Introductory description

Efficient algorithms and data management have contributed to the advances in digital solutions design and implementation. However, practitioners often face resource constraints related to computing speed and memory when designing digital artefacts. It is therefore essential to acquire the knowledge and skills necessary to apply the principles of efficient data structure and algorithm design to better maximise the available computing resources. That is what this module intends to achieve.
This module contributes to AHEP 4 learning outcomes for Partial CEng and BCS accreditation criteria for Full CITP as referenced in the learning outcomes.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Basic and advanced data structures;
Algorithmic paradigms and design; Recursive, brute force, divide & conquer, dynamic programming, greedy algorithms, backtracking;
Searching algorithms,
Sorting algorithms,
Graph data structures.
Space and Time complexity (O-notation).

Learning outcomes

By the end of the module, students should be able to:

• Describe the fundamental data structures and discuss their trade-offs in different contexts [AHEP:4-2.1.1, C1].
• Analyse a range of algorithms by evaluating their complexity [AHEP:4-2.2.2, C1, C2].
• Evaluate common algorithm design techniques based on their strengths and limitations, and correctly apply them to solve real-world problems [AHEP:4-2.1.1, 2.2.1, 2.2.2, C3, C4, C13.]
• Implement efficient data structures and algorithms to solve complex computational problems [AHEP:4-2.1.1, 2.1.2, 2.2.1, C2, C3, C5].

Indicative reading list

Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein;
Algorithm Design by J. Kleinberg and E. Tardos; Algorithms by S. Dasgupta, C.H. Papadimitriou and U.V. Vazirani.

Subject specific skills

Analyse a business problem in order to specify an appropriate digital and technology solution (S3).
Initiate, design, code, test and debug a software component for a digital and technology solution (S4).
Use appropriate analysis methods, approaches and techniques in software engineering projects to deliver an outcome that meets requirements (S18).

Transferable skills

Problem Solving: Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions. Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious. Knows how to learn from failure.
Written communication: Present arguments, knowledge, and ideas, in a range of formats.
Active listening: questioning, reflecting, summarising.
Critical Thinking: Recognise patterns, themes, and key messages from sometimes confused and incomplete data. Make informed decisions on the value of a range of sources allowing an evidence-based conclusion based on this analysis.
Professionalism: Prepared to operate autonomously. Aware of how to be efficient and resilient. Self-motivated, setting and achieving goals, prioritising tasks.