Address: BCS The Chartered Institute for IT,
The Davidson Building, 5 Southampton Street (off The Strand),
Viewed by 25 users
Algorithms in GCSE computer science (2 days, CS Accelerator programme) Thursday Mar 14, 2019 9:00AM until Wednesday Mar 27, 2019 4:00PM
Organised by: NCCE CPDNCCE CPD Hosted by: NCCE
Dave Gibbs,
Tickets: book here:
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.
Computing at School is not accountable for any aspect of the content or management of this event.

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.