Data Science
Computing, Engineering and Technology
Taught
MSc
Taught
MSc
Overview
Application Timeline
Tuition
- Year 1
| Student categories | Study | |
|---|---|---|
| Full-time | Part-time | |
| England | £11,325 | £11,325 |
| Northern Ireland | £11,325 | £11,325 |
| Scotland | £11,325 | £11,325 |
| Wales | £11,325 | £11,325 |
| EU | £18,700 | £18,700 |
| International | £18,700 | £18,700 |
Requirements
Language requirements information is currently unavailable.
Modules
Modules is currently unavailable
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About
The MSc in Data Science prepares graduates for successful careers in data science, artificial intelligence, and machine learning by combining theoretical knowledge with practical skills.
Course Highlights
- Exposure to a wide range of topics including:
- Data Science
- Statistics
- Specialist Programming
- Machine Learning
- Data Visualisation
- Application areas such as business intelligence.
Learning Outcomes
- Understanding of the in-depth theory behind data science and artificial intelligence.
- Development of practical skills essential for careers in this specialized field.
Subject Area Information
Machine learning (ML) is a dynamic and rapidly evolving discipline within the broader field of artificial intelligence (AI). It focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, relying instead on patterns and inference.
1. Introduction to Machine Learning
2. Mathematics for Machine Learning
3. Data Preprocessing and Feature Engineering
4. Advanced Machine Learning Algorithms
5. Deep Learning
6. Natural Language Processing (NLP)
7. Reinforcement Learning
8. Ethics and Fairness in Machine Learning
- Programming: Proficiency in languages such as Python and R, and familiarity with ML libraries like scikit-learn, TensorFlow, and Keras.
- Data Analysis: Ability to preprocess and analyze large datasets, extract meaningful insights, and visualize data.
- Model Building: Skills in selecting appropriate algorithms, tuning hyperparameters, and evaluating model performance.
- Problem-Solving: Developing solutions for real-world problems using ML techniques.
- Critical Thinking: Assessing the ethical implications of ML applications and ensuring models are fair and unbiased.
Career
A machine learning curriculum is comprehensive and multifaceted, preparing students for a range of careers in technology and data science. Through a combination of theoretical knowledge and practical experience, students gain the skills needed to excel in this cutting-edge field.
Machine Learning Engineer
Designing and implementing ML models and systems.
Data Scientist
Analyzing data to extract insights and build predictive models.
AI Research Scientist
Conducting research to advance the field of AI and ML.
Data Analyst
Interpreting complex data sets to help organizations make informed decisions.
Software Developer
Integrating ML algorithms into software applications.
Business Intelligence Analyst
Using ML to improve business processes and strategies.
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