Module title: Data Management and Processing

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

Module code: SET11823
Module leader: Thomas Methven
School School of Computing
Subject area group: Software Engineering
Prerequisites

N/A


Description of module content:

This module will explore and develop data management and processing solutions that will work on dirty, complex, real-world data. This module will examine the key concepts of data warehousing, data cleaning, and data processing in the context of business requirements and focus on how to combine these steps into a coherent data processing pipeline.
First, modern tools and techniques in data management will be examined, with the emphasis on good practice and professional approaches of storing and handling data. Next, the module will examine ways of cleaning noisy real-world data in order to make it suitable for data processing. Finally, data processing and collation techniques such as Machine or Deep Learning will be applied to the data to extract structure and elicit comprehension of the data. Throughout the module, advantages and disadvantages of using local and cloud approaches will be explored, alongside discussing common parallel approaches to facilitate faster solutions.
In short, the goal of this module is to allow students to understand a data processing pipeline from raw data to final delivery. It will cover:
• Data warehousing and storage techniques
• Data cleaning techniques
• A discussion of cloud approaches
• Data processing and collation techniques
• An introduction to parallel data pipeline approaches

Learning Outcomes for module:

LO1: Critically assess different data warehousing techniques and technologies related to data management
LO2: Appraise different methods of data cleaning in the context of large or complex data sets
LO3: Critically reflect on the advantages of local and cloud solutions for data processing
LO4: Display mastery of industry-standard data collation techniques
LO5: Produce a data processing and management pipeline from raw data to a final delivery

Indicative References and Reading List - URL:

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