Courses

View all Courses
Data Handling & Networks

Data Handling & Networks

Learn how to handle, manipulate and visualise data in Python, elucidating correlations and relationships using network graph theory and its application to biological and medical data.
Read more ›
Machine Learning & AI

Machine Learning & AI

A comprehensive exploration of classical machine learning techniques, and artificial intelligence. This module delves into everything from classification and clustering, through to multi-layer perceptrons, convolutional neural networks and large language models.
Read more ›
Complete L2D Course

Complete L2D Course

A complete course in Python programming, data science, network science, classical machine learning and AI. Our most popular course, approved for CPD by the Royal Society of Biology and the Federation of the Royal Colleges of Physicians.
Read more ›

Modules

View all Modules
Introduction to Python

Introduction to Python

For students with limited programming experience, our Basic Python course provides a thorough introduction to programming principles and syntax, building foundational knowledge of core Python features.

Starting from the basics, this course covers essential concepts including variables, data types, iterations, functions and error handling. Students learn to write clean, readable code while developing problem-solving skills through hands-on exercises and practical examples. By completion, students will be comfortable with Python syntax and confident to tackle advanced topics in future modules.

Read more ›
Artificial Intelligence (AI)

Artificial Intelligence (AI)

Excited about AI, but don't know how to start applying it to your work? The L2D AI module has you covered with a deep dive into the background and practical application of deep learning in Biology.
Read more ›
Machine Learning

Machine Learning

L2D’s machine learning module provides an in-depth look at classical machine learning methods, providing a solid introduction to the core concepts and foundations of modern data prediction techniques. Students will learn to implement a range of classification algorithms, and will refine, measure and interpret their output for optimal results. This module will cover both supervised and unsupervised learning methods: namely the classification and clustering of different biological datasets, including images.
Read more ›