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Accessibility statement
An image to illustrate Data management and analysis module
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.
This module has been awarded a quality mark by:
Royal Statistical Society Quality Mark logo
This module will provide a broad overview of modern data management and analysis concepts, techniques and tools. It will compare traditional relational databases with an alternative model (a NoSQL database). It 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. You will be introduced to a range of data visualisation and analysis techniques that will instil an understanding of how to start exploring a new dataset.
To ensure that you are comfortable 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 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 data analytics (understanding how data can be used to answer questions).
The module is framed around a narrative that looks at managing and extracting 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 various charts or geographical mapping techniques) and the limitations of the presented mechanisms. 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. Richard Hamming’s famous quote drives this module: ‘The purpose of computing is insight, not numbers’.
Some of the key ideas are:
Introducing data analysis
This unit will start with a data file, such as a spreadsheet, and 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 module's key ideas and help you become familiar with the module software.
Concepts in data management
You will examine three key areas in data management: data architectures and data access (CRUD), data integrity, and transaction management (ACID). Each topic will be illustrated using a relational database and one non-relational alternative. The advantages and limitations of each model will be 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 data management issues. 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 available data. Issues in processing techniques for large and real-time streamed data collections will also be addressed, along with strategies 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.
This module has been awarded a quality mark by the Royal Statistical Society, providing reassurance that the teaching, learning and assessment within this module is of high quality and meets the needs of students and employers.
Royal Statistical Society Quality Mark logo
You’ll get help and support from an assigned tutor throughout your module.
They’ll help by:
Online tutorials run throughout the module. While they’re not compulsory, we strongly encourage you to participate. Where possible, we’ll make recordings available.
Course work includes:
You’ll have access to a module website, which includes:
Additionally, the website includes:
You can study this module on its own or use the credits you gain towards an Open University qualification.
TM351 is an option module in our:
Data management and analysis (TM351) starts once a year – in October.
It will next start in October 2026.
We expect it to start for the last time in October 2027.
As a student of The Open University, you should be aware of the content of the academic regulations, which are available on our Student Policies and Regulations website.
You must have passed or be studying:
Plus, you must also have either:
The OU strives to make all aspects of study accessible to everyone, and this Accessibility Statement outlines what studying TM351 involves. You should use this information to inform your study preparations and any discussions with us about how we can meet your needs.
To find out more about what kind of support and adjustments might be available, contact us or visit our Disability support website.
StartEndRegister byEngland fee
03 Oct 202630 Jun 202710 Sep 2026£2,044
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Please note: your permanent address/domicile will affect your fee status and, therefore, the fees you are charged and any financial support available to you. The fee information provided here is valid for modules starting before 31 July 2027. Fees typically increase annually. For further information about the University's fee policy, visit our Fee Rules.
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