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Tim Carrington
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/lc6exk/data
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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.
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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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
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Reviews summary
Foundational data warehousing & bi concepts
According to learners, this course offers a largely positive experience, particularly excelling in providing a strong theoretical foundation in data warehousing and business intelligence. Many appreciate the clear explanations and well-structured content, which effectively cover core concepts like ETL, multidimensional modeling, and data mining methods. However, a recurring point for improvement is the limited hands-on application. Students often desire more practical labs and real-world case studies to solidify their understanding and prepare them for direct professional application. It is widely considered a great starting point for foundational knowledge.
Ideal for foundational knowledge, potentially too introductory.
"The content is good for a basic overview, but it really stays at a high level. For someone with some experience, it feels a bit too introductory."
"A perfect starting point for understanding data warehousing and business intelligence."
"Decent course for an introduction, but it definitely needs more practical labs or projects."
"I was hoping for more depth in specific data mining algorithms or advanced ETL techniques."
Instructor's explanations are praised for clarity and engagement.
"The instructor's explanations were clear, and the quizzes helped reinforce learning."
"The instructor was engaging. Highly recommend for anyone in BI."
"The instructor simplifies complex ideas beautifully."
"The instructor is knowledgeable."
Provides a clear and comprehensive theoretical understanding.
"This course provided a great introduction to data warehousing concepts and ETL processes."
"Excellent course! The structure is logical, and the material flows well... This course built a strong base for me."
"A perfect starting point for understanding data warehousing and business intelligence. I grasped the core principles without being overwhelmed."
"I appreciated the comprehensive overview of data warehousing and BI. The course nicely explains complex topics."
Reviewers desire more practical labs and real-world projects.
"I wish there were more practical, hands-on labs with real tools."
"I found the lack of practical exercises with actual data sets and software to be a major drawback."
"While the theory is there, the course felt very dry. There were no real-world case studies or opportunities to apply what I learned."
"My main critique is the limited hands-on component; I learn best by doing, and this course was more about understanding concepts."
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
Show steps
Organizing course notes and assignments will help you stay on top of the material and prepare for assessments
Show steps
-
Gather all course materials
-
Create a system for organizing the materials
-
Regularly review the organized materials
Read 'Data Warehousing Fundamentals' by Lawrence Corr
Show steps
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
Show steps
-
Acquire a copy of the book
-
Set aside time for reading
-
Take notes and highlight important concepts
Read chapters on DWH architecture
Show steps
Reviewing chapters on DWH architecture will help reinforce the concepts covered in our Overview of Data Warehousing Module
Browse courses on
Data Warehousing
Show steps
-
Gather the necessary reading materials
-
Set aside a specific time for reading
-
Take notes and highlight key points
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group
Show steps
Participating in a study group will provide opportunities to discuss course material, ask questions, and learn from peers
Show steps
-
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
Show steps
Completing data mining exercises will help reinforce the concepts covered in our Data Mining for Clustering and Association module
Browse courses on
Data Mining
Show steps
-
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
Show steps
Following guided tutorials on data mining methods will help deepen the understanding gained in our Data Mining for Prediction and Explanation module
Show steps
-
Identify reputable sources for tutorials
-
Set aside time to follow the tutorials
-
Take notes and practice the techniques
Create a data warehouse design document
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
Find a suitable data mining competition
-
Gather a team or work individually
-
Develop a data mining solution
-
Submit your solution and track your progress
Organize course notes and assignments
Show steps
Organizing course notes and assignments will help you stay on top of the material and prepare for assessments
Show steps
Show steps
Show steps
- Gather all course materials
- Create a system for organizing the materials
- Regularly review the organized materials
Read 'Data Warehousing Fundamentals' by Lawrence Corr
Show steps
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
Show steps
Show steps
Show steps
- Acquire a copy of the book
- Set aside time for reading
- Take notes and highlight important concepts
Read chapters on DWH architecture
Show steps
Reviewing chapters on DWH architecture will help reinforce the concepts covered in our Overview of Data Warehousing Module
Browse courses on
Data Warehousing
Show steps
Show steps
Show steps
- Gather the necessary reading materials
- Set aside a specific time for reading
- Take notes and highlight key points
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group
Show steps
Participating in a study group will provide opportunities to discuss course material, ask questions, and learn from peers
Show steps
Show steps
Show steps
- 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
Show steps
Completing data mining exercises will help reinforce the concepts covered in our Data Mining for Clustering and Association module
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- 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
Show steps
Following guided tutorials on data mining methods will help deepen the understanding gained in our Data Mining for Prediction and Explanation module
Show steps
Show steps
Show steps
- Identify reputable sources for tutorials
- Set aside time to follow the tutorials
- Take notes and practice the techniques
Create a data warehouse design document
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
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.
Business Intelligence 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.
Data Scientist
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.
Data Engineer
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.
Database Administrator
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.
Business Analyst
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.
Market Research Analyst
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.
Marketing Analyst
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.
Financial Analyst
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.
Risk Analyst
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.
Operations Research Analyst
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.
Management Consultant
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.
Data Architect
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.
Software Engineer
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.
Statistician
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.
For more career information including salaries, visit:
OpenCourser.com/course/lc6exk/data
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Warehousing and Business Intelligence.
Is the definitive guide to Spark, the open-source framework for big data processing. It is an essential resource for anyone who wants to learn more about Spark and how to use it to manage and process big data.
Classic in the field of reinforcement learning, providing a comprehensive overview of the concepts and algorithms. It is an excellent resource for those who want to learn more about reinforcement learning and its applications in artificial intelligence.
Classic in the field of data warehousing and is widely regarded as one of the most authoritative guides to dimensional modeling. It is an essential read for anyone who wants to design and implement a successful data warehouse.
Is the definitive guide to Hadoop, the open-source framework for big data processing. It is an essential resource for anyone who wants to learn more about Hadoop and how to use it to manage and process big data.
Save
Provides a comprehensive overview of computer vision, covering all the major concepts and algorithms. It is an excellent resource for those who want to learn more about computer vision and its applications in areas such as image recognition, object detection, and video analysis.
Provides a comprehensive overview of business intelligence, covering all the major concepts and technologies. It is an excellent resource for those who want to learn more about the role of business intelligence in decision making.
Provides a comprehensive overview of natural language processing, with a focus on using the Natural Language Toolkit (NLTK) library in Python. It is an excellent resource for those who want to learn more about natural language processing and how to use it to analyze text data.
Save
Comprehensive textbook on data mining, covering all the major concepts and techniques. It is an excellent resource for those who want to learn more about data mining and its applications in business intelligence.
Save
Provides a practical guide to data visualization, focusing on how to create effective and informative visualizations. It valuable resource for those who want to learn more about how to use data visualization to communicate insights.
Save
Provides a practical guide to predictive analytics, focusing on how to use data to make better decisions. It valuable resource for those who want to learn more about the potential of predictive analytics in business.
Provides a comprehensive overview of data warehousing, including its concepts, architectures, and methodologies. It is an excellent resource for those who are new to the field or want to refresh their understanding of the fundamentals.
Provides a gentle introduction to machine learning, with a focus on its applications in business. It good choice for those who want to learn about machine learning without getting bogged down in the technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lc6exk/data
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Effort
4 weeks of study, 1-3 hours/week
Level
Introductory
Via
Coursera
Institution
University of California, Irvine
Instructor
Tim Carrington
Language
English
This course belongs to a
collection
called
Database Design and Operational Business Intelligence. It's comprised of three courses:
- The Nature of Data and Relational Database Design
- Business Intelligence and Visual Analytics
- Data Warehousing and Business Intelligence (viewing)
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