MSc Data Science and Artificial Intelligence

Program Overview

Goldsmiths' MSc Data Science and Artificial Intelligence program equips students with the skills and knowledge to excel in data science, emphasizing AI applications. Through a combination of theoretical foundations and practical experience, graduates gain expertise in data analysis, machine learning, and AI techniques, preparing them for careers in various industries. The program's interdisciplinary approach and industry connections provide students with a competitive edge in the rapidly growing field of data science.

Program Outline

MSc Data Science and Artificial Intelligence at Goldsmiths, University of London

Degree Overview:

This program aims to equip students with the skills and knowledge necessary to succeed in the field of data science, with a particular focus on the application of artificial intelligence techniques. It emphasizes the analysis of large datasets, drawing on expertise from both the Department of Computing and the Institute of Management Studies.

Key Objectives:

• Develop statistical, computational, and data analysis skills.
• Explore the potential of AI in data science and analysis.
• Gain grounding in mathematical foundations of statistics, data mining, and machine learning.
• Apply Natural Language Processing and Deep Learning methods to real-world data.
• Learn from experts in diverse fields, including health analytics, neuroscience, finance, business, and management.
• Benefit from industry connections and guest speakers.
• Utilize industry-standard software for data analysis.
• Conduct original research on real-world data for the final project.

Program Structure:

• Duration: 1 year full-time, 2-3 years part-time
• Modules:
• Compulsory:
• Data Programming (15 credits)
• Machine Learning (15 credits)
• Artificial Intelligence (15 credits)
• Statistics and Statistical Data Mining (15 credits)
• Data Science Research Topics (15 credits)
• Neural Networks (15 credits)
• Final Project in Data Science (60 credits)
• Optional:
(Choose two, 30 credits total)
• Financial Data Modelling (15 credits)
• Blockchain Programming (15 credits)
• Mathematics for Financial Markets (15 credits)
• Econometrics (15 credits)
• From National Statistics to Big Data (15 credits)
• Advanced Econometrics (15 credits)
• Marketing Strategy (15 credits)
• Marketing Analytics (15 credits)
• Digital Marketing and Branding (15 credits)
• Data Visualisation (15 credits)
• Big Data Applications (15 credits)
• R Programming (15 credits)

Assessment:

Assessment methods vary depending on the module and may include:

• Examinations: Traditional written exams testing knowledge and understanding
• Coursework: Assignments, projects, and practical exercises demonstrating application of skills
• Presentations: Delivering presentations on research findings
• Final Project: In-depth research project applying learned skills to a chosen area of interest

Teaching:

• Methods:
• Lectures:
Delivering core content and theoretical foundations
• Seminars:
Interactive sessions for discussion and problem-solving
• Labs:
Hands-on practice with software tools and data analysis techniques
• Independent study:
Self-directed learning and research
• Faculty:
• Composed of experienced researchers and professionals in data science, AI, and related fields
• Expertise in diverse areas such as health analytics, finance, business, and management
• Strong industry connections and real-world experience
• Unique Approaches:
• Emphasis on practical application through projects and labs
• Interdisciplinary perspective drawing on computing and management studies
• Strong industry connections and guest speaker involvement
• Use of industry-standard software tools

Careers:

Potential Career Paths:

• Data Scientist
• Machine Learning Engineer
• AI Specialist
• Data Analyst
• Business Analyst
• Quantitative Analyst
• Financial Analyst
• Marketing Analyst
• Big Data Engineer
• Research Scientist

Opportunities:

• Diverse range of industries including finance, healthcare, technology, marketing, and research
• Strong demand for data science skills across various sectors
• Competitive salaries and career growth potential

Outcomes:

• Graduates are well-prepared for successful careers in data science and related fields
• Equipped with the necessary skills and knowledge to apply AI and data analysis techniques

Other:

• The program welcomes students from diverse academic backgrounds, including computer science, mathematics, statistics, engineering, economics, and finance.
• International students are encouraged to apply.
• The program offers various funding opportunities, including scholarships and tuition fee waivers.
• Goldsmiths provides a vibrant and supportive learning environment with access to state-of-the-art facilities.

Annual tuition fees These are the fees for students starting their programme in the 2024/2025 academic year. Home - full-time: £12520 Home - part-time: £6260 International - full-time: £20460 Additional costs There may also be specific additional costs associated with your programme.