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Alfredo Deza
and
Noah Gift
Master Scalable Data Engineering with Cutting-Edge Tools
Read more
Master Scalable Data Engineering with Cutting-Edge Tools
Read more
Master Scalable Data Engineering with Cutting-Edge Tools
- Learn to handle massive datasets efficiently with this advanced course
- Gain practical expertise in scaling data systems using modern technologies
- Ideal for data scientists, engineers & professionals with data handling experience
Course Highlights:
- Leverage Celery & RabbitMQ for scalable data consumption
- Optimize workflows with Apache Airflow for efficient management
- Utilize Vector & Graph databases for robust data management at scale
- Hands-on projects for real-world experience in solving data challenges
- Create scalable systems & analyze performance for optimum results
Upskill to design, build & optimize data engineering pipelines that can handle complex, large-scale datasets. Prepare for demanding data roles by mastering advanced techniques with this comprehensive training.
Register for this course and see more details by visiting:
OpenCourser.com/course/9zvusz/advanced
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What's inside
Learning objectives
- Create and manage data pipelines and their lifecycle
- Connect and work with message queues to manage data processing
- Use vector, graph, and key/value databases for data storage at scale
- Create and manage data pipelines and their lifecycle
- Connect and work with message queues to manage data processing
- Use vector, graph, and key/value databases for data storage at scale
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Appropriate for data scientists and engineers as well as professionals with experience in data handling
Builds a foundation for beginners and strengthens an existing foundation for intermediate learners
Develops professional skills and deep expertise in advanced data engineering techniques
Teaches skills, knowledge, and tools that are highly relevant to industry
Prepares for demanding data roles by mastering advanced techniques
Save this course
Save Advanced Data Engineering to your list so you can find it easily later:
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Save
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 Advanced Data Engineering with these
activities:
Connect with experienced data engineers for mentorship
Show steps
Seek guidance and support from individuals who have expertise in data engineering.
Show steps
-
Identify potential mentors through online platforms, industry events, or personal connections.
-
Reach out to mentors and express your interest in learning from their experience.
-
Establish clear expectations and goals for the mentorship relationship.
Review fundamental concepts in data engineering
Show steps
Revisiting fundamental concepts will strengthen the foundation for your learning in this course.
Browse courses on
Big Data
Show steps
-
Identify key concepts in data engineering.
-
Review relevant materials, such as textbooks or online resources.
-
Complete practice exercises to test your understanding.
Review Data Structures & Algorithms
Show steps
Review the basics of data structures and algorithms to prepare for advanced concepts in data engineering.
Browse courses on
Data Structures
Show steps
-
Revisit fundamental data structures such as lists, stacks, queues, and trees.
-
Practice implementing common algorithms like sorting and searching.
25 other activities
Expand to see all activities and additional details
Show all 28 activities
Create a curated list of data engineering resources
Show steps
Gather and organize a collection of valuable resources related to data engineering to supplement your learning.
Show steps
-
Identify reputable sources of data engineering information.
-
Search for and collect articles, tutorials, videos, and other resources.
-
Organize the resources into categories or topics.
-
Create a document or online platform to share your curated list.
Data Engineering Tools and Resources Collection
Show steps
Build a comprehensive understanding of available resources.
Browse courses on
Data Engineering
Show steps
-
Research and identify key tools used in data engineering.
-
Compile a list of tutorials, documentation, and community resources.
-
Share the compiled resources with other learners or contribute to a public repository.
Organize Course Materials
Show steps
Stay organized by compiling and reviewing course materials regularly.
Show steps
-
Gather and organize notes, assignments, and other materials.
-
Review materials frequently to reinforce understanding.
Solve coding challenges on LeetCode
Show steps
Practice solving coding challenges to strengthen your understanding of data structures and algorithms.
Browse courses on
Data Structure
Show steps
-
Identify your skill level and choose appropriate challenges.
-
Break down the problem and design a solution.
-
Implement your solution and test it against different test cases.
-
Review your solution and identify areas for improvement.
Participate in peer study sessions to discuss data engineering topics
Show steps
Participating in peer study sessions provides opportunities to engage with others, clarify concepts, and deepen your understanding.
