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Joe Reis
In this course, you’ll model, transform, and serve data for both analytics and machine learning use cases. You’ll explore various data modeling techniques for batch analytics, including normalization, star schema, data vault, and one big table, and you’ll use dbt to transform a dataset based on a star schema and one big table.
Read more
In this course, you’ll model, transform, and serve data for both analytics and machine learning use cases. You’ll explore various data modeling techniques for batch analytics, including normalization, star schema, data vault, and one big table, and you’ll use dbt to transform a dataset based on a star schema and one big table.
Read more
In this course, you’ll model, transform, and serve data for both analytics and machine learning use cases. You’ll explore various data modeling techniques for batch analytics, including normalization, star schema, data vault, and one big table, and you’ll use dbt to transform a dataset based on a star schema and one big table.
You’ll also compare the Inmon vs Kimball data warehouse architectures. You’ll learn about basic machine learning concepts, then model and transform a tabular dataset for machine learning purposes. You’ll also be modeling and transforming unstructured images and textual data. You’ll explore distributed processing frameworks such as Hadoop MapReduce and Spark, and perform stream processing.
Register for this course and see more details by visiting:
OpenCourser.com/course/b9k747/data
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Suitable for undergraduates seeking a foundation in data engineering, including students from computer science, statistics, or business backgrounds
Suited for entry-level data analysts, data scientists, and machine learning engineers looking to strengthen foundational skills
Teaches industry-standard data modeling techniques for batch analytics and machine learning
Covers Hadoop MapReduce and Spark, essential frameworks for distributed processing in data engineering
Might require prior knowledge of basic data science and machine learning concepts
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Reviews summary
Comprehensive data engineering foundations
According to learners, this course offers a comprehensive foundation in data modeling, transformation, and serving, particularly for data engineering and machine learning use cases. Students frequently highlight the practical, hands-on labs, often utilizing AWS services and tools like dbt, as a significant strength. The lectures are consistently praised for clarity and the instructors for making complex topics accessible. While generally perceived as highly relevant for professional work, some learners suggest a need for clearer prerequisites, noting that it may be challenging for true beginners. A few also desired more advanced depth in certain modules like Spark or Hadoop, indicating it functions better as a broad overview than an in-depth specialization. Overall, it's considered an excellent course for solidifying understanding of data pipelines.
Effectively bridges data needs for both analytics and ML.
"One of the best courses I've taken on data engineering. The focus on both analytics and machine learning use cases is brilliant."
"It covers everything from basic data modeling concepts... to modeling and transforming unstructured images and textual data for machine learning purposes."
"While the dbt part was very useful for my analytics work, I felt the ML specific data modeling could have used more examples."
Instructors are knowledgeable and explain complex concepts well.
"The instructors from DeepLearning.AI made complex topics easy to grasp."
"I found the lectures clear and the concepts well-explained, indicating knowledgeable instructors."
"The instruction quality is top-notch; I particularly appreciated the discussion on data transformation best practices."
Hands-on activities provide invaluable practical experience.
"The hands-on labs using AWS services were invaluable. Highly recommend for data engineers looking to solidify their understanding."
"The practical assignments really drove the learning home for me. I consider it truly a gem for understanding data pipelines."
"I found the dbt part particularly useful for my current role. Overall, it was a practical course and the labs were well-structured."
"The hands-on exercises cemented my understanding, and I would highly recommend it for practical application."
Covers a wide range of essential data engineering topics.
"This course is incredibly comprehensive, covering everything from basic data modeling concepts like star schema to advanced topics like distributed processing with Spark."
"I found it an excellent course for anyone entering data engineering, as it covers a wide array of topics, from relational modeling to handling unstructured data for ML."
"It offered me a good introduction to various data modeling techniques and distributed processing, providing a broad overview that's great for understanding the landscape."
"I appreciated the balance between theory and practical application, finding it essential for anyone working with data at scale."
Requires foundational knowledge; may challenge true beginners.
"I found this course somewhat challenging due to the assumed prior knowledge. While it states it's for professionals, a clear list of prerequisites would be better."
"The pace was too fast for me in some sections, especially Spark. I had to supplement a lot with external resources, so it's not for true beginners in data."
"I consider it a good introduction, but I didn't find the deep dives I was hoping for, as some topics felt a bit rushed or too introductory for an 'advanced' course."
"I gained a solid overview, but felt certain areas, like distributed processing, could have gone into greater depth for advanced learners."
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 Modeling, Transformation, and Serving with these
activities:
Review data modeling techniques from previous coursework or resources
Show steps
Solidify your understanding of data modeling principles before beginning the course.
Browse courses on
Data Modeling
Show steps
-
Gather notes, assignments, and materials from previous data modeling courses.
