PRACTICAL BUSINESS ANALYTICS

Description

In today’s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data. Indicative content includes: Business Data Science, Big Data and Business Analytics Business Intelligence (BI), Decision Support Systems (DSS) and Data Warehouses (DW) Data Mining Life Cycle & Cross Industry Standard Process for Data Mining (CRISP-DM) Data Preparation Introduction to Machine Learning Tasks Association Rule Mining Clustering Algorithms Decision Tree Learning Regression Model Evaluation Visualisation Project Management 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 KCP 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 KCPT 004 Implement and evaluate a business analytics solution for a given scenario and justify the approach KCPT 005 Appreciate the importance of team-work when carrying out the above activities PT