LEARN TO DISCOVER

A fully-supported online training course in Python programming, data science, machine learning and artificial intelligence (AI) tailored for health, disease and bioscience.

Whether you are a novice or experienced programmer, L2D contains modules that are right for you.

Basic Python Programming

Data Handling

Network Science

Machine Learning

Artificial Intelligence

Current Courses

L2D is offered both as a complete course, and as two separate modules (Part I and Part II).

Complete L2D Course

A complete Python programming, data science and machine learning course. Our most popular, highest-rated course.

Price: £1,000 + VAT

Complete L2D Course

Basic Python:

The Basic Python component of the L2D course offers learners a comprehensive introduction to programming in Python. Some of the key study areas covered in this module are:

Algorithmic thinking
Variables, types and operations
Conditional statements
Arrays, tuples, lists and indexing
Iterations: for and while loops
Dictionaries: associative arrays
Functions: defining functions, uses and applications

Data Handling:

An introduction to importing, handling and analysing a variety of different data types, in Python. Key study areas include:

Import and characterisation of data as Pandas dataframes
Basic statistics
Data visualisation with Matplotlib
Bivariate and multivariate analyses
The Pearson correlation coefficient and correlation matrix
Image handling: import and characterisation of image data (greyscale and colour)
Image masking and segmentation
Time series: visualisation, filtering and Fourier transform
Relationships between time series data

Note: This module (or its equivalent) is a compulsory prerequisite to taking the Machine Learning component of L2D.

Supervised Machine Learning:

Key areas taught in L2D's exploration of Supervised Machine Learning include:

Classification: preparing data for classification, training classifier models.
State space plot of model predictions
Prediction probabilities and feature importance
Complex training and testing of data
Comparison of different model classes
Stratified shuffle split
Evaluation of classification using AUC and ROC curves
Metrics for model evaluation
Permutation scoring and confusion matrices
Normalisation and hyperparameter tuning
Refinement and progressive adjustment

Unsupervised Machine Learning:

Key areas taught in L2D's exploration of Unsupervised Machine Learning include:

Gaussian Mixture Models (GMMs) and sci-kit learn
Clustering and automated data labelling
Quantitative scoring using ground truth
Introductions to the concept and pitfalls of clustering techniques
GMMs in medical image segmentation and object detection
Dimensionality reduction (reducing computational workloads of large high-dimensionality datasets)
PCA (Principal Component Analysis)

Note: L2D's Basic Python and Data Handling modules (or their equivalent) are compulsory requirements to taking our Machine Learning course.

Price: £1,000 + VAT

Prerequisites: None

See our courses

Featured Lecturers

Prof. Gerold Baier

Academic Lead

Dr. Adam Lee

Senior Fellow

Dr. Laurence Blackhurst

Education and Technology Fellow

See our courses

Part I: Basic Python & Data Handling

For programming novices who wish to learn foundational Python programming, without progressing to Machine Learning, this module can be taken, independently.

Price: £600 + VAT

Part I: Basic Python & Data Handling

Basic Python:

A comprehensive introduction to programming in Python. Key study areas include:

- Algorithmic thinking
- Variables, types and operations
- Conditional statements
- Arrays, tuples, lists and indexing
- Iterations: for and while loops
- Dictionaries: associative arrays
- Functions: uses, applications and defining your own customised Python functions

Data Handling:

An introduction to importing, handling and analysing a variety of different data types, in Python. Key study areas include:

- - Import and characterisation of data as Pandas dataframes
  - Basic statistics
  - Data visualisation with Matplotlib
  - Bivariate and multivariate analyses
  - The Pearson correlation coefficient and correlation matrix
  - Image handling: import and characterisation of image data (greyscale and colour)
  - Image masking and segmentation
  - Time series: visualisation, filtering and Fourier transform
  - Relationships between time series data

Note: This module (or its equivalent) is a compulsory prerequisite to taking the Machine Learning component of L2D.

Price: £600 + VAT

Prerequisites: None

Enroll Now / More Info

Featured Lecturers

Prof. Gerold Baier

Academic Lead

Dr. Adam Lee

Senior Fellow

Dr. Laurence Blackhurst

Education and Technology Fellow

Enroll Now / More Info

Part II: Machine Learning

For experienced Python programmers and data scientists already proficient at handling data using Python. This is a fast-track to the Machine Learning modules of L2D for experienced programmers.

Price: £500 + VAT

Part II: Machine Learning

Note: L2D's Basic Python & Data Handling course (or its equivalent) is a compulsory prerequisite to taking the Machine Learning component of L2D.

