This module addresses some of the key concepts required for the traditionally important area of data management and the increasingly important area of data analytics. You'll gain a practical, legal and ethical understanding of how to access, query and manage data collections using traditional relational databases and contemporary NoSQL approaches. Using real-world datasets, standard software packages and data visualisation techniques, you'll learn how to organise and analyse data collections to answer questions about the world and develop an appreciation of user needs surrounding data systems.
| Course facts | |
|---|---|
| About this course: | |
| Course code | TM351 |
| Credits | 30 |
| OU Level | 3 |
| SCQF level | 10 |
| FHEQ level | 6 |
| Course work includes: | |
| 2 Tutor-marked assignments (TMAs) | |
| 7 Interactive computer-marked assignments (iCMAs) | |
| End-of-module assessment | |
| No residential school | |
This module will provide you with a broad overview of the concepts, techniques and tools of modern data management and analysis. It will compare traditional relational databases with an alternative model (a NoSQL database), and will help you learn how to choose the most appropriate means of storing and managing data, depending on the size and structure of a particular dataset and its intended use. You will be introduced to preliminary techniques in data analysis, starting from the position that data is used to answer a question, and introduced to a range of data visualisation and analysis techniques that will instil an understanding of how to start exploring a new data set.
To ensure that you are comfortable with handling datasets, you will explore a range of real-world datasets to illustrate the key concepts in the module. Sources such as data.gov.uk, the World Bank, and a range of other national and international agencies may be used to provide appropriate data. You will spend approximately equal time between issues in data management (technical and socio-legal issues in storing and maintaining datasets), and issues in data analytics (understanding how data can be used to answer questions).
The module is framed around a narrative that looks at how to manage and extract value and insight from a range of increasingly large data collections. At each stage, a comparison will be drawn between different ways of representing the data (for example, using different sorts of charts or geographical mapping techniques), and limitations of the mechanisms presented. To enable you to get a feel for the use of data, each stage will also include an overview of some data analysis techniques, including summary reporting and exploratory data visualisation. This module is driven by Richard Hamming's famous quote: 'The purpose of computing is insight, not numbers'.
Some of the key ideas are:
Introducing data analysis
Starting with a data file such as a spreadsheet, this unit will provide you with a brief introduction to some basic operations on simple data files. This will give you an opportunity to study an outline of the key ideas in the module and help you become familiar with the module software.
Concepts in data management
You will look at three key areas in data management: data architectures and data access (CRUD), data integrity, and transaction management (ACID). Each of these topics will be illustrated using a relational database, and one non-relational alternative. The advantages and limitations of each model are discussed.
Legal and ethical issues
Here you will consider the legal and ethical issues involved in managing data collections. You will be required to obtain and read (parts of) the Data Protection Act and the Freedom of Information Act, and demonstrate how these apply to issues in data management. You will also consider privacy, ownership, intellectual property and licensing issues in data collection, management, retrieval and reuse.
Concepts in data analytics
These sections will focus on using data to answer a real question; the focus will be on exploratory techniques (such as visualisation) and formulating a question into a form that can be answered realistically using the data that is available. Issues in processing techniques for large and real-time streamed data collections will also be addressed along with techniques and technologies (such as MapReduce) for handling them. In this part of the module you will use a statistical package such as the python scientific libraries and/or ggplot2 to visualise the data and carry out appropriate analyses.
If you are considering progressing to The computing and IT project (TM470), this is one of the OU level 3 modules on which you could base your project topic. Normally, you should have completed one of these OU level 3 modules (or be currently studying one) before registering for the project module.
You must have passed, or be studying:
Plus, you must also have either:
Module website, online study materials, sample datasets and module software.
Throughout your module studies, you'll get help and support from your assigned module tutor. They'll help you by:
Module tutors also run online tutorials throughout the module. Where possible, recordings of online tutorials will be made available to students. While these tutorials won't be compulsory for you to complete the module, you're strongly encouraged to take part.
The assessment details for this module can be found in the facts box.
The OU strives to make all aspects of study accessible to everyone. The Accessibility Statement below outlines what studying this module involves. You should use this information to inform your study preparations and any discussions with us about how we can meet your needs.
All of this module's study materials are online. Online materials are composed of pages of text with images; audio and video clips of 2–30 minutes long (all with transcripts/subtitles), diagrams, interactive media, animations, and multiple-choice self-assessed quizzes. Online materials also include links to external resources, online forums and online tutorial rooms. Module software is provided in a virtualised computing environment.
If you're using printed materials as part of reasonable adjustments to support your studies, note that printed versions of online materials are unavailable for this module.
This module has online tutorials. Although not compulsory, tutorials will help you consolidate your learning.
Online practical work – programming, data investigations and analysis – is a required component of assessment. This includes some optional collaborative group work. This module extends your knowledge of using an online OpenStudio environment which allows show-and-tell and crowd-sourced feedback.
Mathematical and scientific symbols and expressions are used throughout the module and you will be required to use such notation within assessment.
The study materials contain a considerable number of diagrams, graphs and photographs. Reading, interpreting and producing examples of these is an important part of the study of this module and is assessed. Figure descriptions are provided for most figures. The module also uses a range of visualisation techniques to interpret, present and explain data.
You may be required to search for, and make use of, third-party material online and this is assessed. Alternatives for required/assessed research material can be provided to enable you to meet the learning outcomes of the module.
In this module you will be working with specialist reading material such as computer code and some mathematical notation. There is also some graphical notation and some visualisations. These are delivered online.
This module has tutor-marked assignments (TMAs) and an end-of-module assessment (EMA) that you must submit via the online TMA/EMA service and interactive computer-marked assignments completed online.
You will receive feedback from your tutor on your submitted Tutor-Marked Assignments (TMAs). This will help you to reflect on your TMA performance. You should refer to it to help you prepare for your next assignment.
All University modules are structured according to a set timetable and you will need time management skills to keep your studies on track. You will be supported in developing these skills.
Data management and analysis (TM351) starts once a year – in October.
This page describes the module that will start in October 2025.
We expect it to start for the last time in October 2026.
This course is expected to start for the last time in October 2026.