Module Specification

Advanced Business Intelligence and Analytics

London school of INNOVATION

Module Specification

Advanced Business Intelligence and Analytics : Transform Data into Strategic Power



In the current landscape, data is an invaluable asset for businesses seeking to maintain a competitive edge. The ability to navigate and manipulate large data sets effectively is crucial for informed decision-making. This module offers a comprehensive journey through the advanced concepts and practical applications of data science and analytics.

This module covers the entire BI lifecycle, from data collection to deriving actionable insights. You will master ETL processes using leading tools like Apache NiFi and Microsoft SSIS, ensuring the highest data quality. It delves into data warehousing, exploring OLAP, MOLAP, and ROLAP, and extends to cloud-based platforms such as Microsoft Azure Synapse Analytics and AWS Redshift. Real-time data processing with Apache Kafka and Apache Flink is also included.

By engaging with practical tools like Tableau, Power BI, and Python libraries, you will gain hands-on experience in predictive analytics, data mining, and advanced statistical methods. The curriculum also covers natural language processing for text analytics and the creation of BI dashboards. Graduates will emerge with a robust skill set, well-equipped to tackle real-world challenges and drive data-informed strategies in their organisations.


Code Number of Credits ECTS Credits Framework HECoS code
BI71 30 15 FHEQ - L7

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Demonstrate systematic understanding of advanced Business Intelligence and analytics concepts and methodologies within modern enterprises.
LO2 Knowledge and Understanding Exhibit comprehensive knowledge of data warehousing, real-time processing, cloud-based analytics platforms, and advanced data processing techniques.
LO3 Intellectual Skills Critically analyse and evaluate advanced BI and analytics methodologies to propose innovative solutions in complex business contexts.
LO4 Technical/Practical Skills Design and implement advanced ETL processes and data warehousing solutions using industry-standard tools to support strategic decision-making.
LO5 Technical/Practical Skills Develop and deploy advanced analytical models and BI dashboards using tools like Python, R, and Tableau to extract actionable insights.
LO6 Professional/Transferable Skills Critically evaluate ethical considerations and professional responsibilities in data analytics, advocating appropriate solutions in complex business contexts.

Assessment Patterns

Weighting Format Outcomes assessed
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
K LO2
I LO3
T LO4
T LO5
P LO6
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
K LO2
I LO3
P LO6

Student workload

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 36.00
AI formative assessment 18.00
Workshop/Lab Sessions 27.00
Independent reading, exploration and practice 153.50
Summative assessment 64.00
300.00

Prerequisites and Co-requisites

  • Prerequisite: Modern Database Systems

Content Structure

Week Chapter Name Chapter Description
Week 1 Mastering ETL Processes Strengthen your ability to integrate and prepare data by mastering ETL processes. Focus on ensuring data quality through cleansing and transformation using tools like Apache NiFi, Talend, and Microsoft SSIS. This foundational skill ensures that the data used in analysis is reliable and accurate, setting the stage for effective business intelligence.
Week 2 Designing Data Warehouses Build a solid foundation in storing and managing large datasets by designing effective data warehouses. Explore concepts like OLAP, MOLAP, and ROLAP, and learn how to create data marts and schemas. This knowledge enables you to organise data efficiently, supporting robust analytics and timely decision-making.
Week 3 Leveraging Cloud Analytics Harness the power of cloud-based analytics platforms to handle large-scale data processing and storage. Work with services like Microsoft Azure Synapse Analytics, Google BigQuery, and AWS Redshift. This allows you to scale your business intelligence solutions flexibly, meeting the demands of growing data volumes.
Week 4 Implementing Real-Time Processing Discover the importance of immediate insights in today's fast-paced business environment. Learn how to implement real-time data processing using tools like Apache Kafka and Apache Flink. This skill enables you to respond swiftly to changing conditions and make timely, informed decisions.
Week 5 Analysing Data Patterns Uncover meaningful patterns, trends, and correlations within datasets. Use tools like Tableau, Power BI, and Python libraries such as Pandas, NumPy, and Matplotlib. Focus on techniques like regression analysis, clustering, and classification to inform strategic decisions with data-driven insights.
Week 6 Predictive and Prescriptive Analytics Learn how to leverage machine learning techniques for implementing predictive and prescriptive analytics, with hands-on experience of TensorFlow. Develop models that can forecast future trends and behaviours, empowering proactive business strategies.
Week 7 Exploring Advanced Analytics Dive deeper into advanced statistical methods and data mining techniques. Explore association rule mining, decision trees, time series analysis, survival analysis, and neural networks using R and Python. This expertise allows you to extract complex insights and uncover hidden patterns in data.
Week 8 Utilising Text Analytics Expand your analytical toolkit by incorporating natural language processing for text analytics. Use libraries like NLTK and spaCy to analyse unstructured text data. This enables you to gain insights from a wider range of data sources, including documents, social media, and customer feedback.
Week 9 Creating BI Dashboards Learn to create and deploy interactive business intelligence dashboards. Utilise cloud-based BI services like Google Data Studio and Microsoft Power BI Service, to communicate insights and data narratives to facilitate informed decision-making across the organisation.

Module References

There are no module reference contents to display.

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) (36.00 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 (18.00 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.


Workshop/Lab Sessions (27.00 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.


Independent reading, exploration and practice (153.50 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 (64.00 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

Programme Term Type
1 Data Science and Analytics (MSc) 2 Core
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Advanced Business Intelligence and Analytics : Transform Data into Strategic Power (BI71)