Module title: Data Wrangling

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

Module code: SET11821
Module leader: Dimitra Gkatzia
School School of Computing
Subject area group: Software Engineering
Prerequisites

n/a

2019/0, Trimester 2, ONLINE,
Occurrence: 002
Primary mode of delivery: ONLINE
Location of delivery: WORLDWIDE
Partner:
Member of staff responsible for delivering module: Dimitra Gkatzia
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
The online module presence has been designed to provide a clear roadmap for students to work through the learning materials. Module specific materials designed to support your studies are detailed in the module descriptors. This include items such as:
• A module introduction/overview, including learning outcomes, and summary of key learning points.
• Units of Learning materials, (subject specific units).
• Recorded keynote lectures/guest speakers (where appropriate).
• Case Studies (and outline solutions).
• Online discussions instigated and moderated by the module leader.
• Self-Assessment Questions,
• Reflective Exercises.
• End of Unit Progress tests.
• Links to core module academic materials; book chapters / journal articles / case materials etc.
• Additional readings – electronic links to journal articles, chapters etc.

In addition to this, online students are provided with online support in the form of:
• Dedicated online administrators who will keep track of student progress and will help you if you are having any problems.
• A dedicated interactive database of frequently asked questions specific to the online learning environment.
• A regular ‘virtual office hour’ will be held where module staff will be available for contact with you.

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. The module is assessed on whether you have met its learning outcomes. You will be provided with formative feedback throughout the module through the use of self-assessment questions and case studies, both of which have outline solutions. This formative feedback 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 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.


Summative Assessment:
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Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Online Lecture 12
Online Practical classes and workshops 24
Online Tutorial 15
Independent Learning Guided independent study 109
Online Guided independent study 40
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Project - Practical 30 1,2 7 HOURS= 12, WORDS= 1000
Project - Practical 70 3,4 14/15 HOURS= 28, WORDS= 1000
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

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 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: Employ specialised techniques for dealing with complex data sets
LO4: Design, develop and critically evaluate data driven applications in Python.

Indicative References and Reading List - URL:

Core - MCKINNEY, W. (2012) PYTHON FOR DATA ANALYSIS: DATA WRANGLING WITH PANDAS, NUMPY, AND IPYTHON.: O’REILLY, 1st ed.
Core - CIELEN, D. & MEYSMAN, A. (2016) INTRODUCING DATA SCIENCE: BIG DATA, MACHINE LEARNING,...: MANNING PUBLICATIONS, 1st ed.
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