BUSINESS ANALYTICS WITH DATA VISUALISATION

Description

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. 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 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. Attributes Developed 001 Analyse business objectives and the choice of performance metrics to measure them and translate these into Key Performance Indicators CKP 002 Understand and describe different data mining techniques (e.g. classification, clustering, regression, etc) and how these can be applied to different real-world problems KT 003 Analyse a given business problem and provide a well-reasoned rationale for the choice of tools and techniques CKPT 004 Implement and evaluate a business analytics solution for a given scenario and justify the approach CKPT 005 Appreciate the importance of team-work when carrying out the above activities PT