Module Code: H8DSP
Long Title Data Science Project
Title Data Science Project
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 20
Module Coordinator: Arghir Moldovan
Module Author: Arghir Moldovan
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Master’s degree in a computing or cognate discipline. May have industry experience also.

 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply knowledge, skills and competencies acquired during the programme of study and work placement to the analysis and solution of a real-world or research problem.
LO2 Specify, design and implement a medium-to-large scale project related to the area of study using ethically sourced datasets.
LO3 Carry out project planning and time management activities to meet strict project deadlines.
LO4 Develop and enhance interpersonal communication, presentation and storytelling skills.
LO5 Document, present and defend the project through a technical document, presentation, and demonstration of relevant artefact, product or data analysis.
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

Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
 

Module Content & Assessment

Indicative Content
Time and Project Management
This seminar will give students an overview of how to use their time effectively and how to manage multiple tasks at the same time. The primary focus will be on how a student can best manage their time to reach their project goals.
GitHub
This seminar will give an overview on how to use GitHub for code versioning. Students are requested to have a GitHub Account set up before attending this class.
Requirements Gathering
This seminar will give an overview on requirements gathering, a critical step in any project.
Academic Writing and Referencing
This seminar will give an overview on academic writing, how to reference correctly (including how to use a reference management system such as Zotero).
Conducting a literature review
This seminar will give an overview of how to conduct a literature review, including how to search for relevant research articles using online research engines and databases (e.g., Google Scholar, IEEE Xplore, etc.)
LaTeX
This seminar will provide an overview of using LaTeX typesetting system.
Data Pipelining
This seminar will provide an overview of data pipelining between various sources and databases
Mid-point Presentation Guide
This seminar will discuss what is required at the Mid-Point Presentations.
Presentation Skills
This seminar will contain an overview of how to present information clearly and effectively.
Understanding the Marking Scheme
This seminar will overview the marking scheme and how students to ensure that their project avails of the marking allowances.
Showcase Deliverables
This seminar will provide an overview of the materials required for the project showcase (e.g., poster, demo, photos, profile description)
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided both by the lecturer and supervisor on an ongoing basis.
Assessment Type: Project % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
Learners will implement a data science project
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
Learners who fail the Data Science Project module will be required to do a repeat project where all learning outcomes will be examined.

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 Classroom & Demonstrations (hours) 24 Per Semester 2.00
Independent Learning Independent learning (hours) 226 Per Semester 18.83
Total Weekly Contact Hours 2.00
 

Module Resources

Recommended Book Resources
  • Lipston, C.. (2005), How to Write a BA Thesis: A Practical Guide from Your First Ideas to Your Finished Paper, University of Chicago Press.
  • Swetnam, D.& Swetnam, R.. (2000), Writing Your Dissertation: The bestselling guide to planning, preparing and presenting first-class work (3rd Ed, Hachette UK, ).
This module does not have any article/paper resources
Other Resources
Discussion Note: