Core Module Information
Module title: Data Wrangling

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

Module code: SET10415
Module leader: Dimitra Gkatzia
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

N/A

Timetables
Description of module content:

The challenges of contemporary data acquisition and analysis have been characterised as “the five V’s of Big Data” (volume, variety, velocity, veracity 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 data-driven and Machine Learning 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, textual, unstructured
• Data sources and interfaces: open data, APIs, social media, web-based
• NoSQL databases such as MongoDB
• Techniques for dealing with large, complex data sets
• Developing Data Driven and Machine Learning 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, 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 skills for employment.

Learning Outcomes for module:

On completion of this module, you will be able to
LO1: Evaluate the tools and techniques of the data storage, interfacing, aggregation, and processing
LO2: Select and apply a range of specialised data types, tools and techniques for data storage, 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 evaluate data driven applications in Python

Full Details of Teaching and Assessment

Indicative References and Reading List - URL:
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