-
Review key concepts such as normalization, star schema, and data vault.
Complete practice problems on data modeling and normalization
Show steps
Reinforce data modeling concepts by applying them to practical exercises.
Browse courses on
Data Modeling
Show steps
-
Find practice problems on data modeling and normalization.
-
Solve the problems using appropriate techniques.
Create a glossary of key data modeling and transformation terms
Show steps
Enhance your understanding by defining and organizing important terms related to the course.
Browse courses on
Data Modeling
Show steps
-
Identify key terms from the course materials.
-
Define each term concisely and accurately.
-
Organize the terms alphabetically or by category.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review DBMS Fundamentals by Ramez Elmasri
Show steps
Review core database concepts to strengthen your understanding of data modeling and transformation.
Show steps
-
Read Chapters 1-3: Database Concepts, Data Modeling, and Relational Model.
-
Complete the practice exercises at the end of each chapter.
Participate in peer-led review sessions
Show steps
Engage in discussions and exchange ideas with peers to enhance understanding.
Browse courses on
Data Modeling
Show steps
-
Form a peer group with classmates.
-
Meet regularly to discuss course concepts, assignments, and projects.
-
Provide constructive feedback and support to each other.
Follow tutorials on Apache Spark for Distributed Processing
Show steps
Gain practical knowledge of Spark's capabilities for data processing at scale.
Browse courses on
Apache Spark
Show steps
-
Find tutorials on Spark's RDD, DataFrame, and SQL interfaces.
-
Follow along with the tutorials and complete the exercises.
Create a Data Transformation Plan for a Real-World Dataset
Show steps
Develop hands-on experience in transforming a real-world dataset to meet specific requirements.
Browse courses on
Data Transformation
Show steps
-
Identify a raw dataset of interest.
-
Define the desired transformed dataset structure.
-
Perform data cleaning and transformation using appropriate tools or libraries.
-
Document the data transformation steps and rationale.
Contribute to open-source data transformation projects
Show steps
Gain hands-on experience and contribute to a real-world project related to data transformation.
Browse courses on
Open Source
Show steps
-
Identify open-source data transformation projects.
-
Find issues or areas where you can contribute.
-
Follow project guidelines and submit your contributions.
Review data modeling techniques from previous coursework or resources
Show steps
Solidify your understanding of data modeling principles before beginning the course.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Gather notes, assignments, and materials from previous data modeling courses.
- Review key concepts such as normalization, star schema, and data vault.
Complete practice problems on data modeling and normalization
Show steps
Reinforce data modeling concepts by applying them to practical exercises.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Find practice problems on data modeling and normalization.
- Solve the problems using appropriate techniques.
Create a glossary of key data modeling and transformation terms
Show steps
Enhance your understanding by defining and organizing important terms related to the course.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Identify key terms from the course materials.
- Define each term concisely and accurately.
- Organize the terms alphabetically or by category.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review DBMS Fundamentals by Ramez Elmasri
Show steps
Review core database concepts to strengthen your understanding of data modeling and transformation.
Show steps
Show steps
Show steps
- Read Chapters 1-3: Database Concepts, Data Modeling, and Relational Model.
- Complete the practice exercises at the end of each chapter.
Participate in peer-led review sessions
Show steps
Engage in discussions and exchange ideas with peers to enhance understanding.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Form a peer group with classmates.
- Meet regularly to discuss course concepts, assignments, and projects.
- Provide constructive feedback and support to each other.
Follow tutorials on Apache Spark for Distributed Processing
Show steps
Gain practical knowledge of Spark's capabilities for data processing at scale.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Find tutorials on Spark's RDD, DataFrame, and SQL interfaces.
- Follow along with the tutorials and complete the exercises.
Create a Data Transformation Plan for a Real-World Dataset
Show steps
Develop hands-on experience in transforming a real-world dataset to meet specific requirements.
Browse courses on
Data Transformation
Show steps
Show steps
Show steps
- Identify a raw dataset of interest.
- Define the desired transformed dataset structure.
- Perform data cleaning and transformation using appropriate tools or libraries.
- Document the data transformation steps and rationale.
Contribute to open-source data transformation projects
Show steps
Gain hands-on experience and contribute to a real-world project related to data transformation.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source data transformation projects.
- Find issues or areas where you can contribute.
- Follow project guidelines and submit your contributions.
Career center
Learners who complete Data Modeling, Transformation, and Serving will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/b9k747/data
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Rating
4.5
Effort
28 hours to complete
Level
Intermediate
Via
Coursera
Institutions
Amazon Web Services
DeepLearning.AI
Instructor
Joe Reis
Language
English
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