Data Warehousing and Business Intelligence

Overview

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This course builds on “The Nature of Data and Relational Database Design” to extend the process of capturing and manipulating data through data warehousing and data mining. Once the transactional data is processed through ETL (Extract, Transform, Load), it is then stored in a data warehouse for use in managerial decision making. Data mining is one of the key enablers in the process of converting data stored in a data warehouse into actionable insight for better and faster decision making. By the end of this course, students will be able to explain data warehousing and how it is used for business intelligence, explain different data warehousing architectures and multidimensional data modeling, and develop predictive data mining models, including classification and estimation models. IN addition, students will be able to develop explanatory data mining models, including clustering and association models.

Syllabus

Overview of Data Warehousing

Welcome to Module 1, Overview of Data Warehousing. In this module, we will overview data warehousing and data warehousing architectures. We will also define the Extract, Transform, Load (ETL) process as well as touch on data warehousing in the cloud and practice these through a short quiz. Finally, in our activity we will differentiate between the Kimball and Inmon design approaches for data warehouse architecture.

Multidimensional Modeling for Data Warehousing

Welcome to Module 2, Multidimensional Modeling for Data Warehousing. In this module, we will go over data modeling for data warehousing. We will also learn the steps needed to construct a multidimensional data model and differentiate between star schema and snowflake schema. These will be practiced through a short quiz. Finally, we will create a normalized snowflake schema in our activity.

Data Mining for Prediction and Explanation

Welcome to Module 3, Data Mining for Prediction and Explanation. In this module, we will overview the data mining process and data mining methods. We will also identify the steps in a data mining process and differentiate between data mining methods. We will practice identifying these through a short quiz. In our activity, we will also select what data mining methods are best for a particular data set.

Data Mining for Clustering and Association

Welcome to Module 4, Data Mining for Clustering and Association. In this module, we will go over unsupervised data mining for explanatory modeling. We will also learn the definitions for clustering and segmentation, K-means clustering, association, and market basket analysis and practice these through a short quiz. Finally we will practice identifying clusters in a dataset through our activity.