Core Module Information
Module title: Data Wrangling

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

Module code: SET10116
Module leader: Dimitra Gkatzia
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

n/a

Description of module content:

Contemporary data acquisition and analysis present many challenges, therefore they require the use of specialized data preprocessing, aggregation and manipulation 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 processing technologies and techniques, such as natural language processing and machine learning.

The module covers the following topics:

• Data types and formats: numerical and time series, textual, unstructured.
• Data sources and interfaces: open data, APIs, social media data.
• Techniques for dealing with large text data sets, including natural language processing and machine learning.
• Developing and evaluating 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: Evaluate the tools and techniques of data interfacing, aggregation, and processing
LO2: Select and apply a range of specialised data types, tools, and techniques for data 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
2022/3, Trimester 2, FACE-TO-FACE, Edinburgh Napier University
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MERCHISTON
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Dimitra Gkatzia
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
The module has been designed to provide a clear roadmap for students to work through the learning materials. Module-specific materials designed to support the studies are detailed in the module descriptors. This includes items such as:
• A module introduction/overview, including learning outcomes, and a summary of key learning points.
• Units of Learning materials, (subject-specific units).
• Case Studies/Practical exercises (and outline solutions).
• Online discussions instigated and moderated by the module leader through the student forum
• Reflective Exercises
• Links to core module academic materials; book chapters / journal articles / case materials etc.
• Additional readings – electronic links to journal articles, chapters etc.

Teaching will concentrate on the critical analysis of the underlying principles and theories, and of their implementation in the Python language and relevant specialised code libraries, (LOs 1 - 4). Students are expected to spend a substantial proportion of their time doing practical programming exercises and researching the underlying principles and theories, and related academic literature (LOs 1 - 4). The practical materials are organised and selected for enhancing students’ understanding of the theories/principles covered.

Formative Assessment:
A variety of both formative and summative assessment methods are used in this module. You will be provided with formative feedback throughout the module through the use of self-assessment practical exercises for which outline solutions will be provided. These outline solutions will be available once the task is completed and will provide you with information to enable you to assess your progress and your level of understanding and provide you with constructive guidance to inform your development. Reflective Exercises within each unit will enable you to apply theory to practice – this is not assessed, but it will support your personal and professional development.

Summative Assessment:
Summative assessment will comprise of one practical coursework worth 100% of the final mark (covering LOs 1- 4). An element of this coursework will be submitted around week 7 to give some formative feedback (30%: L.Os 1, 2), with the main submission being at the end of the module (70%: LOs 3, 4) after which the final feedback will be given.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 24
Face To Face Practical classes and workshops 24
Independent Learning Guided independent study 152
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Project - Practical 100 1,2,3,4 14/15
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Indicative References and Reading List - URL:
Contact your module leader