What is the course like?

This course offers a comprehensive curriculum that encompasses advanced machine learning algorithms, providing students with a deep understanding of both supervised and unsupervised learning techniques. It also includes a detailed comparison with classical statistical models, allowing students to understand the strengths and limitations of each approach and how to integrate them effectively. In addition, the curriculum covers techniques for analysing unstructured data, such as text mining, natural language processing (NLP), and image analysis, equipping students with the skills needed to handle diverse data types. Practical project work is a key component, enabling students to apply their theoretical knowledge to real-world problems and develop end-to-end machine learning solutions. This hands-on experience helps students build a portfolio of projects that demonstrate their capabilities to potential employers. Overall, the course ensures that students are well-prepared for careers in data science by balancing theoretical foundations with practical applications and staying current with industry trends and developments.

You'll gain

• Advanced machine learning algorithms
• How to compare these algorithms with classical statistical models
• Techniques for analyzing unstructured data from various sources
• You will also apply your knowledge through practical project work.

You'll learn

• Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods.

• You'll compare machine learning algorithms to classical statistical models and learn which is best for specific scenarios.
• This module looks at analysing unstructured data such as that found social media, newspaper articles, videos and more.

• The Data Science in Practice module provides you with an opportunity to yor apply knowledge through project work. 

Great for

The Postgraduate Diploma in Data Science is ideal for graduates with a background in numerate disciplines who seek to develop advanced skills and knowledge in machine learning and data analysis.

You'll need

For graduates with a first degree or equivalent in a numerate discipline.