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Josh Bernhard ,
Mike Yi,
Judit Lantos,
David Drummond,
Andrew Paster,
Juno Lee,
and
Luis Serrano
Data engineers are in high demand. Take Udacity's Data Engineering for Data Scientists course and learn to build data pipelines and more through hands-on projects.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic SQL
- Python for data science
- JSON
- Relational database proficiency
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/f497e1/data
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What's inside
Syllabus
You will get an introduction to the data engineering for data scientists course and project. The lessons include ETL pipelines, natural language pipelines, and machine learning pipelines.
Read more
Syllabus
You will get an introduction to the data engineering for data scientists course and project. The lessons include ETL pipelines, natural language pipelines, and machine learning pipelines.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Suits data scientists who are beginners in data engineering
Taught by notable experts in data engineering
Provides hands-on experience through projects
Explores industry-standard data engineering techniques
Covers essential concepts for data scientists seeking to enhance their skills in data engineering
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Reviews summary
Practical data engineering pipelines
According to learners, this course offers a practical and hands-on approach to data engineering, particularly focusing on building various data pipelines including ETL, natural language, and machine learning pipelines. Students appreciate that the curriculum is highly relevant for developing skills currently sought after in the industry, enabling them to confidently work with large datasets. The projects are engaging and provide valuable practical experience. However, prospective learners should be aware that a solid foundation in prerequisites like SQL, Python for data science, JSON, and relational databases is essential for success, as the course moves at a professional pace and builds directly on this prior knowledge.
Aligns directly with in-demand data engineering roles.
"I feel much better prepared for data engineering roles after completing this program."
"The skills taught are highly applicable and relevant to current industry needs and job descriptions."
"This course helped me bridge the gap between data science theory and practical engineering application in my work."
Covers diverse data pipeline types relevant to data science.
"I gained a clear understanding of ETL, NLP, and ML pipelines, which are all vital skills for a data engineer."
"The course structure effectively covers different types of data integration and processing."
"It provided a good overview of how to engineer features and deploy machine learning models in a pipeline."
Provides practical experience in building data pipelines.
"I found the hands-on projects extremely helpful for applying what I learned to real scenarios."
"The ability to build complete, real-world data pipelines was the strongest takeaway for me."
"I appreciate the emphasis on practical application through comprehensive and engaging projects."
Requires a strong background in foundational data skills.
"You really need a solid grasp of SQL and Python for data science before starting this course."
"I struggled a bit without deeper knowledge of relational databases and JSON beforehand."
"Ensure you meet the prerequisites, as the course moves quickly and builds directly on them."
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 Engineering with these
activities:
Review Python Data Analysis Handbook
Show steps
Build stronger foundational knowledge in Python Data Analysis, which is covered in the first few weeks of this course.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Read Chapters 1 and 2 of the book.
-
Complete the exercises in Chapters 1 and 2.
Review Designing Data-Intensive Applications
Show steps
This book covers principles and patterns of building large-scale data-intensive applications, a topic that is central to this course.
View
Secret Colors
on Amazon
Show steps
-
Read Chapters 1-3 of the book.
-
Complete the exercises in Chapters 1-3.
Practice SQL queries
Show steps
Reinforce your understanding of SQL by practicing queries on a dataset of your choice.
Browse courses on
SQL
Show steps
-
Find a dataset that you are interested in.
-
Write 10 SQL queries to retrieve different types of information from the dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice data engineering tasks
Show steps
Develop your proficiency in data engineering tasks by working on a small project.
Browse courses on
Data Engineering
Show steps
-
Find a small dataset to work with.
-
Write a script to extract, transform, and load the data.
-
Deploy the script to run on a regular schedule.
Write a blog post on data engineering
Show steps
Solidify your understanding of data engineering by writing a blog post that explains a specific concept or technique.
Browse courses on
Data Engineering
Show steps
-
Choose a topic related to data engineering that you are interested in.
-
Research the topic and write an outline for your blog post.
-
Write the blog post.
-
Publish the blog post on a platform of your choice.
Build a data pipeline for a real-world problem
Show steps
Apply the skills you have learned in this course to a real-world problem by building a data pipeline.
Browse courses on
Data Pipelines
Show steps
-
Identify a real-world problem that you can solve with a data pipeline.
-
Design the data pipeline.
-
Implement the data pipeline.
-
Deploy the data pipeline.
-
Monitor the data pipeline.
Attend a data engineering workshop
Show steps
Expand your knowledge and skills in data engineering by attending a workshop.
Browse courses on
Data Engineering
Show steps
-
Research data engineering workshops in your area.
-
Register for a workshop that interests you.
-
Attend the workshop.
Review Python Data Analysis Handbook
Show steps
Build stronger foundational knowledge in Python Data Analysis, which is covered in the first few weeks of this course.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1 and 2 of the book.
- Complete the exercises in Chapters 1 and 2.
Review Designing Data-Intensive Applications
Show steps
This book covers principles and patterns of building large-scale data-intensive applications, a topic that is central to this course.
View
Secret Colors
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 of the book.
- Complete the exercises in Chapters 1-3.
Practice SQL queries
Show steps
Reinforce your understanding of SQL by practicing queries on a dataset of your choice.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Find a dataset that you are interested in.