Show steps
-
Find or create a study group with peers.
-
Discuss course materials, concepts, and assignments.
-
Collaborate on projects or assignments.
Collaborate with Classmates
Show steps
Enhance your learning by discussing concepts and sharing insights with classmates.
Show steps
-
Form or join study groups with other classmates.
-
Organize regular meetings to discuss course material and assignments.
Join a study group for the course
Show steps
Collaborate with other students to discuss course concepts, solve problems, and quiz each other.
Show steps
-
Find or create a study group with peers who have similar learning goals.
-
Establish regular meeting times and stick to them.
-
Take turns leading discussions and presenting summaries of key concepts.
-
Work together to solve problems and understand complex topics.
-
Provide feedback and support to each other.
Data Processing Drills
Show steps
Improve understanding of data processing concepts and techniques.
Browse courses on
Data Processing
Show steps
-
Solve data processing challenges using Celery and RabbitMQ.
-
Implement data ingestion and transformation pipelines.
-
Optimize data processing workflows with Apache Airflow.
Explore Apache Airflow for data workflow management
Show steps
Follow tutorials to gain hands-on experience with Apache Airflow, enhancing your ability to manage data workflows efficiently.
Browse courses on
Apache Airflow
Show steps
-
Set up the necessary environment and dependencies.
-
Find and select suitable tutorials on Apache Airflow.
-
Work through the tutorials, completing all exercises and examples.
Explore Apache Airflow Best Practices
Show steps
Enhance your understanding of Apache Airflow by following guided tutorials that demonstrate best practices.
Browse courses on
Apache Airflow
Show steps
-
Locate and review tutorials focusing on Apache Airflow best practices.
-
Implement recommended practices in your own Airflow workflows.
Big Data Management with Vector and Graph Databases
Show steps
Gain practical experience working with big data and specialized databases.
Browse courses on
Data Management
Show steps
-
Learn about the theory and principles of vector and graph databases.
-
Follow tutorials on implementing these databases in real-world projects.
-
Develop data моделиng and querying skills for complex data structures.
Build a basic data pipeline using Celery and RabbitMQ
Show steps
Practice using Celery and RabbitMQ to build a basic data pipeline, reinforcing your understanding of these tools.
Browse courses on
Celery
Show steps
-
Set up the necessary infrastructure and dependencies.
-
Create a Celery worker and task.
-
Configure RabbitMQ as the message broker.
Attend a workshop on Vector and Graph databases
Show steps
Attend a workshop to gain practical experience with Vector and Graph databases, expanding your knowledge of data storage at scale.
Browse courses on
Vector Databases
Show steps
-
Find and register for a relevant workshop.
-
Attend the workshop and actively participate in discussions.
-
Complete any assignments or exercises provided.
Attend Industry Meetups
Show steps
Network with professionals in the field and learn about industry trends and best practices.
Show steps
-
Identify and attend local meetups related to data engineering.
-
Engage in conversations and share knowledge with other attendees.
Create a visual summary of a course module
Show steps
Create a visual representation of a course module to reinforce your understanding and share it with others.
Show steps
-
Choose a course module to summarize.
-
Identify the key concepts and relationships in the module.
-
Design a visual representation that effectively conveys these concepts.
-
Use appropriate tools and techniques to create your visual summary.
-
Share your visual summary with others for feedback and discussion.
Follow online tutorials on advanced data engineering tools
Show steps
Expand your knowledge of advanced data engineering tools by following online tutorials.
Browse courses on
Celery
Show steps
-
Identify an advanced data engineering tool that you want to learn more about.
-
Search for online tutorials or workshops that cover that tool.
-
Follow the tutorial step-by-step and try out the examples.
-
Experiment with the tool on your own to gain hands-on experience.
-
Share your learnings with others or write a blog post about your experience.
Data-Driven Decision-Making Dashboard
Show steps
Develop practical skills in data visualization and analysis.
Browse courses on
Data Visualization
Show steps
-
Gather and clean real-world data relevant to a specific business or industry.
-
Design and create a data-driven dashboard using visualization tools.
