BUSINESS ANALYTICS WITH DATA VISUALISATION - 2025/6

Module Overview

In an era where data is a key driver of innovation and growth, practical expertise in data science is essential for solving complex business challenges. This module uses a case study-based approach to teach data science concepts and techniques, providing students with real-world contexts to develop their skills. Each case study focuses on a different industry or application, such as healthcare, finance, telecommunication, social media, and marketing, allowing students to explore the diverse ways data science creates value. Through hands-on experience with tools, methods, and datasets, students will gain practical insights into solving problems, generating predictions, and making data-driven decisions.

Module provider

Computer Science

Module Leader

MARSHAN Alaa (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

This module emphasizes hands-on learning and practical application, structured around a series of industry-focused case studies. These case studies highlight the role of data science in solving real-world problems, fostering critical thinking, and developing technical expertise. Key areas of focus include:
-Understanding and applying data science principles in industry contexts, such as healthcare, finance, telecommunications, and marketing.
-Exploring advanced analytics techniques, including predictive modelling, machine learning, and recommendation systems.
-Employing various data visualization techniques to communicate findings effectively and support decision-making.

This module provides students with a broad foundation in data science while fostering the ability to adapt skills to different domains and challenges.

Topics Covered Across Case Studies
-Overview of the Data Science Life Cycle and CRISP-DM framework
-Data preparation and pre-processing for case study datasets
-Introduction to machine learning tasks (classification, regression, clustering)
-Evaluation of models and metrics selection

Assessment pattern

Alternative Assessment

The alternative assessment will be an individual coursework with a smaller scope compared to the group coursework, which is about implementing a Business Analytics Solution based on a case study.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module¿s intended learning outcomes described above.

Thus, the summative assessment for this module consists of:

-A mid-term class test, which evaluates students' comprehension of essential data science concepts and their relevance to various business contexts. This addresses LO1 and LO2.

-A group project based on a case study. The students will need to analyse the business problem, determine the business objectives, relevant performance metrics to measure them and provide the business requirements for a business analytics solution and then implement and evaluate it. This will address LO1 to LO5 .

Formative assessment and feedback

Formative assessment and feedback during the lab sessions.

Self-assessment lab exercises will be provided in SurreyLearn. Feedback is also given during the class discussions and lab sessions and as part of the feedback provided for the group project.

Module aims

• The aim of this module is to introduce students to Business Analytics from a practical point of view
• Students will also learn about related concepts such as Data Mining Life Cycle, Machine Learning Algorithms, Model Evaluation and Data Visualisation
• Students will learn about applications of Business Analytics through case studies and practical examples in lab sessions and coursework.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:
help students achieve the intended learning outcomes of the module through in-class discussions and hands-on exercises in the lab sessions and via the coursework.

The learning and teaching methods include:
-Lectures with class discussion
-Lab classes

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COM3032

Other information

Digital Capabilities The advanced AI skills taught in this module provide students digital skills that are fundamental to solving many computer science problems today. Building on COM2028 (Artificial Intelligence) teaches students techniques to use computers to identify patterns in large datasets. These skills are highly valued in industry. Employability This module provides advanced AI, and software skills that are important in solving a many real-life problems today. As the title suggests, students are equipped with practical experience through lab sessions of employing a range of AI techniques to analyse large datasets. The problem-solving skills, theoretical skills, and mathematical and statistical skills are all highly valuable to employers. Global and Cultural Skills Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world. Sustainability This module demonstrates how computers can be used to analyse a wide range of data. These datasets can relate to different topics including the UN sustainability goals. This module will teach techniques to identify patterns in the data and draw conclusions. Resourcefulness and Resilience This module involves practical problem-solving skills that teach a student how to reason about and solve new unseen problems through combining the theory taught with practical technologies for systems that are in everyday use. Students who complete this module will be able to take different data sets and apply a range of techniques to analyse them for patterns. This is a common problem in computer science with widespread applications in industry such as customer data analytics.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.