Certificate in Data Mining & Business Intelligence

Introduction

Business intelligence is a collection of tools, techniques and approaches which includes data mining, data science, artificial intelligence, machine learning, neural networks, data visualisation, deep learning and others that identify the sources of data, discern patterns, associations, clusters and relationships in the data to turn data into meaningful information. That information can be used to produce the answers to big questions, diagnose and solve difficult or impossible problems, and even predict the future. This course explores the various forms and architecture of business intelligence and how business intelligence and its associated technologies are used to help organisations make operational and strategic decisions. The course also introduces data mining, as it is used to support business intelligence through analysing vast amounts of data to produce information and recommendations by application of association rules, K-Nearest Neighbour (KNN) analysis, clustering, and Market Basket Analysis.

Objectives

Benefits of Attending

• Use data-based tools to make more accurate and timely decisions
• Understand the mechanics and architecture behind business intelligence, data mining and Big Data
• Utilise data mining techniques for predictive analysis, to assist in making decisions about the presentand predicting future events
• Visualise data using business intelligence and data mining visualisation methods and tools

Training Methodology

This highly interactive course employs the latest in business intelligence and data mining technologies. In addition to practical exercises with business intelligence graphics and data mining algorithms, participants will see real-life business examples of business intelligence and data mining in use with an emphasis on predictive analytics. You will learn how to form questions and develop analytics techniques which are then transformed into business intelligence. You will also practice the statistical techniques behind data mining and producing recommendations for and predictions about the business. Additionally, you will learn how to apply artificial intelligence, neural networks, and machine learning to the application of business intelligence and data mining. The course is primarily focused on doing rather than listening, so you will be engaged in exercises for nearly 70% of the class time.

Who Should Attend?

This course is designed for Managers, Executives, Data Scientists, Data Analysts, Business Analysts, professionals working with data analytics or business intelligence, and anyone who needs to understand how to use data to make better decisions

Competencies

• Understanding business intelligence
• Performing online analytical processing
• Investigating the Extraction, Transformation, Load (ETL) process
• Understanding data analytics
• Understanding data mining
• Exploring Cross-Industry Standard Process for Data Mining (CRISP-DM)
• Implementing predictive analytics
• Using cluster analysis
• Exploring data mining models

Course Outline

Incorporating Business Intelligence into Organizational Decision Making

• The challenge of decision making
• The challenge of asking and answering questions What is “business intelligence”?
• The business intelligence value proposition
• The evolution of business intelligence Business intelligence taxonomy
• Business intelligence management issues

The Architecture of Business Intelligence Data Warehousing

• The data warehouse and its relationships
• Data architecture: fact table, stars, and snowflakes
• Building the data warehouse – Extraction, Transformation, Load (ETL)
• Data marts

Extracting Intelligence from the Data

• Business intelligence: the front-end
• The concept of dimensions in data
• Online analytical processing
• Visualising and working with data cube

The Architecture of Data Mining

• Big Data: types and structure
• Business challenges of Big Data Identifying sources of data
• Transforming Big Data into value
• Technologies of Big Data: Hadoop, Pig, and Hive
• Clouds: public, private and hybrid
• Applying Big Data to business problems

Create a Data Mine – A Step-By-Step Process

• Cross-Industry Standard Process for Data Mining (CRISP-DM)
• Use of data mining in business
• Data mining models
• Descriptive, predictive, and prescriptive models

Employing the Models – Identifying Patterns and Anomalies

• Classification
• Association rules
• Clustering and cluster analysis

Employing the Models – Predicting the Future

• Market Basket Analysis
• Time series
• Predictive analytics and regression