Numerical Computing in Python with NumPy & SciPy

Key Information

Tutor: Dr Christopher Cooling 
Course Level: Level 2 
Course Credit: 1 credit 
Prerequisites: See below
Course Duration: 2 x 2 hour sessions 
Format: Live online or live face to face with hands on practice

Prerequisites

Basic knowledge of Python is essential. Ideally an attendee will have used Python intensively for at least three months prior to attending this course. Python users who are already familiar with NumPy and/or SciPy will gain less from this course as it is primarily aimed at those learning about these features for the first time. Knowledge of the following areas of maths is required: 

  • Vectors 
  • Matrices 
  • Differentiation of simple functions 
  • Integration of simple functions 
  • Ordinary differential equation (useful, not required) 

If you are unfamiliar with of some of these areas, or would like a refresher, consider taking the Maths and Stats Online Catch Up course before you attend this course. This course can be completed at your own pace.

Course Resources

Python has many great advantages that leads to it being the programming language of choice for a large range of audiences. However, it is an inherently inefficient language and performing extensive numerical calculations in pure Python can be very slow. Fortunately, the NumPy and SciPy modules are a popular and effective way to greatly improve the performance of Python for numerical computing.

This course aims to introduce the basic features of the NumPy and SciPy packages and give attendees the experience required to begin using these packages in their own work. This will be achieved through a series of demonstrations, followed by hands-on practicals, which challenge attendees to apply the tools demonstrated to sample problems of increasing complexity.

Syllabus

  • What are NumPy and SciPy?
  • Creating and manipulating NumPy arrays
  • Operations using NumPy arrays
  • Performance comparison of NumPy arrays with standard Python
  • Using SciPy to perform numerical calculations
  • Extended exercises

The course will be delivered through a combination of written material, demonstrations and hands-on practicals.

Learning Outcomes


On completion of this workshop you will be better able to:

  • Describe the key functionality and advantages of NumPy and SciPy
  • Utilise NumPy arrays to store and perform operations on data sets
  • Locate appropriate SciPy functions for a specific problem
  • Create basic programs using NumPy and SciPy to solve numerical problems


Dates & Booking Information


There are no further sessions taking place this academic year. Course dates for 2024-25 will be available to book from late September.

To book your place, please follow the booking process advertised on the main programme page