-
Analyze data, identify patterns, and draw insights.
-
Present findings and recommendations based on data analysis.
Develop a presentation on data pipelines for efficient data handling
Show steps
Creating a presentation on data pipelines will help you synthesize your understanding and effectively communicate the key concepts.
Browse courses on
Data Pipelines
Show steps
-
Gather and organize relevant information on data pipelines.
-
Design and create slides that clearly convey the key points.
-
Practice delivering the presentation to improve clarity and engagement.
Solve Scalability Challenges
Show steps
Reinforce your understanding of scalability issues and apply solutions through practice drills.
Browse courses on
Scalability
Show steps
-
Identify common scalability challenges encountered in data engineering.
-
Explore and implement techniques like partitioning, sharding, and load balancing.
Build a mini data engineering project using the course tools
Show steps
Apply your learning by creating a mini data engineering project that utilizes the tools and techniques covered in the course.
Show steps
-
Identify a small-scale data engineering problem that you can solve.
-
Design a solution using the tools and techniques learned in the course.
-
Implement your solution and test it against different scenarios.
-
Document your project and share it with others.
Personal Data Engineering Project
Show steps
Apply knowledge and skills in a real-world project.
Browse courses on
Data Engineering
Show steps
-
Define a data engineering problem or challenge to solve.
-
Design and implement a data pipeline to address the problem.
-
Test and evaluate the performance of the pipeline.
-
Deploy the pipeline and monitor its effectiveness.
Build a data engineering solution using scalable systems and performance analysis
Show steps
Building a data engineering solution will provide you with hands-on experience in designing, implementing, and evaluating scalable systems.
Browse courses on
Scalable Systems
Show steps
-
Define the requirements and scope of the data engineering solution.
-
Design and implement the data pipeline using suitable technologies.
-
Analyze the performance of the system and identify areas for improvement.
Participate in Data Engineering Workshops
Show steps
Gain hands-on experience and develop practical skills through data engineering workshops.
Show steps
-
Locate and register for relevant data engineering workshops.
-
Actively participate in exercises and engage with instructors.
Develop a Data Engineering Pipeline
Show steps
Apply your learning by creating a comprehensive data engineering pipeline that solves a real-world problem.
Browse courses on
Data Integration
Show steps
Participate in Kaggle Competitions
Show steps
Test and refine your data engineering skills by participating in Kaggle competitions.
Show steps
-
Identify and select relevant Kaggle competitions.
-
Work through the competition and apply your data engineering knowledge.
Connect with experienced data engineers for mentorship
Show steps
Seek guidance and support from individuals who have expertise in data engineering.
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms, industry events, or personal connections.
- Reach out to mentors and express your interest in learning from their experience.
- Establish clear expectations and goals for the mentorship relationship.
Review fundamental concepts in data engineering
Show steps
Revisiting fundamental concepts will strengthen the foundation for your learning in this course.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Identify key concepts in data engineering.
- Review relevant materials, such as textbooks or online resources.
- Complete practice exercises to test your understanding.
Review Data Structures & Algorithms
Show steps
Review the basics of data structures and algorithms to prepare for advanced concepts in data engineering.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Revisit fundamental data structures such as lists, stacks, queues, and trees.
- Practice implementing common algorithms like sorting and searching.
25 other activities
Expand to see all activities and additional details
Show all 28 activities
Create a curated list of data engineering resources
Show steps
Gather and organize a collection of valuable resources related to data engineering to supplement your learning.
Show steps
Show steps
Show steps
- Identify reputable sources of data engineering information.
- Search for and collect articles, tutorials, videos, and other resources.
- Organize the resources into categories or topics.
- Create a document or online platform to share your curated list.
Data Engineering Tools and Resources Collection
Show steps
Build a comprehensive understanding of available resources.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Research and identify key tools used in data engineering.
- Compile a list of tutorials, documentation, and community resources.
- Share the compiled resources with other learners or contribute to a public repository.
Organize Course Materials
Show steps
Stay organized by compiling and reviewing course materials regularly.
Show steps
Show steps
Show steps
- Gather and organize notes, assignments, and other materials.
