Study information

Introduction to Data Science and Data Systems - 2025 entry

MODULE TITLEIntroduction to Data Science and Data Systems CREDIT VALUE30
MODULE CODECOMM443Z MODULE CONVENER Tianjin Huang (Coordinator), Dr Xiaoyang Wang (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated)
DESCRIPTION - summary of the module content

In this module, you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling and visualisation, statistical modelling, network analysis and text mining. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises, individual study and interactive tasks will consolidate your learning and provide the foundations for later study. You will also be introduced to the ways in which data is stored within a computer system. You will learn about a variety of types of databases including those based on the structured query language (SQL). You will develop a theoretical understanding about how data should be organised and will learn how to access and modify the data in a database from an application. 

AIMS - intentions of the module

This module will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will evolve to reflect current issues in data science, providing student with the tools to formulate data science problems and construct pipelines to begin to solve them technically. In addition, this module will instil students with an appreciation of the different ways that data can be stored. By introducing multiple approaches (e.g. SQL-based) students will learn how to select the most appropriate storage for a given application, taking into account the complexities around accessing and writing data. Students will also learn how to construct software to connect an application to a database securely.

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. Design a data science pipeline for a given problem in a chosen domain. 
2. Understand how to design applications that uses an API to access and modify data stored in a database. 

Discipline Specific Skills and Knowledge

3. Articulate a decision problem to be solved with data science affecting business or society. 
4. Contrast between different types of database tools. 

Personal and Key Transferable / Employment Skills and Knowledge

5. Present the results of a piece of data science work in the form of a report. 
6. Design an appropriate data storage scheme for a project within a chosen problem domain. 
SYLLABUS PLAN - summary of the structure and academic content of the module

Example topics (with associated exercises and seminar discussions)  

  • Data wrangling  
  • Data visualisation 
  • Statistics  
  • Probability  
  • Regression 
  • Networks and Text Data 
  • Three-level Architecture and Data model 
  • Entity-Relational Modelling  
  • Relational Model  
  • Normalization 
  • SQL – Data Manipulation Language  
  • SQL – Data Definition Language 
  • Physical Database Design.

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 100 Guided Independent Study 200 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities   100 Asynchronous online learning activities
Guided independent study 200 Including preparation for online content, reflection on taught material, wider reading and completion of assessments
     

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Problem sets/open-ended questions 2 hours per week 1-6 Model answers and discussion with tutor in forum on ELE
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Data Science Coursework  50 20 hours coursework 1,3,5,6 Written
Data Systems Coursework 50 20 hours coursework 2,4,5,6 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
Data Science Coursework Replacement Coursework 1,3,5,6 Referral/deferral period
Data Systems Coursework Replacement Coursework 2,4,5,6 Referral/deferral period
       

 

RE-ASSESSMENT NOTES
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:

Web based and Electronic Resources:

  • ELE.

Other Resources:

 

Reading list for this module:

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

CREDIT VALUE 30 ECTS VALUE 15
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING Yes
ORIGIN DATE Tuesday 30th September 2025 LAST REVISION DATE Thursday 27th November 2025
KEY WORDS SEARCH Database; design; modelling

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