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.
Data Warehousing and Business Intelligence
Data Warehousing and Business Intelligence
This course is part of Database Design and Operational Business Intelligence Specialization
Instructor: Tim Carrington
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4.5
(105 reviews)
Beginner level
Recommended experience
6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explain different data warehousing architectures and multidimensional data modeling
Develop predictive data mining models, including classification and estimation models
Develop explanatory data mining models, including clustering and association models
Skills you'll gain
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Assessments
4 assignments
Taught in English
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This course is part of the Database Design and Operational Business Intelligence Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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.
What's included
7 readings1 assignment1 discussion prompt
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.
What's included
6 readings1 assignment1 discussion prompt
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.
What's included
5 readings1 assignment1 discussion prompt
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.
What's included
4 readings1 assignment1 discussion prompt
Instructor
Instructor ratings
4.4 (24 ratings)Offered by
Recommended if you're interested in Data Management
University of California, Irvine
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University of California San Diego
University of California San Diego
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