Supervised Machine Learning:

Key areas taught in L2D's exploration of Supervised Machine Learning include:

Classification: preparing data for classification, training classifier models.
State space plot of model predictions
Prediction probabilities and feature importance
Complex training and testing of data
Comparison of different model classes
Stratified shuffle split
Evaluation of classification using AUC and ROC curves
Metrics for model evaluation
Permutation scoring and confusion matrices
Normalisation and hyperparameter tuning
Refinement and progressive adjustment

Unsupervised Machine Learning:

Key areas taught in L2D's exploration of Unsupervised Machine Learning include:

Gaussian Mixture Models (GMMs) and sci-kit learn
Clustering and automated data labelling
Quantitative scoring using ground truth
Introductions to the concept and pitfalls of clustering techniques
GMMs in medical image segmentation and object detection
Dimensionality reduction (reducing computational workloads of large high-dimensionality datasets)
PCA (Principal Component Analysis)

Note: L2D's Basic Python and Data Handling modules (or their equivalent) are compulsory requirements to taking our Machine Learning course.

Price: £500 + VAT

Prerequisites: Part I: Basic Python & Data Handling or equivalent

Enroll Now / More Info

Featured Lecturers

Prof. Gerold Baier

Academic Lead

Dr. Adam Lee

Senior Fellow

Dr. Laurence Blackhurst

Education and Technology Fellow

Enroll Now / More Info

View all courses & modules

Lesson Resources

Our course provides a rich resource of learning materials, available online for learning and studying at a learner’s preferred pace. These include:

Live Lectures

With each lesson topic release, we hold a 1-hour live lecture online, that allows learners to log in remotely, while we explain the core concepts of each topic. Students have the option to code live with us, in real-time, and pause, ask questions, and have us review their code on-screen with them, to help with any issues they may be facing. These live lectures are filmed in high resolution, and made archival for students to replay, at their leisure.

Drop-In Sessions

Following on from our live lecture, we hold a 1-hour drop-in session offering those taking the course a chance to speak directly with our L2D academics and tutors. This provides a relaxed, live, open-forum discussion space for students to ask questions about course lesson topics, assignments and even highlight technical queries.

Written Materials

These effectively provide an online textbook-style resource, that thoroughly explores each topic, complete with plentiful life science-based examples. These materials are complete with figures, practice exercises and solutions. All our online learning materials are hosted online as webpages, and can be accessed on a variety of devices.

Video Materials

With each lesson topic offered on L2D, we also provide succinct tutorial videos, that cover the main areas of study to which learners are introduced. These are professionally filmed at high resolution, and are presented and narrated by an L2D academic. The tutorial videos also come complete with type-along, animated code and output boxes, that learners can follow at their own pace – pausing and rewinding, as necessary.

Assignments

Learners are expected to complete one assignment per lesson topic. Assignments are marked by our L2D tutors, who provide plentiful point-by-point, personalised feedback, giving any learners who are struggling the opportunity to thoroughly discuss and understand the material and concepts taught on the course.

Introducing Learn to Discover

Training-in-Machine-Learning-and-Data-Science

Testimonials

“Thanks so much to the whole L2D team! I really enjoyed this course, I found it so useful and applied to the biology we do at GSK.”

Aisling Roche

Senior Scientist at GSK

“I enjoyed all aspects of the L2D course, which gave a great insight in to python coding, data analysis and machine learning. I’ve learned a great deal and the course has been highly enjoyable.”

Chris Sayer

Scientist at Abzena, Cambridge

“L2D was very useful for me, in terms of the acquired data handling and machine learning skills, enabling us to compare and contrast different datasets.”

James Sweet-Jones

PhD Student at University College London

“Proud to have passed with 'Flawless' and 'Excellent' feedback, this journey has been a blend of challenge and discovery. The project work was a practical and enriching experience.”

Jo Renaut

PhD Student at the University of Sussex

“I really enjoyed the Training in Data Science & Machine Learning for Health, Disease & Bioscience course, which has been a comprehensive introduction to machine learning (ML) using python programming.”

Molly Went

Analytical Scientist at The Institute of Cancer Research

“I really enjoyed every lesson,... When we started looking at multivariate analyses, we started looking at EEG data and brain scans; that, to me - as a neuroscientist - was very useful.”

Prof. Chris Petkov

Professor of Comparative Neuropsychology at Newcastle University

“It’s been an incredible journey to be part of L2D-June2023. The course, led by the knowledgeable trio of Gerold, Adam and Saba is undeniably of high quality, and I can’t recommend it more. ”

Yanxia Wu

Technology Platforms Manager and Senior Scientist

Register Your Interest

Our next course commences on 3rd of November 2025. Add your email to register your interest now.

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FAQs

When does the next course start?

L2D runs two courses per year: one in the Spring and one in the Autumn. For 2025 admission, the Spring course commences on May 12th 2025, and the Autumn course provisionally commences on November 3rd 2025.

Are there any prerequisites to taking L2D?

