Data Warehousing and Business Intelligence from Coursera

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.

What's inside

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Helps students get ahead in their managerial roles by preparing them for data-driven decision-making

Provides hands-on experience through ETL (Extract, Transform, Load) and data mining processes

Offers a comprehensive overview of data warehousing architectures, including Kimball and Inmon design approaches

Covers data mining methods, including classification, estimation, clustering, and association models

Taught by Tim Carrington, an experienced data warehousing and data mining expert

Requires prior knowledge of data management concepts and SQL

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Warehousing and Business Intelligence with these activities:

Organize course notes and assignments

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Organizing course notes and assignments will help you stay on top of the material and prepare for assessments

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Gather all course materials
Create a system for organizing the materials
Regularly review the organized materials

Read 'Data Warehousing Fundamentals' by Lawrence Corr

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This book provides a comprehensive overview of data warehousing concepts and will complement the course material effectively

View Agile Data Warehouse Design: Collaborative... on Amazon

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Acquire a copy of the book
Set aside time for reading
Take notes and highlight important concepts

Read chapters on DWH architecture

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Reviewing chapters on DWH architecture will help reinforce the concepts covered in our Overview of Data Warehousing Module

Browse courses on Data Warehousing

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Gather the necessary reading materials
Set aside a specific time for reading
Take notes and highlight key points

Five other activities

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Join a study group

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Participating in a study group will provide opportunities to discuss course material, ask questions, and learn from peers

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Find or create a study group
Set regular meeting times
Prepare for each meeting by reviewing the material
Actively participate in discussions

Complete data mining exercises

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Completing data mining exercises will help reinforce the concepts covered in our Data Mining for Clustering and Association module

Browse courses on Data Mining

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Gather a set of data mining exercises
Set aside time to work on the exercises
Review the solutions and identify areas for improvement

Learn about data mining methods

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Following guided tutorials on data mining methods will help deepen the understanding gained in our Data Mining for Prediction and Explanation module

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Identify reputable sources for tutorials
Set aside time to follow the tutorials
Take notes and practice the techniques

Create a data warehouse design document

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Creating a data warehouse design document will help solidify the concepts covered in our Multidimensional Modeling for Data Warehousing module

Browse courses on Data Warehousing

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Gather the requirements for the data warehouse
Design the data warehouse schema
Develop a data loading plan
Document the design decisions

Participate in a data mining competition

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Participating in a data mining competition will provide practical experience and challenge you to apply the concepts covered in this course

Browse courses on Data Mining

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Find a suitable data mining competition
Gather a team or work individually
Develop a data mining solution
Submit your solution and track your progress

Career center

Learners who complete Data Warehousing and Business Intelligence will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst combines their knowledge of data analysis tools and techniques with a business understanding to help organizations make data-driven decisions. Those who want to lead in this position can gain a strong foundation in managing large datasets with data warehousing through this course.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Similar to a Data Analyst, a Business Intelligence Analyst collects and analyzes data to understand business trends and patterns. Data Warehousing and Business Intelligence teaches the fundamental concepts of data mining, modeling, and data warehousing that lend themselves to this role.

See salaries and explore the career path for Business Intelligence Analyst

Data Scientist

Data Scientists use advanced analytical techniques to extract meaningful insights from data. Their skills overlap with the data mining techniques taught in this course, including clustering, association, estimation, and classification models.

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Data Engineer

A Data Engineer designs, builds, and maintains data management systems. The course will cover foundational knowledge of data warehousing and multidimensional modeling, which are essential for designing and managing large-scale data systems.

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Database Administrator

A Database Administrator is responsible for managing and maintaining databases. This course will help build a strong foundation for managing data, including data warehousing and multidimensional modeling techniques.

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Business Analyst

A Business Analyst helps organizations understand their business processes and make data-driven decisions. This course will provide a foundation in data warehousing and business intelligence, which are essential for understanding data in a business context.

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Market Research Analyst

A Market Research Analyst collects and analyzes data to understand market trends and consumer behavior. This course can help build a strong foundation in data mining techniques, which are essential for understanding data and making informed decisions.

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Marketing Analyst

A Marketing Analyst uses data to understand marketing campaigns and measure their effectiveness. This course teaches data mining techniques that are valuable for understanding data and making informed decisions related to marketing.

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Financial Analyst

A Financial Analyst uses data to make investment recommendations. Data warehousing and business intelligence concepts are essential for understanding and making sense of financial data.

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Risk Analyst

A Risk Analyst uses data to assess and mitigate risk. Data warehousing and business intelligence concepts are essential for understanding and making sense of risk-related data.

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Operations Research Analyst

An Operations Research Analyst uses data to improve the efficiency of operations. Data warehousing and business intelligence concepts are essential for understanding and making sense of operational data.

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Management Consultant

A Management Consultant advises organizations on how to improve their performance. Data warehousing and business intelligence concepts are essential for understanding an organization's data and making informed recommendations.

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Data Architect

A Data Architect designs and builds data management systems. This course helps build a strong foundation in data warehousing and modeling, which are essential for designing and managing data systems.

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Software Engineer

A Software Engineer designs, develops, and maintains software systems. Data warehousing and business intelligence concepts are essential for designing and building software systems that handle data.

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Statistician

A Statistician collects, analyzes, and interprets data. This course may be helpful for building a foundation in data mining, which is a specialized field within statistics.

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