STATISTICAL DATA SCIENCE - 2025/6
Module code: COMM071
Module Overview
The module provides for coverage of a variety of statistical methods, including descriptive statistics and validating formulated hypotheses, as well as predictive analytics. The computational foundations and methods of importance to data science are also covered, along with consideration for relevant supporting software and tools.
Module provider
Computer Science and Electronic Eng
Module Leader
THORNE Tom (CS & EE)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 90
Lecture Hours: 24
Tutorial Hours: 11
Laboratory Hours: 11
Guided Learning: 14
Module Availability
Semester 1
Prerequisites / Co-requisites
N/A
Module content
The module includes:
- The basics of probability theory necessary for data science, covering discrete and continuous random variables and common probability distributions used.
- Fundamentals of hypothesis testing.
- Statistical inference for statistical models, applying maximum likelihood and Bayesian approaches.
- Linear regression and validation.
- Communicating results of statistical analyses through reports and visualisation.
- An introduction to the R programming language, and R libraries for data science.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | Individual Coursework | 50 |
| Examination | Invigilated Open-Book Exam (2 hrs) | 50 |
Alternative Assessment
N/A
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate the ability to critically appreciate and apply statistical methods. Thus, the summative assessment for this module consists of:
¿ A coursework project evaluating the practical application of LOs 2, 3 and 5.
¿ An examination, evaluating learning objectives 1 and 4.
Formative assessment - Students will be guided to work on weekly tasks through lab exercises, the solutions to which will provide for feedback on understanding and practice, which will feed forward into the coursework and the exam.
Module aims
- This module aims to introduce students to the necessary background material in statistics and probability that underlie modern data science and machine learning, with applications to real world problems, and to provide students with practical experience in working with these tools.
Learning outcomes
| Attributes Developed | ||
| 001 | Understand the different classes of data science and machine learning problems, and be able to explain which class a problem belongs to. | |
| 002 | Understand and apply probability theory as it relates to data science, including classes of random variables, independence, conditional probability and common probability distribution. | |
| 003 | Choose and execute standard methods from existing R statistical libraries to analyse data and visualise results. | CPT |
| 004 | Be able to explain the difference between Bayesian and frequentist approaches, and interpret the output of a Bayesian data analysis. | CPT |
| 005 | Apply linear regression and classification with appropriate validation. | CPT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to provide students with the knowledge, skills, and practical experience covering the module aims and learning outcomes.
The learning and teaching methods include:
Lectures, to convey and discuss the key concepts.
Tutorials, to discuss the material covered in lectures and discuss solutions to questions set in advance.
Lab sessions, to apply the key concepts taught in each week using R.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COMM071
Other information
The school of Computer Science and Electronic Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: Digital Capabilities This foundational maths for data science module teaches students to use computer to solve mathematical problems. This involves learning to programming and learning to apply these skills to solve technical problems. Employability The ability to draw meaning from large data sets is currently an area that is in high demand in industry. This module teaches the mathematical foundations to allow students to work with large and complex real world datasets. These skills are highly valuable to employers. Global and Cultural Skills Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world. Resourcefulness and Resilience This module involves practical problem-solving skills that teach a student how to work with complex and unstructured data sets. The foundational maths taught in this can be applied to a wide range of different scenarios, giving students new techniques for solving problems.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.