Core Module Information
Module title: Data Analytics and Wrangling

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

Module code: SET10714
Module leader: Valerio Giuffrida
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

Module Code CSN08714 SET09702
Module Title Scripting for Cyber Security and Networks Database Technology
Examples of Equivalent Learning A suitable mathematics qualification including statistics.
Experience with relational databases.

Description of module content:

The challenges of contemporary data acquisition and analysis provide new challenges as unstructured data and information reaches the web, e.g. text reviews, social media data, etc. 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, data analysis and evaluation modelling techniques as well as visualisation approaches.

The module covers the following topics:
• Data Preparation – Data collection, feature generation and data selection.
• Data Pre-processing – data quality, data cleaning, data integration
• Data Analysis – techniques of analysing data, such as correlation, regression, forecasting, classification, clustering, including a variety of machine learning methods that are widely used in data mining
• Post processing – data visualisation, interpretation, evaluation
• Data types and formats: numerical and time series, textual, unstructured
• Data sources and interfaces: open data, APIs, social media, web-based
• Techniques for dealing with heterogeneous data sets
• Developing Data Driven Applications in Python

Tools used in this module include Weka, OpenRefine, Pandas, SciPy, NLTK, or R.

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:

Upon completion of this module you will be able to:
LO1: Analyse the concepts and process of data analysis, including pre-processing and preparation of data.
LO2: Analyse and evaluate modelling methods and techniques in data analysis.
LO3: Integrate data analysis algorithms to conduct data analysis and visualisation.
LO4: Critically interpret and evaluate results generated by analysis techniques.
LO5: Integrate specialised techniques for dealing with heterogeneous data sets.

Full Details of Teaching and Assessment
2022/3, Trimester 1, FACE-TO-FACE,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: UK PARTNER
Partner:
Member of staff responsible for delivering module: Dimitra Gkatzia
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
The delivery is flexible, with a number of face to face workshops, running within a UK Higher Apprenticeship scheme. Apprentices will have to work in their own time for the majority of the module, supported via distance learning techniques. Face to Face workshops gives apprentices time to discuss material in a group, and engage on group learning activities as well as review independent learning activities.
The module is presented via a combination of distance learning lectures and reading material (covering LOs 1- 5) and supporting online practical sessions (also covering LOs 1-5). The practical sessions are “hands-on” which is reflected throughout the teaching of the module. The lecture material will therefore cover both the theory and implementation of data analysis and wrangling concepts.
Students will receive formative feedback on their progress throughout the module via the practical sessions.

DATA ANALYTICS
Students will be expected to do self-oriented research and provide a critical analysis and evaluation of much of the theories behind these subjects (LOs 1, 2, 3). Teaching will concentrate on the critical analysis of that information and on practical exercises (LOs 3). Students are expected to spend a large proportion of their time doing comprehensive reading and practice. Tutorial and practical materials are well organised and selected for enhancing students’ understanding of the theories/principles covered.

DATA WRANGLING
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 1-2, 4-5). 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. The practical materials are organised and selected for enhancing students’ understanding of the theories/principles covered.





Formative Assessment:
To support formative feedback, the Software Engineering subject group utilise a lab based teaching approach across their provision. During these lab sessions, staff will support and evaluate student progress and provide feedback on how well they are progressing with their work. All modules in the subject group also require students to demonstrate their coursework on submission to provide further formative feedback on how the work could be improved.

Summative Assessment:
Fundamentals and theories as well as the practical skills will be assessed by a set of coursework. Element one will be report based and will cover LOs 1-2 and element two will be a practical assignment and will cover LOs 3-5. The combined coursework covers the entire module.



Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Practical classes and workshops 18
Independent Learning Guided independent study 182
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 3 HOURS= 12, WORDS= 0
Project - Practical 70 3,4,5 4 HOURS= 28, WORDS= 0
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

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