ANALYTICS TOOLS FOR BUSINESS ECONOMICS

Description

The aim of this module is to introduce many of the important new ideas in data mining and business analytics, explain them as statistical framework, and describe some of their applications in Business Economics and Finance. Data mining is the process of mining large quantity of data to extract useful information. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well-known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including at firms such as Meta. An understanding of business analytics and data mining concepts and techniques can offer a valuable advantage in the competition for jobs and placements. This sector remains one of just a few areas showing consistent growth in terms of job opportunities and salaries even during recession. The module content will focus on a selected set of critical areas in data analytics and the software tools used. As an indication of the kind of concepts that will be covered, below is an indicative set of topics: -An introduction to data mining process model for business and management -Data pre-processing, visualisation and exploratory analysis used in large datasets -Use of AI including neural network in data mining and its application in risk analysis -Classification, decision trees and their applications. -Association and rule mining and their applications to business and management -Data mining predictive models and their applications -Accessing and collecting data from the Web and introduction to text mining. Introduce the fundamentals of data mining and its application to business analytics. Identify the various algorithms of data mining and how to effectively apply them to real-world problems. Explore leading data mining using examples from Business Economics. Attributes Developed 001 Identify the key steps of the data mining process. KP 002 Apply key methods using analytics software for turning diverse data into useful insights. KPT 003 Demonstrate the ability to use data mining and coding in Python. KPT 004 Construct text mining models for analysing complex data structures. CKPT 005 Recognise and tools needed to reveal patterns and valuable information hidden in large data sets. CKT