Study information

Foundations of Data Science - 2025 entry

MODULE TITLEFoundations of Data Science CREDIT VALUE15
MODULE CODECOMM038 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 12
Number of Students Taking Module (anticipated) 40
DESCRIPTION - summary of the module content

Everything from data visualization to machine learning to neural networks relies on an underpinning mathematical framework. A deep understanding of modern data science therefore requires a deep understanding of the maths and statistics behind it. This course will cover the key concepts in statistics and linear algebra that are necessary for cutting edge research in data science. This course is appropriate for students who want a thorough grounding in the mathematics that will enable them to understand, implement and contribute to the development of advanced methods.

AIMS - intentions of the module
This module aims to give students the mathematical tools to understand, implement and modify algorithms in data science and machine learning.  This module also will equip you with the ability to rigorously evaluate, compare and analyse data using appropriate statistical methods. This knowledge is essential for students hoping to develop and research new data science techniques and algorithms while also giving a proper foundation for evaluation of methods and results in practical applications.
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. Demonstrate a comprehensive understanding of linear algebra, in particular, techniques and algorithms which are used in machine learning.

  2. Apply appropriate statistical methods to rigorously compare alternatives, evaluate uncertainty and model data.

Discipline Specific Skills and Knowledge:

  1. Implement software for addressing real world, small- and large-scale data analysis problems using appropriate libraries and report results with necessary statistical rigour. 

  2. Comprehend, use and modify machine learning algorithms.

Personal and Key Transferable/ Employment Skills and Knowledge:

  1. Choose and apply appropriate techniques for the analysis of data.

  2. Evaluate and explain cutting edge data science techniques on a theoretical and practical basis.

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

  • Exploratory Data Analysis

  • Data and Sampling Distributions

  • Statistical Theory

  • Confirmatory Data Analysis

  • Model Selection

  • Vectors, Matrices and Tensors

  • Linear algebra theory

  • Orthogonality and Eigendecomposition

  • Numerical Linear Algebra

  • Selected Advanced Topics

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36 Guided Independent Study 114 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category  Hours of study time  Description 
Scheduled Learning and Teaching 24 Lectures
Scheduled Learning and Teaching 12 Workshops
Guided Independent Study 114 Suggested reading, workshop prep

 

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
Weekly Workshops 12 hours All In workshop, verbal discussion with TA/Module Lead

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 30 Written Exams 70 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of credit Size of the assessment e.g. duration/length ILOs assessed 
Written Exam – Closed book 70 2-hour exam All
Continuous Assessment (a weekly hand in exercise) 30 Weekly Hand in All

 

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
Written Exam  Written exam All Referral/deferral Period
Continuous assessment (a weekly hand in exercise) Problem set  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:

  • Cohen, Mike X. Practical linear algebra for data science. " O'Reilly Media, Inc.", 2022.

  • Nield, Thomas. Essential Math for Data Science. " O'Reilly Media, Inc.", 2022.

  • Emmert-Streib, F., Moutari, S. and Dehmer, M., 2022. Mathematical foundations of data science using R. Walter de Gruyter GmbH & Co KG.

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; Linear Algebra;

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