completion, graduates possess the necessary skills and qualifications for pursuing actuarial professional qualifications alongside expertise in data science and analytics, opening doors to diverse career avenues.
• Career support resources offered by the university facilitate job search, resume preparation, and interview skills development.

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Other:

• The School of Mathematical Sciences boasts modern, well-equipped facilities to enhance the learning experience.
• Students benefit from personalized support services provided by the university.
• The program welcomes applications from candidates with both actuarial science and non-actuarial science backgrounds, recognizing the value of diverse perspectives and backgrounds.

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Full-time studySeptember 2024 | 1 yearPart-time studySeptember 2024 | 2 yearsStarting inSeptember 2024LocationMile EndFeesHome: £12,650 Overseas: £31,850EU/EEA/Swiss studentsConditional depositHome: Not applicableOverseas: £2000Information about depositsPart-time studySeptember 2024 | 2 yearsStarting inSeptember 2024LocationMile EndFeesHome: £6,350 Overseas: £15,950EU/EEA/Swiss studentsThe course fee is charged per annum for 2 years.