Algorithmic Data Science (969G5)
Algorithmic Data Science
Module 969G5
Module details for 2022/23.
15 credits
FHEQ Level 7 (Masters)
Library
Mining of Massive Data Sets – Leskovec, Rajaraman and Ullman (2014)
Introduction to Algorithms – Cormen, Leiserson, Rivest and Stein (2009)
Introduction to Data Science: a Python approach to concepts, techniques and applications – Igual and Segui (2017)
Module Outline
The module teaches the computer science aspects of data science. A particular focus is on how data are represented and manipulated to achieve good performance on large data sets (> 10 GBytes) where standard techniques may no longer apply. In lectures, students will learn about data structures, algorithms, and systems, including distributed computing, databases (relational and non-relational), parallel computing, and cloud computing. In laboratory sessions, students will develop their Python programming skills; work with a variety of data sets including large data sets from real world applications; and investigate the impact on run-time of their algorithmic choices.
Module learning outcomes
Apply knowledge of standard data structures to the formulation and decomposition of big data.
Understand the fundamental issues and challenges of developing parallel distributed algorithms for big data.
Evaluate choice of computing model and data representation based on estimation and measurement of impact on space and time complexity and communication performance.
Apply appropriate methods to store and retrieve structured big data.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Test | T1 Week 4 (1 hour) | 10.00% |
| Test | T1 Week 9 (1 hour) | 10.00% |
| Report | XVAC Week 1 | 80.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Autumn Semester | Lecture | 2 hours | 11111111111 |
| Autumn Semester | Laboratory | 2 hours | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Adam Barrett
Assess convenor
/profiles/156234
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