University of California, Irvine
Data Warehousing and Business Intelligence
University of California, Irvine

Data Warehousing and Business Intelligence

Tim Carrington

Instructor: Tim Carrington

4,839 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(88 reviews)

Beginner level

Recommended experience

6 hours (approximately)
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

Details to know

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Assessments

4 quizzes

Taught in English

Course

Gain insight into a topic and learn the fundamentals

4.5

(88 reviews)

Beginner level

Recommended experience

6 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

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 quiz1 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 quiz1 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 quiz1 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 quiz1 discussion prompt

Instructor

Instructor ratings
4.4 (20 ratings)
Tim Carrington
University of California, Irvine
6 Courses24,189 learners

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Recommended if you're interested in Data Management

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4.5

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