Core Module Information
Module title: Data Wrangling

SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET11121
Module leader: Dimitra Gkatzia
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science


Description of module content:

The challenges of contemporary data acquisition and analysis have been characterised as “the four V’s of Big Data” (volume, variety, velocity and validity). These require the use of specialised data storage, aggregation and processing techniques. This module introduces a range of tools and techniques necessary for working with data in a variety of formats with a view to developing data driven applications. The module focuses primarily on developing applications using the Python scripting language and associated libraries and will also introduce a range of associated data storage and processing technologies and techniques.

The module covers the following topics:

• Data types and formats: numerical and time series, graph, textual, unstructured,
• Data sources and interfaces: open data, APIs, social media, web-based
• NoSQL databases such as document (MongoDB), graph and key value pair
• Techniques for dealing with large data sets, including Map Reduce
• Developing Data Driven Applications in Python

The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.

Learning Outcomes for module:

On completion of this module, students will be able to:
LO1: Critically evaluate the tools and techniques of the data extraction, cleaning, interfacing, aggregation and processing
LO2: Select and apply a range of specialised data types, tools and techniques for data extraction, cleaning, interfacing, aggregation and processing
LO3: : Experiment with specialised techniques for dealing with complex text data sets, such as Natural Language Processing and machine learning.
LO4: Design, develop and critically evaluate data-driven applications in Python

Full Details of Teaching and Assessment

Indicative References and Reading List - URL:
SET11121/SET11821/SET11521 Data Wrangling