Study information

Data Science Portfolio - 2025 entry

MODULE TITLEData Science Portfolio CREDIT VALUE15
MODULE CODECOMM041 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 10
Number of Students Taking Module (anticipated) 40
DESCRIPTION - summary of the module content

Data scientists work with a huge diversity of data types, across a wide variety of applications. A data scientist must be able to rapidly learn enough domain knowledge to effectively use their skills in programming, statistics and machine learning to answer questions and provide insight for partners and customers. This module consists of a number of guided mini projects, using different data sources like text, images, simulation and survey data from different domains e.g. social media, engineering, weather prediction and more. At the end of this course students will have built a small portfolio demonstrating different data science skills to potential employers.

AIMS - intentions of the module

The aim of the module is to expose students to real world data sets and have them complete several guided but open-ended short projects. This will not only help build competence in application of techniques learned in other modules but also provide key preparation for larger projects in the final term or post award.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge:

  1. Clean, validate, analyse and summarise realistic datasets using basic data science techniques

  2. Effectively communicate the results of a data science project through a project report.

Discipline Specific Skills and Knowledge:

  1. Demonstrate the ability to work with complex and diverse data types

  2. Critically evaluate the output of statistical and machine learning methods

Personal and Key Transferable/ Employment Skills and Knowledge:

  1. Discuss and evaluate findings with problem stakeholders from different domains

  2. Independently learn and use new techniques and methods of analysis

SYLLABUS PLAN - summary of the structure and academic content of the module

  • Numerical Data

  • Image Data

  • Text Data

  • Geographic Data

  • Data summary

  • Applications of machine learning

  • Data presentation and Visualisation

  • Technical Writing

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 12 Guided Independent Study 138 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category  Hours of study time  Description 
Scheduled Learning and Teaching 12 Workshops
Guided Independent Study 138 Individual Assessed Work

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of the assessment e.g. duration/length ILOs assessed Feedback method
Workshops 12 hours All In person discussions with module lead/TA

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of credit Size of the assessment e.g. duration/length ILOs assessed  Feedback method
Portfolio  100 Approx 5000 words All Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original form of assessment Form of re-assessment  ILOs re-assessed Time scale for re-assessment
Portfolio Portfolio All Referral/Deferral period

 

RE-ASSESSMENT NOTES
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
 
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit a further assessment as necessary. If you are successful on referral, your overall module mark will be capped at 50%.
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading

  • Van der Aalst, 2016, Process Mining: Data Science in Action, Springer Heidelberg

  • Kotu, Desphande, 2018, Data Science: Concepts and Practice, Morgan Kaufmann

  • Schutt, O’Neill, 2014, Doing Data Science: Straight Talk from the Frontline, O’Reilly

Web-based and electronic resources:

  • ELE 

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 28th November 2024 LAST REVISION DATE Wednesday 21st May 2025
KEY WORDS SEARCH Data Science; Project

Please note that all modules are subject to change, please get in touch if you have any questions about this module.