Module title: Data Analytics and Wrangling

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

Module code: SET10614
Module leader: Taoxin Peng
School School of Computing
Subject area group: Software Engineering

Module Code CSN08614
Module Title Scripting for Cyber Security and Networks
Examples of Equivalent Learning A suitable mathematics qualification including statistics.
Experience with relational databases.

2018/9, Trimester 2, FACE-TO-FACE,
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MYANMAR
Member of staff responsible for delivering module: Taoxin Peng
Module Organiser:

Learning, Teaching and Assessment (LTA) Approach:
The module will be introduced by an Edinburgh Napier lecturer who will deliver an initial 25 hours of lectures, practical work and tutorials the additional hours will be delivered by our partner Info Myanmar College (IMC). The module will run over 5 consecutive weeks with the later four weeks being delivered by IMC staff. Lectures are used to introduce underlying principles and the practical and tutorial work is used to broaden & develop deeper understanding of the subject area. This is mixed with student-centred work, such as research questions and online exercises, as well as group activities such as discussion groups, group presentation exercises, and peer review.

As the module is delivered in a block over 5 consecutive weeks, standard Academic Calendar weeks and trimesters are not applicable for tables below.

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:
Assessment will comprise two practical projects. The first coursework (Week 3) will cover LO 1 and 2. Fundamentals and theories as well as the practical skills will be assessed by the second coursework (Week 5) (LOs 3, 4, 5), which will include a mix of reports and practical assignments.

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 48
Independent Learning Guided independent study 128
Total Study Hours200
Expected Total Study Hours for Module200

Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Project - Practical 30 1,2 1 HOURS= 12, WORDS= 0
Project - Practical 70 3,4,5 1 HOURS= 28, WORDS= 0
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

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.

Indicative References and Reading List - URL:

Please contact your Module Leader for details
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