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

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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

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.

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

7 readingsTotal 35 minutes

Need for Data Warehousing5 minutes
Data Warehousing Architectures5 minutes
Extract, Transform, Load (ETL)5 minutes
Data Marts5 minutes
Operational Data Stores5 minutes
Data Warehousing in the Cloud5 minutes
Supplemental Resources5 minutes

1 assignmentTotal 30 minutes

Module 1 Knowledge Check30 minutes

1 discussion promptTotal 30 minutes

Activity30 minutes

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.