Address: National STEM Learning Centre,
University of York,
YO10 5DD
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Algorithms in GCSE computer science (2-day course) Tuesday Jan 15, 2019 9:45AM until Monday Feb 11, 2019 4:00PM
Organised by: NCCE CPDNCCE CPD Hosted by: The National Centre for Computing Education
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Please note that this event is not one organised by Computing at School.
Should you wish to register for this event, you will be taken to a booking link on an external website.
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This course is delivered as part of the National Centre for Computing Education, and forms part of the Computer Science Accelerator programme.

By booking on this course you are agreeing to the terms and conditions of the National Centre for Computing Education. These can be found here

An understanding of algorithms is fundamental to success in computer science. To reach their full potential, students of GCSE computer science need to be confident in using decomposition and abstraction to solve problems.

This course centres on the core search and sort algorithms; exploring how they manipulate data structures and comparing the relative efficiency of different methods.

You will become skilled using formal maths and logic to design algorithms, and be able to trace algorithms confidently, finding and fixing errors. You’ll become familiar with the execution of algorithms in Python, supporting practical programming tasks.

Mapped closely to the specifications of GCSE Computer Science, the CPD will provide you with deepened knowledge and confidence that your students are equipped for their exams.

Bursaries exist to support you through 40 hours of CPD to complete the CS Accelerator programme. If you don’t complete the programme, we reserve the right to reclaim any bursaries paid.


You will gain the required knowledge to help your students:

understand and compare algorithms in terms of inputs, processes and outputs, including sorts, searches and string manipulations;

analyse problems, interpret flowcharts and pseudocode and evaluate the fitness of solutions using test data and logical reasoning;

trace algorithms and improve them, by identifying and correcting errors;

design algorithms using written descriptions, pseudocode and flowcharts to solve problems, leading to modular programming of solutions in Python code.