Core Module Information
Module title: Data Wrangling

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

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

Requisites: Pre-requisite: [Module SET07111] Scripting for Data Science

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 PythonThe 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:

Upon 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
2024/5, Trimester 2, IN PERSON,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: IN PERSON
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Md Zia Ullah
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Online Lecture 200 no data included in migration. Only Adding to close withdrawal.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Practical Skills Assessment 30 1~2 Not Yet Determi HOURS= 12 hours practical
Practical Skills Assessment 70 3~4 Not Yet Determi HOURS= 12 hours practical
Component 1 subtotal: 30
Component 2 subtotal: 70
Module subtotal: 100

Indicative References and Reading List - URL:
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