Algorithms and Data Structures

COMP26120 Algorithms and Data Structures 20 Full year Undergraduate Level 2 Louise Dennis Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Middle part of Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 This course unit covers fundamental material in Computer Science concerning algorithms and their correctness and performance. It is a two-semester practical course, with some video and reading resources, considerable practical experience and lab support. The student is expected to seek out material to support work on the course, thus contributing to developing "algorithmic literacy". The implementation of algorithms is illustrated in C, Java and Python. This course unit covers fundamental material in Computer Science concerning algorithms and their correctness and performance. It is a two-semester practical course, with some video and reading resources, considerable practical experience and lab support. The student is expected to seek out material to support work on the course, thus contributing to developing "algorithmic literacy". The implementation of algorithms is illustrated in C, Java and Python..  To make best use of available learning time by encouraging active learning and by transmitting information in the most effective ways.To make students aware of the importance of algorithmic concerns in real-life Computer Science situations.To emphasise practical concerns, rather than mathematical analysis.To become confident with a range of data structures and algorithms and able to apply them in realistic tasks. <wbr style="color: rgb(0, 0, 0); font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;" /><div> <ul> <li> ILO 1 Analyse problems to identify and implement the most appropriate algorithmic solution</li> <li> ILO 2 Define standard notions of asymptotic complexity and use these to reason about the complexity of algorithms</li> <li> ILO 3 Use pseudocode to represent algorithms and informally reason about their correctness </li> <li> ILO 4 Recall the definitions and representations of basic data structures and the complexity of the operations on them</li> <li> ILO 5 Explain, using examples of real-world applications, standard algorithmic problems coming from sorting and searching on different data structures, operations on graphs, and number theory</li> <li> ILO 6 Identify from a set of taught algorithms, which algorithm should apply in a given situation, explain how it should be applied, and compare the solution to possible alternatives </li> <li> ILO 7 Explain the algorithmic techniques such as divide-and-conquer, dynamic programming, greedy algorithms, and linear programming, discuss when they are appropriate, and apply them to solve problems</li> <li> ILO 8 Recall and explain the notions of tractability and NP-completeness, with a particular focus on classical NP-complete problems, and apply these to demonstrate NP-completeness of new problems</li> </ul> <div>  </div></div>  Analytical skills Innovation/creativity Problem solving Written communication Algorithms - what they are and how to express them (in pseudocode and selected programming languages: Java, C or Python).Practical experience in devising, assessing and using algorithms:<ul> <li>considerable practice in algorithmic problem solving for realistic problems</li> <li>examples from a wide range of application areas</li> <li>finding appropriate algorithms and data-structures</li> <li>inventing appropriate algorithms and data-structures</li></ul>Practical experience in 'algorithmic literacy' - knowing how to use the extensive literature on the subject, recognising what algorithms to use in applications and assessing their utility.A range of basic data structures: arrays, lists, trees (including ordered and balanced trees and heaps), and various kinds of graphs.  Representations of basic data structures in programming languages.A range of basic algorithms: searching and sorting algorithms, tree traversal and manipulation algorithms, some basic graph algorithms. Other algorithmic areas will be explored through practical examples.An introduction to algorithmic performance: space and time requirements, worst-case, average-case and best case estimates. Practical experience and techniques for measuring and predicting performance: Counting operations.Scaling and some common rates of growth.Reasoning about algorithms - experience in informally reasoning about algorithms to establish correctness. Asynchronous material (directed videos and reading):44 hours in total, 2 hours of study per week Synchronous Q&A Sessions22 in total, 1 per weekLaboratories44 hours in total, 44 1-hour sessions, 2 per week<div>Examinable content is introduced in both asynchronous material and lab exercises</div><div> </div><div>Formative Blackboard quizzes each week provide early feedback on examinable concepts</div> 1 Written exam 50% 8 Practical skills assessment 50% Feedback is via a variety of methods. Immediate feedback is provided in laboratory sessions. There are Blackboard quizzes associated with all material to track understanding. Summative feedback on assessed coursework is provided on individual scripts within Blackboard. COMP16321 Introduction to Programming 1 Pre-Requisite Compulsory COMP16412 Introduction to Programming 2 Pre-Requisite Compulsory Alternative equivalent knowledge of C, Java or Python accepted. N <a href="http://studentnet.cs.manchester.ac.uk/syllabus/index.php?code=COMP26120&year=2020" target="_blank">COMP26120 reading list</a> can be found on the Department of Computer Science website for current students. Assessment written exam 4 Lectures 22 Practical classes & workshops 44 0 130 Course unit materials<a target="_blank" href="http://studentnet.cs.manchester.ac.uk/ugt/syllabus.php">Links to course unit teaching materials</a> can be found on the School of Computer Science website for current students.