Master of Science in Machine Learning and Data Science
Imperial College London
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
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Programme Length
Each academic year is divided into three terms.
Complete 12 modules over two years (90 ECTS), including a research portfolio.
On average, you will dedicate 21 hours per week to study.
| Year | Term 1 | Term 2 | Term 3 |
|---|---|---|---|
| Year 1 | 3 modules (12.5 ECTS) | 2 modules (12.5 ECTS) | 3 modules (15 ECTS) |
| Year 2 | 2 modules (15 ECTS) | 3 modules (15 ECTS) | 1 module* (20 ECTS) |
*Research project
Note: Ethics module is split into 3 parts (T1,T3,T5)
Flexibility
The online Machine Learning and Data Science degree is a chance to earn a Master's degree from a leading university. Students can keep their commitments while earning the degree, studying online on their own schedule. The 100% online format of the programme fosters global collaboration and shared research among students in a way that campus-based programmes cannot.
Coursera on Mobile
Access course materials anywhere with the mobile app available on iOS and Android.
- Using the mobile app, learners can:
- Save a week’s worth of reading and video content for offline access with one click
- View text transcripts of lecture videos
- Take notes directly in the app
- Set reminder alerts to help you make progress
Download Coursera's mobile app
Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution. If upon graduation you intend to pursue a PhD or apply for employment which requires a master-level degree beyond 90 ECTS credits, we encourage you to investigate whether this programme meets your academic and/or professional needs before applying.
