Programme

Programme Overview

The MLDS degree is a fully online degree part-time programme, delivered and structured over two-years, with three terms per academic year.

Year one modules:

• Ethics in Data Science and Artificial Intelligence (Part 1)
• Programming for Data Science
• Applicable Maths
• Exploratory Data Analytics and Visualisation
• Supervised Learning
• Big Data: Statistical scalability with PySpark
• Ethics in Data Science and Artificial Intelligence (Part 2)
• Bayesian Methods

Year two modules:

• Deep Learning
• Unsupervised Learning
• Ethics in Data Science and Artificial Intelligence (Part 3)
• Unstructured Data Analysis
• Learning Agents
• Research Project

With hands-on projects, students build a portfolio using industry-standard tools such as PySpark to showcase their new skills in applications such as probabilistic modeling, deep learning, unstructured data processing and anomaly detection. Relationships with corporate partners provide students the opportunity to address issues from different sectors, such as finance, software, health and medicine, engineering, and government.

When you graduate, you’ll be able to:

• Distinguish between machine learning modalities: supervised and unsupervised learning
• Identify appropriate machine learning methods and paradigms of inference for data analysis, showing awareness of their relative strengths and weaknesses
• Perform suitable pre-processing steps to prepare raw data for analysis
• Produce informative graphics and summaries to explore unfamiliar data
• Anticipate ethical and socially adverse consequences of machine learning methods
• Assess the performance of machine-learning methods using metrics and diagnostic plots
• Identify the limitations (computational and statistical) of machine learning methods and be aware of the dangers of working with observational data
• Interpret the output of machine learning algorithms in the original data science context
• Design end-to-end pipelines for data science, taking raw data as input and producing predictions and inferences as outputs
• Appreciate and critically appraise existing data analysis frameworks and tools
• Select computing architectures appropriate to a problem’s scale
• Summarize and communicate the output of models effectively in plain language
• Work independently with unfamiliar datasets of diverse types and demonstrate the ability to research novel problems and areas
• Automate optimal decision in the face of uncertainty

Try a course from Imperial College London

You can choose from the following two Specializations from other programmes offered by Imperial College London:

Mathematics for Machine Learning
Statistical Analysis with R for Public Health