At 5-10 hrs/week
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To be successful in this program, you should have intermediate Python and SQL skills.See detailed requirements.
Learn to create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra.Data Modeling with PostgresData Modeling with Apache Cassandra
Sharpen your data warehousing skills and deepen your understanding of data infrastructure. Create cloud-based data warehouses on Amazon Web Services (AWS).Build a Cloud Data Warehouse
Understand the big data ecosystem and how to use Spark to work with massive datasets. Store big data in a data lake and query it with Spark.Build a Data Lake
Schedule, automate, and monitor data pipelines using Apache Airflow. Run data quality checks, track data lineage, and work with data pipelines in production.Data Pipelines with Airflow
Combine what you've learned throughout the program to build your own data engineering portfolio project.Data Engineering Capstone
from industry experts
Personal career coach and
Developer Advocate at DataStax
Amanda is a developer Advocate for DataStax after spending the last 6 years as a Software Engineer on 4 different distributed databases. Her passion is bridging the gap between customers and engineering. She has degrees from University of Washington and Santa Clara University.
Staff Engineer at SpotHero
In his career as an engineer, Ben Goldberg has worked in fields ranging from Computer Vision to Natural Language Processing. At SpotHero, he founded and built out their Data Engineering team, using Airflow as one of the key technologies.
CEO at Novelari & Assistant Professor at Nile University
Sameh is the CEO of Novelari, lecturer at Nile University, and the American University in Cairo (AUC) where he lectured on security, distributed systems, software engineering, blockchain and BigData Engineering.
Data Engineer at Wolt
Olli works as a Data Engineer at Wolt. He has several years of experience on building and managing data pipelines on various data warehousing environments and has been a fan and active user of Apache Airflow since its first incarnations.
VP of Engineering at Insight
David is VP of Engineering at Insight where he enjoys breaking down difficult concepts and helping others learn data engineering. David has a PhD in Physics from UC Riverside.
Data Engineer at Split
Judit was formerly an instructor at Insight Data Science helping software engineers and academic coders transition to DE roles. Currently, she is a Data Engineer at Split where she works on the statistical engine of their full-stack experimentation platform.
Curriculum Lead at Udacity
Juno is the curriculum lead for the School of Data Science. She has been sharing her passion for data and teaching, building several courses at Udacity. As a data scientist, she built recommendation engines, computer vision and NLP models, and tools to analyze user behavior.
Excellent.. Initially thought how come they teach industry standard / real time projects.. But after joining the course, I am with Udemy. Yes!!, teaching not only concepts and technical terms. Teaching the industry best practice to write code, documentation, subject matters and mentor is really helping and validating all the key aspects before completing the project. Really good and enjoying in my learning. Thank you
The first project shows that the team is putting in a lot of effort on creating thorough and creative projects! Good knowledge of databases, python, SQL and even Pandas (which was new to me) is needed. The online notebook is great, useful, and functional! And I could not forget mentioning the project reviews: really well done!!
The course is well structured. It's easier enough for you to progress consistently and hard enough so that you're constantly learning and being challenged. I also appreciate the fact that there's someone to go to to ask questions; this gives you the confidence and peace of mind to be able to keep on trying and moving forward.
The lessons are highly informative. I like the hands-on labs we get to practice on throughout each lesson, since it helps reinforce what is being taught. And by far what really drives all of it home are the projects. I wish it was possible to search in the chat. That would really make everything so much better.
I really enjoyed this programm and learned a lot of practical things: data modeling using SQL or no-SQL approach with Postgres and Cassandra, working with Amazon cloud web services (Redshift database and S3 storage), data analysis using Apache Spark, as well as ETL pipeline construction using Apache Airflow.
This Nanodegree taught me all the essentials tools to begin my path as a Data Engineer. All projects are really challenges and focus on real problems. I would definitely recommend this course, there's no course on the web that you can learn all these frameworks and tools in just one course.
Numbers don't lie. See what difference it makes in career searches.*
Career-seeking and job-ready graduates found a new, better job within six months of graduation.
Average salary increase for graduates who found a new, better job within six months of graduation.
The data engineering field is expected to continue growing rapidly over the next several years, and there’s huge demand for data engineers across industries.
Udacity has collaborated with industry professionals to offer a world-class learning experience so you can advance your data engineering career. You will get hands-on experience running data pipelines, building relational and noSQL data models, creating databases on the cloud, and more. Udacity provides high-quality support as you master in-demand skills that will qualify you for high-value jobs in the data engineering field and help you land a job you love.
By the end of the Nanodegree program, you will have an impressive portfolio of real-world projects and valuable hands-on experience.
This program is designed to prepare people to become data engineers. This includes job titles such as analytics engineer, big data engineer, data platform engineer, and others. Data engineering skills are also helpful for adjacent roles, such as data analysts, data scientists, machine learning engineers, or software engineers.
This Nanodegree program offers an ideal path for experienced programmers to advance their data engineering career. If you enjoy solving important technical challenges and want to learn to work with massive datasets, this is a great way to get hands-on practice with a variety of data engineering principles and techniques.
The prerequisites for this program include proficiency in Python and SQL. You should be comfortable writing functions and loops, using classes, working with libraries in Python. You should be comfortable querying data using joins, aggregations, and subqueries in SQL.
Udacity’s School of Data Science consists of several different Nanodegree programs, each of which offers the opportunity to build data skills, and advance your career. These programs are organized around four main career roles: Business Analyst, Data Analyst, Data Scientist, and Data Engineer.
The School of Data currently offers two clearly-defined career paths. These paths are differentiated by whether they focus on developing programming skills or not. Whether you are just getting started in data, are looking to augment your existing skill set with in-demand data skills, or intend to pursue advanced studies and career roles, Udacity’s School of Data has the right path for you! Visit “How to Choose the Data Science Program That’s Right for You” to learn more.
There is no application. This Nanodegree program accepts everyone, regardless of experience and specific background.
The Data Engineer Nanodegree program is designed for students with intermediate Python and SQL skills.
In order to successfully complete the program, students should be comfortable with the following programing concepts:
The Data Engineer Nanodegree program is comprised of content and curriculum to support six (6) projects. We estimate that students can complete the program in five (5) months working 10 hours per week.
Each project will be reviewed by the Udacity reviewer network. Feedback will be provided and if you do not pass the project, you will be asked to resubmit the project until it passes.
Please see the Udacity Executive Program FAQs for policies on enrollment in our programs.
There are no software and version requirements to complete this Nanodegree program. All coursework and projects can be done via Student Workspaces in the Udacity online classroom.