- Review materials frequently to reinforce understanding.
Solve coding challenges on LeetCode
Show steps
Practice solving coding challenges to strengthen your understanding of data structures and algorithms.
Browse courses on
Data Structure
Show steps
Show steps
Show steps
- Identify your skill level and choose appropriate challenges.
- Break down the problem and design a solution.
- Implement your solution and test it against different test cases.
- Review your solution and identify areas for improvement.
Participate in peer study sessions to discuss data engineering topics
Show steps
Participating in peer study sessions provides opportunities to engage with others, clarify concepts, and deepen your understanding.
Show steps
Show steps
Show steps
- Find or create a study group with peers.
- Discuss course materials, concepts, and assignments.
- Collaborate on projects or assignments.
Collaborate with Classmates
Show steps
Enhance your learning by discussing concepts and sharing insights with classmates.
Show steps
Show steps
Show steps
- Form or join study groups with other classmates.
- Organize regular meetings to discuss course material and assignments.
Join a study group for the course
Show steps
Collaborate with other students to discuss course concepts, solve problems, and quiz each other.
Show steps
Show steps
Show steps
- Find or create a study group with peers who have similar learning goals.
- Establish regular meeting times and stick to them.
- Take turns leading discussions and presenting summaries of key concepts.
- Work together to solve problems and understand complex topics.
- Provide feedback and support to each other.
Data Processing Drills
Show steps
Improve understanding of data processing concepts and techniques.
Browse courses on
Data Processing
Show steps
Show steps
Show steps
- Solve data processing challenges using Celery and RabbitMQ.
- Implement data ingestion and transformation pipelines.
- Optimize data processing workflows with Apache Airflow.
Explore Apache Airflow for data workflow management
Show steps
Follow tutorials to gain hands-on experience with Apache Airflow, enhancing your ability to manage data workflows efficiently.
Browse courses on
Apache Airflow
Show steps
Show steps
Show steps
- Set up the necessary environment and dependencies.
- Find and select suitable tutorials on Apache Airflow.
- Work through the tutorials, completing all exercises and examples.
Explore Apache Airflow Best Practices
Show steps
Enhance your understanding of Apache Airflow by following guided tutorials that demonstrate best practices.
Browse courses on
Apache Airflow
Show steps
Show steps
Show steps
- Locate and review tutorials focusing on Apache Airflow best practices.
- Implement recommended practices in your own Airflow workflows.
Big Data Management with Vector and Graph Databases
Show steps
Gain practical experience working with big data and specialized databases.
Browse courses on
Data Management
Show steps
Show steps
Show steps
- Learn about the theory and principles of vector and graph databases.
- Follow tutorials on implementing these databases in real-world projects.
- Develop data моделиng and querying skills for complex data structures.
Build a basic data pipeline using Celery and RabbitMQ
Show steps
Practice using Celery and RabbitMQ to build a basic data pipeline, reinforcing your understanding of these tools.
Browse courses on
Celery
Show steps
Show steps
Show steps
- Set up the necessary infrastructure and dependencies.
- Create a Celery worker and task.
- Configure RabbitMQ as the message broker.
Attend a workshop on Vector and Graph databases
Show steps
Attend a workshop to gain practical experience with Vector and Graph databases, expanding your knowledge of data storage at scale.
Browse courses on
Vector Databases
Show steps
Show steps
Show steps
- Find and register for a relevant workshop.
- Attend the workshop and actively participate in discussions.
- Complete any assignments or exercises provided.
Attend Industry Meetups
Show steps
Network with professionals in the field and learn about industry trends and best practices.
Show steps
Show steps
Show steps
- Identify and attend local meetups related to data engineering.
- Engage in conversations and share knowledge with other attendees.
Create a visual summary of a course module
Show steps
Create a visual representation of a course module to reinforce your understanding and share it with others.
Show steps
Show steps
Show steps
- Choose a course module to summarize.
- Identify the key concepts and relationships in the module.
- Design a visual representation that effectively conveys these concepts.
- Use appropriate tools and techniques to create your visual summary.
- Share your visual summary with others for feedback and discussion.
Follow online tutorials on advanced data engineering tools
Show steps
Expand your knowledge of advanced data engineering tools by following online tutorials.
Browse courses on
Celery
Show steps
Show steps
Show steps
- Identify an advanced data engineering tool that you want to learn more about.
- Search for online tutorials or workshops that cover that tool.
- Follow the tutorial step-by-step and try out the examples.
- Experiment with the tool on your own to gain hands-on experience.
- Share your learnings with others or write a blog post about your experience.
Data-Driven Decision-Making Dashboard
Show steps
Develop practical skills in data visualization and analysis.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather and clean real-world data relevant to a specific business or industry.
- Design and create a data-driven dashboard using visualization tools.
- Analyze data, identify patterns, and draw insights.
- Present findings and recommendations based on data analysis.
Develop a presentation on data pipelines for efficient data handling
Show steps
Creating a presentation on data pipelines will help you synthesize your understanding and effectively communicate the key concepts.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Gather and organize relevant information on data pipelines.
- Design and create slides that clearly convey the key points.
- Practice delivering the presentation to improve clarity and engagement.
Solve Scalability Challenges
Show steps
Reinforce your understanding of scalability issues and apply solutions through practice drills.
Browse courses on
Scalability
Show steps
Show steps
Show steps
- Identify common scalability challenges encountered in data engineering.
- Explore and implement techniques like partitioning, sharding, and load balancing.
Build a mini data engineering project using the course tools
Show steps
Apply your learning by creating a mini data engineering project that utilizes the tools and techniques covered in the course.
Show steps
Show steps
Show steps
- Identify a small-scale data engineering problem that you can solve.
- Design a solution using the tools and techniques learned in the course.
- Implement your solution and test it against different scenarios.
- Document your project and share it with others.
Personal Data Engineering Project
Show steps
Apply knowledge and skills in a real-world project.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Define a data engineering problem or challenge to solve.
- Design and implement a data pipeline to address the problem.
- Test and evaluate the performance of the pipeline.
- Deploy the pipeline and monitor its effectiveness.
Build a data engineering solution using scalable systems and performance analysis
Show steps
Building a data engineering solution will provide you with hands-on experience in designing, implementing, and evaluating scalable systems.
Browse courses on
Scalable Systems
Show steps
Show steps
Show steps
- Define the requirements and scope of the data engineering solution.
- Design and implement the data pipeline using suitable technologies.
- Analyze the performance of the system and identify areas for improvement.
Participate in Data Engineering Workshops
Show steps
Gain hands-on experience and develop practical skills through data engineering workshops.
Show steps
Show steps
Show steps
- Locate and register for relevant data engineering workshops.
- Actively participate in exercises and engage with instructors.
Develop a Data Engineering Pipeline
Show steps
Apply your learning by creating a comprehensive data engineering pipeline that solves a real-world problem.
Browse courses on
Data Integration
Show steps
Show steps
Show steps
Participate in Kaggle Competitions
Show steps
Test and refine your data engineering skills by participating in Kaggle competitions.
Show steps
Show steps
Show steps
- Identify and select relevant Kaggle competitions.
- Work through the competition and apply your data engineering knowledge.
Career center
Learners who complete Advanced Data Engineering will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer designs, constructs, and oversees data systems. These systems handle the collection, storage, and manipulation of data from many sources. They create and optimize pipelines to ensure data reliability and support data-driven workflows. This course can help by giving a stronger foundation in scaling data systems with modern technologies.
Data Warehouse Architect
Data Warehouse Architect
Data Warehouse Architects design and implement data warehouses. They work with a variety of stakeholders, including business analysts, data scientists, and database administrators. This course can help you learn about the latest tools and techniques for handling large-scale data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work with data scientists to gather and prepare data, and then they develop and deploy models that can learn from data and make predictions. This course is a great way to learn about the tools and techniques used to scale data systems for machine learning.
Data Warehouse Developer
Data Warehouse Developer
Data Warehouse Developers design and develop data warehouses. They work with a variety of stakeholders, including business analysts, data scientists, and database administrators. This course can help you learn about the latest tools and techniques for handling large-scale data.