To comfortably enrol in the L2D course, it is recommended that you have a very basic level of proficiency in using a personal computer: and a basic proficiency in using the operating system of your choosing (either Windows, Linux or Mac OS). You will also need a suitable computer of your own and access to a broadband internet connection.

How much does it cost to take the entire L2D course?

If you choose to take the entire L2D course, comprising the Basic Python, Data Handling and Machine Learning modules, the combination price of the course is reduced to £1000, per individual.

Is the course suitable for beginners and programming novices?

Yes. Our Basic Python course – in its earliest modules – takes learners through the basics of setting up Python, and the most basic programming operations and functions. For those individuals who have either not programmed before, or who have limited programming experience, we recommend that you enrol in L2D from the Basic Python stage. Please contact [email protected] for more information on the optimal point at which to join the course.

Is the course suitable for individuals who are already experienced in Python programming?

Yes. For those individuals who have extensive Python programming experience, we recommend they take modules onwards of our Data Handling units: a prerequisite of this and all subsequent modules on our course, is a basic proficiency in the Python programming language. For learners who have this level of experience, we recommend joining from this stage, onwards. Please contact [email protected] to discuss this, and how to join at the appropriate stage of the course.

What is the pace of the course?

Each year, we run two L2D course cohorts, both of which centre around fortnightly lesson release calendars. We release one lesson topic every fortnight, and during this 14-day period, we give one online live lecture, and release lesson materials for self-study. Learners are given this full 14-day period to go through the materials at their preferred pace, and complete the lesson assignment, which is to be submitted before the release of the next lesson topic.

What kind of support and help is available to me, on the L2D course?

L2D has a dedicated team of tutors and academics on standby for learners who have specific questions or queries related to their study. Tutors are also available to engage in dedicated 1-to-1 support sessions, that learners can arrange via our dedicated online booking system. For learners who wish to seek other avenues of support, questions can be posted on our dedicated online forums, moderated regularly by L2D tutors. Furthermore, the assignments submitted by learners every fortnight are assessed by our tutors, and promptly returned to learners with copious feedback and suggestions.

What is the workload of the L2D course like?

In order to comfortably complete the L2D course, we recommend about 8 hours of self-study per lesson topic: this covers time for attending our live lectures, as well as self-study and completing assignments. Learners are also expected to submit an assignment at the end of each 14-day lesson period.

While we average the hours of study per topic to be roughly 8 hours in total, realistically, this varies slightly. Many learners find the Basic Python topics faster to complete, while the more complex Machine Learning topics may take individuals a longer period of time to finish, for example.

Once a learner has completed the final Machine Learning lessons, we assess their learning with a Final Project; completion of this is allocated a further 30 days, and tutor support is available throughout, should learners have any questions.

What learning platform is used for the L2D course?

The L2D course is currently hosted on a GitHub-based learning environment. We provide assignment submission and marking via GitHub Classroom, and materials are hosted on individually-created GitHub repositories, provided for each learner on a per-topic basis. These repositories contain everything a learner will need to complete each lesson offered on L2D: this includes written materials, video materials, details of live lectures, supplementary Jupyter Notebook assignment templates and data: specific to each lesson release. Our GitHub learning environment also features a public discussion forum available to all learners, allowing interaction with both L2D academics and other learners partaking in the course. We fully encourage and make heavy use of these forums as a place to announce updates, and receive questions from our learners, with publicised solutions and answers.

By the end of the course, L2D learners will have fully familiarised themselves with GitHub as a platform and environment for learning, programming, collaborating and exchanging code, data and files. GitHub is also a global community that serves as an international online hub for informatics, programming and computer science. Together with the materials and topics covered, the training we provide via GitHub is a pivotal component of the knowledge that L2D learners leave our course with.

How is the course assessed?

The L2D course is assessed via topic-wise assignments, together with a Final Project:

Assignments: With each lesson release, assignments are set to monitor a learner’s progress, and identify facets of their learning that may require improvement.
Final Project: This is assessed more strictly, upon successful completion of all our modules. The grade awarded for this project is pivotal to learners being awarded their L2D Certificate of Completion. Marks and written feedback are provided by our tutors throughout and are returned directly to students, shortly after submission.

How do I sign up for the L2D course?

To register your interest in an upcoming course, please fill out our enquiry form, and a representative will get back to you as soon as possible.

Do you offer any in-person teaching?

We offer a limited number of in-person workshops throughout the year. Upon request, it is possible to book our L2D Academics to host a face-to-face, in-person workshop at a venue of your choosing. In terms of content and activities, these workshops typically offer tailored programmes of learning spread out over one or more days of teaching activities. If you are interested in attending or suggesting a future L2D workshop, please get in touch with us at [email protected].

Will learning materials remain available once the course has been completed?

Yes. Our online resources are available to each individual learner on a personalised, one-user-only login to our online learning portal. These will be available for a limited time, following completion of the course.