Program Overview
This master's degree combines knowledge of social sciences, business, and humanities with data science methodologies. Graduates can pursue careers in various fields, including media production, policy analysis, and technological development. The program emphasizes responsible data use and societal implications, equipping students with a critical understanding of the ethical challenges involved in data-driven decision-making.
Program Outline
Degree Overview:
This MSc program in Human and Social Data Science is designed for graduates in the social sciences, business, or humanities who have an interest in data and data-driven methods. The program aims to equip students with the knowledge and skills necessary for a career in the field of data science. Students will explore:
- The Python programming language
- Probability theory, statistics, linear algebra, and calculus
- Machine learning techniques
- Ethics and interpretability Students can choose to focus their studies on digital media or innovation and policy. The program benefits from collaboration with industry partners, providing career development opportunities.
Outline:
The program is offered both full-time and part-time. The full-time program is one year in duration, while the part-time program is two years.
Core Modules:
- Dissertation (Human and Social Data Science): This module allows students to conduct independent research and apply their knowledge to a specific area of interest.
- Data Science Research Methods: This module introduces students to the principles and practices of data science research, including data collection, analysis, and interpretation.
- Mathematics and Computational Methods for Complex Systems: This module provides students with a foundation in the mathematical and computational tools used in data science, including probability, statistics, and linear algebra.
- Data Science Masters Research Proposal: This module guides students in developing a research proposal for their dissertation.
- Machine Learning: This module introduces students to the principles and techniques of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
- Wider Topics in Data Science (L7): This module explores advanced topics in data science, such as big data analytics, data visualization, and data ethics.
Optional Modules:
- Applied Natural Language Processing: This module focuses on the application of natural language processing techniques to real-world problems, such as text classification, sentiment analysis, and machine translation.
- Media, Law and Ethics: This module examines the legal and ethical implications of data use in media, including privacy, copyright, and freedom of expression.
- Policy Making and Policy Analysis: This module introduces students to the principles and practices of policy analysis, including data collection, analysis, and evaluation.
- Programming through Python: This module provides students with hands-on experience in programming using the Python language.
- Artificial Intelligence & Global Digital Policies: This module explores the impact of artificial intelligence on global digital policies, including privacy, security, and economic development.
- Industrial and Innovation Policy: This module examines the role of data in industrial and innovation policy, including the development of new technologies and the regulation of emerging industries.
- Machine Learning and Statistics for Health (L7): This module focuses on the application of machine learning and statistical methods to health data, including disease prediction, treatment optimization, and public health surveillance.
- New Developments in Digital Media: This module explores emerging trends in digital media, including social media, mobile computing, and the Internet of Things.
- Race, Culture and the Media: This module examines the role of data in shaping representations of race and culture in media.
- Techno-Feminism History and Practice: This module explores the history and practice of techno-feminism, including the use of technology to challenge gender inequality and promote social justice.
Assessment:
The assessment methods for the program may vary depending on the specific modules. However, common assessment methods include:
- Essays: Students may be required to write essays on various topics related to data science, such as the ethical implications of data use or the application of machine learning techniques to a specific problem.
- Projects: Students may be required to complete projects that involve collecting, analyzing, and interpreting data.
- Presentations: Students may be required to present their research findings or project results to their peers and faculty.
Teaching:
The program is taught by a team of experienced faculty members who are experts in their respective fields. The teaching methods may include:
- Lectures: Lectures provide students with a comprehensive overview of key concepts and theories.
- Seminars: Seminars offer students the opportunity to engage in discussions with faculty and peers.
- Workshops: Workshops provide students with hands-on experience in using data science tools and techniques.
- Guest speakers: The program may feature guest speakers from industry and academia to share their insights and experiences.
Careers:
Graduates of the Human and Social Data Science MSc program are well-prepared for a wide range of careers in various fields, including:
- Online retail and marketing: Data scientists can help businesses understand customer behavior, optimize marketing campaigns, and improve customer experience.
- Media production: Data scientists can help media companies understand audience preferences, personalize content, and improve the effectiveness of their productions.
- Innovation and technology policy: Data scientists can help governments and organizations develop policies that promote innovation and address the challenges of the digital age.
- Management, finance, and banking: Data scientists can help organizations make better decisions by analyzing financial data, identifying trends, and predicting future outcomes.
- The Civil Service: Data scientists can work in government agencies to analyze data, inform policy decisions, and improve public services. Students will develop a critical understanding of the ethical challenges associated with data collection, analysis, and interpretation.
Home students: £10,500 per year for full-time students Channel Islands and Isle of Man students: £10,500 per year for full-time students International students: £21,500 per year for full-time students