Data Scientist
Data Scientist
A Data Scientist investigates data to find trends and patterns. They build and maintain models to predict outcomes or make recommendations. The course is a great resource for learning how to handle massive datasets efficiently, which is a very common task for a Data Scientist.
Database Developer
Database Developer
Database Developers design and develop databases. They work with a variety of stakeholders, including database administrators, data analysts, and software engineers. This course can help you learn about the latest tools and techniques for handling large-scale data.
Data Analyst
Data Analyst
Data Analysts clean, prepare, and analyze large datasets to provide valuable information to organizations. This course is a great option for learning about modern data handling tools and techniques, which will be essential to any Data Analyst.
Database Administrator
Database Administrator
Database Administrators are responsible for the maintenance and performance of databases. They ensure that data is stored securely and efficiently, and they optimize database performance. This course can help you learn about the latest technologies and techniques for managing large-scale databases.
Data Architect
Data Architect
Data Architects design and implement data architectures. They work with a variety of stakeholders, including business analysts, data scientists, and database administrators. This course can help you learn about the latest tools and techniques for handling large-scale data.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts develop and implement data governance policies and procedures. They work with a variety of stakeholders, including business leaders, data stewards, and IT staff. This course can help you learn about the latest tools and techniques for handling large-scale data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with a variety of technologies, including databases, operating systems, and programming languages. This course can help you learn about the latest tools and techniques for handling large-scale data.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud computing solutions. They work with clients to understand their business needs and then design and implement solutions that meet those needs. This course can help you learn about the latest technologies and techniques for handling large-scale data in the cloud.
Business Analyst
Business Analyst
Business Analysts help organizations understand their business needs and then develop solutions to meet those needs. They work with a variety of stakeholders, including customers, employees, and executives. This course can help you learn about the latest tools and techniques for handling large-scale data.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers. This course can help you learn about the latest tools and techniques for handling large-scale data.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with a variety of stakeholders, including clients, team members, and executives. This course can help you learn about the latest tools and techniques for handling large-scale data.
For more career information including salaries, visit:
OpenCourser.com/course/9zvusz/advanced
Reading list
We've selected ten 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
Advanced Data Engineering.
Save
Covers principles and patterns for designing and building data-intensive applications. Provides in-depth insights into distributed systems, data storage, and data processing.
Save
Provides a comprehensive overview of different NoSQL database technologies, including vector, graph, and key/value databases. Useful for understanding the advantages and use cases of each technology.
Save
A practical guide to using RabbitMQ for message queuing. Covers concepts, configuration, and advanced topics such as clustering and security.
Save
Provides a high-level overview of big data principles and technologies. Covers topics such as data processing, storage, and analytics, with a focus on real-time applications.
Vector database are covered in this course. Elasticsearch is an essential tool for building, deploying, and managing vector databases at scale. provides in-depth guide to doing so.
Save
Provides practical guidance on optimizing Apache Spark performance for large-scale data processing. Focuses on techniques for improving performance, scalability, and efficiency.
Save
Introduces graph databases and their applications for managing connected data. Covers concepts, implementation, and case studies.
Introduces Python libraries for data analysis, such as NumPy, Pandas, and Matplotlib. Covers topics such as data manipulation, data visualization, and statistical analysis.
Focuses on using MapReduce for text processing tasks. Covers topics such as natural language processing, text classification, and information retrieval.
A beginner-friendly introduction to data science. Covers topics such as data analysis, machine learning, and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9zvusz/advanced
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Effort
3 to 6 hours per week for 4 weeks
Level
Advanced
Via
edX
Institution
Pragmatic AI Labs
Instructors
Alfredo Deza
Noah Gift
Session
March 13, 2024
- March 14, 2025
Language
English
Transcript
English
Good to know
Appropriate for data scientists and engineers as well as professionals with experience in data handling
Builds a foundation for beginners and strengthens an existing foundation for intermediate learners
Develops professional skills and deep expertise in advanced data engineering techniques
Teaches skills, knowledge, and tools that are highly relevant to industry
Prepares for demanding data roles by mastering advanced techniques
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