Course Code: MSM552

Synopsis

This course presents data analytics as a key modern approach for decision making in business organisations. It examines the key aspects of Business Analytics based on Cross-Industry Process for Data Mining (CRISP-DM) framework. Students will learn to apply CRISP-DM by going through a series of projects involving data exploration, data visualisation, text mining and predictive modelling. By walking students through such projects, they will gain experience in turning data into important insights that may improve organisational performance. 本课程介绍数据分析作为商业组织决策制定的关键现代方法。它审视了基于跨行业数据挖掘流程 (CRISP-DM) 框架分析的关键方面。 学生将通过一系列涉及数据探索、数据可视化、文本数据分析和预测模型等项目来学习应用 CRISP-DM。 通过引导学生完成此类项目，他们将 获得将数据转化为可以提高组织绩效的重要见解的经验。
Level: 5
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Introduction to Business Analytics 商业分析简介
  • An Overview of CRISP-DM 跨行业数据挖掘流程概述
  • Data Visualisation 数据可视化
  • Process and challenges in a Data Visualisation project 数据可视化项目的过程和挑战
  • Business Performance Dashboard 业务绩效仪表盘
  • An overview of Text Mining 文本挖掘概述
  • Process and challenges in a Text Mining project 文本挖掘项目的过程和挑战
  • Text Data Preparation: Parsing and Feature Extraction 文本数据准备：解析和特征提取
  • Text Transformation and Vectorisation文本转换和矢量化
  • Topic Modelling 主题分析
  • Sentiment analysis 情感分析
  • Text Mining and Predictive Modelling 文本挖掘与预测模型

Learning Outcome

  • Design analytics solutions using the CRISP-DM framework 使用 CRISP-DM 框架设计分析解决方案
  • Inspect and prepare data for visualisation 检查并准备可视化数据
  • Choose appropriate data visualisation techniques based on given data 根据给定的数据选择适当的数据可视化技术
  • Create data visualisation dashboard using a software package 使用软件包创制数据可视化仪表板
  • Prepare text data for mining and analysis 准备文本数据进行挖掘和分析
  • Implement the various approaches to text mining using appropriate softwares 使用适当的软件实现多种文本挖掘方法
  • Propose text mining and predictive modelling-based business analytics solutions 提出基于文本挖掘与预测模型的商业解决方案
Part-time Programmes / CET Courses