- Write 10 SQL queries to retrieve different types of information from the dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice data engineering tasks
Show steps
Develop your proficiency in data engineering tasks by working on a small project.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Find a small dataset to work with.
- Write a script to extract, transform, and load the data.
- Deploy the script to run on a regular schedule.
Write a blog post on data engineering
Show steps
Solidify your understanding of data engineering by writing a blog post that explains a specific concept or technique.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Choose a topic related to data engineering that you are interested in.
- Research the topic and write an outline for your blog post.
- Write the blog post.
- Publish the blog post on a platform of your choice.
Build a data pipeline for a real-world problem
Show steps
Apply the skills you have learned in this course to a real-world problem by building a data pipeline.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Identify a real-world problem that you can solve with a data pipeline.
- Design the data pipeline.
- Implement the data pipeline.
- Deploy the data pipeline.
- Monitor the data pipeline.
Attend a data engineering workshop
Show steps
Expand your knowledge and skills in data engineering by attending a workshop.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Research data engineering workshops in your area.
- Register for a workshop that interests you.
- Attend the workshop.
Career center
Learners who complete Data Engineering will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines that process and transform raw data into a usable format for data scientists and other analysts. The Udacity Data Engineering for Data Scientists course provides a solid foundation for this role by teaching you how to build ETL pipelines, natural language pipelines, and machine learning pipelines. You'll also learn how to use Scikit-Learn to code a machine learning pipeline, which will help you ingest data, create features, and train a machine learning algorithm in just one step.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and builds machine learning models. They use data to train models that can make predictions or decisions. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They use their knowledge of programming languages and software engineering principles to create software that meets the needs of users. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Business Analyst
Business Analyst
A Business Analyst helps businesses understand their data and make informed decisions. They use data to identify trends, patterns, and opportunities. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. They work with stakeholders to define project goals, develop project plans, and track project progress. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Technical Writer
Technical Writer
A Technical Writer creates and maintains technical documentation. They work with engineers and other technical professionals to document software, hardware, and other technical products. The Udacity Data Engineering for Data Scientists course can help you build the skills you need to succeed in this role by teaching you how to write clear and concise technical documentation.
Data Architect
Data Architect
A Data Architect designs and builds data architectures. They work with businesses to understand their data needs and develop data architectures that meet those needs. The Udacity Data Engineering for Data Scientists course may help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. They work with database software to ensure that databases are running smoothly and that data is safe and secure. The Udacity Data Engineering for Data Scientists course may help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Systems Analyst
Systems Analyst
A Systems Analyst analyzes and designs business systems. They work with businesses to understand their business needs and develop systems that meet those needs. The Udacity Data Engineering for Data Scientists course may help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
IT Consultant
IT Consultant
An IT Consultant provides consulting services to businesses on how to use technology to meet their business needs. They work with businesses to assess their technology needs, develop technology plans, and implement technology solutions. The Udacity Data Engineering for Data Scientists course may help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Quality Assurance Analyst
Quality Assurance Analyst
A Quality Assurance Analyst tests software to ensure that it meets quality standards. They work with developers to identify and fix bugs. The Udacity Data Engineering for Data Scientists course may help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
Technical Support Specialist
Technical Support Specialist
A Technical Support Specialist provides technical support to users of software and hardware products. They work with users to troubleshoot problems and resolve issues. The Udacity Data Engineering for Data Scientists course may help you build the skills you need to succeed in this role by teaching you how to build data pipelines, process and transform data, and use machine learning to solve business problems.
For more career information including salaries, visit:
OpenCourser.com/course/f497e1/data
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
Data Engineering.
Save
Provides a comprehensive introduction to Apache Spark and its applications in data engineering. It covers all aspects of the Spark ecosystem, from data ingestion to data analysis.
Provides a comprehensive guide to data engineering on AWS. It covers all aspects of the AWS ecosystem, from data ingestion to data analysis.
Save
Provides a comprehensive introduction to R and its applications in data science. It covers all aspects of the R language, from data manipulation to data visualization.
Provides a comprehensive introduction to machine learning and its applications in data science. It covers all aspects of the machine learning process, from data preparation to model evaluation.
Provides a comprehensive introduction to deep learning and its applications in data science. It covers all aspects of the deep learning process, from data preparation to model evaluation.
Provides a comprehensive guide to data integration and data warehousing. It covers all aspects of the data integration process, from data modeling to data cleansing.
Provides a comprehensive guide to data analysis with Pandas. It covers all aspects of the Pandas library, from data loading to data visualization.
Save
Provides a comprehensive introduction to big data and its challenges. It covers topics such as data storage, data processing, and data analysis.
Provides a comprehensive introduction to natural language processing using Python. It covers topics such as text preprocessing, tokenization, stemming, lemmatization, and parsing.
Provides a comprehensive introduction to data science and its applications in business. It covers topics such as data mining, machine learning, and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f497e1/data
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Effort
1 month
Level
Advanced
Via
Udacity
Instructors
Josh Bernhard
Mike Yi
Judit Lantos
David Drummond
Andrew Paster
Juno Lee
Luis Serrano
Language
English
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