Module Code: BHSCDAD
Long Title Data Application Development
Title Data Application Development
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: Arghir Moldovan
Module Author: Arghir Moldovan
Departments: School of Computing
Specifications of the qualifications and experience required of staff

MSc and/or PhD degree in computer science or cognate discipline. May have industry experience also. Experience with tools, frameworks and programming languages for data analytics.
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Design algorithms and implement key programming patterns and constructs for data analytics.
LO2 Apply practical skills using a professional tool/language of data analytics (e.g., R, Python).
LO3 Assess the challenges associated with data application development and compare and contrast best practices for the data application development.
LO4 Investigate parallel and distributed computing and write programs for processing datasets in distributed computing and cloud computing environments using relevant programming paradigms and techniques.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

See Section 4.2 Entry Procedures and Criteria for the programme.
 

Module Content & Assessment

Indicative Content
Data Applications Design and Development
Data science methodologies (e.g., KDD, CRISP-DM) Software design and development processes, use-case modelling, flowcharts, data-flow modelling Documentation and reporting
Tools and Frameworks
Tools and frameworks for data applications development (e.g., R Studio, JupyterLab) Programming languages for data analytics (e.g., R, Python) Use of support libraries (e.g., R pacakges, Pandas)
Data Structures and Functions
Data types and data structures for analytics Indexing and working with data structures Creating and working with functions
Extract, Transform, Load
Read/write data from/to different file formats (e.g., csv, xlsx, xml, json) Extract data from the Internet (e.g., connecting to APIs, web scraping) Programmatically connecting to databases, Create/Read/Update/Delete (CRUD) Operations Dealing with missing values Developing programs for data processing activities (e.g., data extraction, cleaning, merging, aggregation, analysis, reporting)
Data Visualisation
Data visualisation principles Data visualisation libraries (e.g., ggplot2) Dashboard frameworks (e.g., R Shiny)
Big Data Programming
Challenges associated with programming for big data Utilisation of cloud computing platforms for big data processing Distributed programming frameworks (e.g., Hadoop, Spark) Distributed programming paradigms (e.g., MapReduce) Design patterns
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities.
Assessment Type: Practical (0260) % of total: 30
Assessment Date: Week 8 Outcome addressed: 2
Non-Marked: No
Assessment Description:
This assessment will consist of a practical in-class test, that will assess learners’ competences on programmatically processing and analysing datasets.
Assessment Type: Project % of total: 70
Assessment Date: Sem 2 End Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
The terminal assessment will consist of a project with both practical and research elements that will evaluate all learning outcomes. Learners will have to identify and carry out a series of analyses of at least two large datasets that complement each other, utilising appropriate programming languages, tools and techniques.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
The reassessment strategy for this module will consist of a project that will assess all learning outcomes. Students who fail the module will be afforded an opportunity to do the repeat project over the Summer months.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 24 Per Semester 2.00
Tutorial No Description 24 Per Semester 2.00
Independent Learning No Description 77 Per Semester 6.42
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 24 Per Semester 2.00
Tutorial No Description 24 Per Semester 2.00
Independent Learning No Description 77 Per Semester 6.42
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • J. D. Long, Paul Teetor. (2019), R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics, 2nd Edition. O'Reilly Media, p.600, [ISBN: 978-1492040682].
  • Wes McKinney. (2017), Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd Edition. O'Reilly Media, p.550, [ISBN: 978-1491957660].
Supplementary Book Resources
  • Todd Morley. (2019), Data Science Design Patterns, 1st Edition. Addison-Wesley Professional, p.512, [ISBN: 978-0134000053].
  • Tom White. (2015), Tom White. Hadoop: the Definitive Guide; Storage and Analysis at Internet Scale, 4th Edition. O'Reilly Media, p.756, [ISBN: 978-1491901632].
  • Bill Chambers, Matei Zaharia. (2018), Spark: The Definitive Guide: Big Data Processing Made Simple, 1st Edition. O’Reilly Media, p.606, [ISBN: 978-1491912218].
This module does not have any article/paper resources
Other Resources
Discussion Note: