These courses aim to provide beginners with an introduction to programming and computing languages (e.g. Matlab, Python, R, Unix), introductory statistics (i.e. Core Statistics in R) and best programming practices.
For all of these courses, the focus is always on how to use programming languages, statistics and best practices to solve biological problems.
Introduction to R
R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free.
For additional information and to register your interest, follow this link.
Introduction to Python
This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.
For additional information and to register your interest, follow this link.
Introduction to UNIX and bash
Using the Linux operating system and the bash command line interface, we will demonstrate the basic structure of the UNIX operating system and how we can interact with it using a basic set of commands. Applying this, we will learn how to navigate the filesystem, manipulate text-based data and structure simple pipelines out of these commands.
For additional information and to register your interest, follow this link.
Core Statistics using R
In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.
For additional information and to register your interest, follow this link.
High Performance Computing: An Introduction
The course aims to give an introductory overview of High Performance Computing (HPC) in general, and of the facilities of the High Performance Computing Service (HPCS) available at the University of Cambridge.
For additional information and to register your interest, follow this link.
Reproducible Research with R
This course introduces concepts about reproducibility that can be used when you are programming in R. We will explore how to create notebooks - a way to integrate your R analyses into reports using Rmarkdown. The course also introduces the concept of version control. We will learn how to create a repository on GitHub and how to work together on the same project collaboratively without creating conflicting versions of files.
For additional information and to register your interest, follow this link.
Introduction to Statistical Analysis
This course provides a refresher on the foundations of statistical analysis. The emphasis is on interpreting the results of a statistical test, and being able to determine the correct test to apply.
For additional information and to register your interest, follow this link.
Managing your Research Data
This workshop will provide an overview of some basic principles on how we can work with data more effectively.
For additional information and to register your interest, follow this link.
Introduction to Machine Learning
Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences.
For additional information and to register your interest, follow this link.
An introduction to MATLAB
This course aims to give you an introduction to the basics of Matlab. During the two day course we will use a practical based approach to give you the confidence to start using Matlab in your own work. In particular we will show you how to write your own scripts and functions and how to use pre-written functions. We will also explore the many ways in which help is available to Matlab users. In addition we will cover basic computer programming in Matlab to enable you to write more efficient scripts.
This course is currently not scheduled to run, but for more information and to express your interest please click here
Data Science in Python
This course covers concepts and strategies for working more effectively with Python with the aim of writing reusable code, using function and libraries. Participants will acquire a working knowledge of key concepts which are prerequisites for advanced programming in Python e.g. writing modules and classes.
This course is currently not scheduled to run, but for more information and to express your interest please click here
You may also find the following links useful:
Programming Courses at the Computing Service
Unix Training at the Computing Service