Module Specification

Foundations of Data

London school of INNOVATION

Module Specification

Foundations of Data: A comprehensive introduction to the world of data



This module provides an enriching journey into the world of data that lies at the heart of our digital society. In this era where data is the new oil, it is pivotal to understand the intrinsic complexities and subtleties that underlie the vast data landscapes.

This module provides a strong foundation for understanding various facets of data. From data storage, transfer, and security to advanced concepts such as data preparation, data models, data mining, big data, data lakes, data tools, and data analytics, this module illuminates the intricate processes behind data handling. But it does not stop at just theory. It also equips you with the most essential and in-demand skills in the realm of data, including governance and communication skills, empowering you with practical skills to tackle real-world problems of this age. The module will also look at the future of data.

Aimed at both technical and managerial professionals, the purpose of this module is to demystify the domain of data and illuminate the profound role data plays in different sectors, innovation, and decision-making, as well as its pivotal role in enabling intelligent digital systems of today and tomorrow....


Code Number of Credits ECTS Credits Framework HECoS code
FD41 15 7 FHEQ - L4 databases (100754)

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Systematic understanding the importance of data across various aspects of modern society and digital ecosystems.
LO2 Intellectual Skills Critically evaluate different data models and their efficacy in contextual real-world scenarios.
LO3 Technical/Practical Skills Apply principles of data handling, including storage, transfer, and ethical usage, in solving problems.
LO4 Technical/Practical Skills Synthesise and apply techniques to uncover insights from complex datasets.
LO5 Technical/Practical Skills Use data tools for managing and visualising information for strategic decision-making.
LO6 Professional/Transferable Skills Demonstrate and apply ethical and professional data skills to make sound judgements.

Assessment Patterns

Weighting Format Outcomes assessed
40% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.
K LO1
I LO2
P LO6
60% Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.
K LO1
I LO2
T LO3
T LO4
T LO5
P LO6

Student workload

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 18.00
AI formative assessment 9.00
Case Study Review 9.00
Workshop/Lab Sessions 13.50
Individual or group assignments 18.00
Independent reading, exploration and practice 51.00
Summative assessment 30.00
150.00

Content Structure

Week Chapter Name Chapter Description
Week 1 Foundations of Data This chapter dives into the primary elements that formulate the basis of data understanding. Students will be guided through the evolution of data as a critical asset in various sectors, touching upon its role in innovation, decision-making, and as an enabler of digital systems. The content emphasises both the theoretical underpinnings and practical implications, setting a solid foundation for the following chapters.
Week 2 Safekeeping Digital Assets Focusing on the pillars of data stewardship, this chapter uncovers the intricacies of data storage and transfer. It provides insights into the secure handling, sharing, and protection of data, which forms the cornerstone of trust and integrity. The chapter aims to highlight the importance of robust systems that facilitate data's secure journey from source to destination.
Week 3 Database Systems and Their Models Advancing into the structured world of data, this chapter explores the myriad of database models and systems available. Students will gain awareness of how data is systematically arranged, accessed, and effectively utilised within these systems, highlighting the significance of each model concerning various business requirements and outcomes.
Week 4 Platforms for Modern Data Needs This chapter scrutinises the frameworks that support the tremendous volume of data in the contemporary age. It outlines the key infrastructure components and platforms that cater to growing data demands, ensuring students appreciate the balance between scalability, efficiency, and sustainability.
Week 5 Data Management Strategies Entering the operational domain of data handling, this chapter details the dynamic interaction with data through various manipulation and management strategies. Students will gain insights into the active role of data specialists in curating, refining, and harnessing information to serve organisational objectives.
Week 6 Processing in the Data Age This chapter unveils the approaches and methodologies involved in the processing of both analytical and real-time data streams. It illustrates the transformative effect of efficient processing techniques in generating timely and accurate analytics for strategic decision-making.
Week 7 Extracting Meaning from Data Delving into the extraction of hidden patterns and predictive insights, this chapter illuminates the data mining and modelling techniques. The focus will be on empowering students with the competencies to transform raw data into meaningful narratives that can inform and drive innovation.
Week 8 Data Architecture Exploring modern data architecture advancements, this chapter underscores the emergence and significance of data lakes and Data as a Service (DaaS) in addressing expansive and diverse data needs. Students will contemplate the implications of these developments on future data strategies and practices.
Week 9 Data Governance This chapter discusses the paramount importance of data governance and the regulatory landscape governing data practices. It aims to instil in students the principles of ethical data use, the impact of regulations on data handling and the criticality of compliance in professional environments.
Week 10 Data Security Dedicating attention to the preservation of data integrity, this chapter delineates the security measures necessary to protect data across its lifespan. Students will learn about the risks, defensive mechanisms, and recovery strategies that ensure the safeguarding of digital assets against evolving threats.

Module References

Type Description
Book A book that has evolved from teaching through several years at Stanford, this is a thorough and practical guide to different aspects of extracting insight from Big Data.
Website Link Brief intro to the different levels of data analytics (as covered in Week 3), and the business value brought about at each level
Book Exceptional book, where 24 experts on data visualizations from different backgrounds have shared their approaches, methods, and thinking. Originally published back in 2010, the ideas are valid and inspirational years on.
Website Link Nice and concise introduction to "Types of Data", as covered in Week 2

Methods of teaching/learning


Introductory lecture (1.50 hours)

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.


Concept learning (knowledge graph) (18.00 hours)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.


AI formative assessment (9.00 hours)

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.


Case Study Review (9.00 hours)

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.


Workshop/Lab Sessions (13.50 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students. There will be an opportunity also for Q&A in every session.


Individual or group assignments (18.00 hours)

Each Workshop/Lab session will be followed by an assignment. Assignments are used to reinforce learning and encourage independent thinking and problem-solving. They help the students identify the gaps in their understanding of the subject and provide them with an opportunity to apply what they have learned in a practical setting.

Assignments can be individual or group-based (teams of 2 to 4). They can take many forms, including essays, presentations, or projects. When they are group-based, teams will be randomly picked by AGS, in order to promote broader teamwork practice. Assignment files will be uploaded to AGS by the students ahead of the next weekly session. Feedback will be provided on each submitted assignment.


Independent reading, exploration and practice (51.00 hours)

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.


Summative assessment (30.00 hours)

Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module.

Programmes this module appears on

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Foundations of Data: A comprehensive introduction to the world of data (FD41)