We may earn an affiliate commission when you visit our partners.
Noah Gift,
Alfredo Deza,
and
Kennedy Behrman
In this practical course, you'll gain essential skills for modern data engineering:
- Build interactive Jupyter notebooks for data analysis and machine learning
- Deploy notebooks on cloud platforms like Google Colab and AWS SageMaker
- Construct scalable Python microservices using FastAPI
- Containerize and deploy machine learning microservices
- Create robust command-line tools in Python and Rust
- Automate testing and publishing of your data engineering projects
Read more
In this practical course, you'll gain essential skills for modern data engineering:
- Build interactive Jupyter notebooks for data analysis and machine learning
- Deploy notebooks on cloud platforms like Google Colab and AWS SageMaker
- Construct scalable Python microservices using FastAPI
- Containerize and deploy machine learning microservices
- Create robust command-line tools in Python and Rust
- Automate testing and publishing of your data engineering projects
Read more
In this practical course, you'll gain essential skills for modern data engineering:
- Build interactive Jupyter notebooks for data analysis and machine learning
- Deploy notebooks on cloud platforms like Google Colab and AWS SageMaker
- Construct scalable Python microservices using FastAPI
- Containerize and deploy machine learning microservices
- Create robust command-line tools in Python and Rust
- Automate testing and publishing of your data engineering projects
Whether you're a data engineer, scientist, or analyst, this course will level up your abilities to build powerful data solutions. Get hands-on experience with cutting-edge tools and techniques you can apply on the job.
Register for this course and see more details by visiting:
OpenCourser.com/course/6uly6t/web
What's inside
Learning objectives
- Jupyter for data engineering workflows
- Cloud notebook deployment
- Fastapi microservices development
- Containerization of ml microservices
- Python command-line tools
- Rust cli app development
- Automated testing and publishing
- Jupyter for data engineering workflows
- Cloud notebook deployment
- Fastapi microservices development
- Containerization of ml microservices
- Python command-line tools
- Rust cli app development
- Automated testing and publishing
Syllabus
Here is the course structure formatted with bullets for each module:
Module 1: Jupyter Notebooks (4 hours)
\- Introduction to web applications and command-line tools for data engineering
Read more
Syllabus
Here is the course structure formatted with bullets for each module:
Module 1: Jupyter Notebooks (4 hours)
\- Introduction to web applications and command-line tools for data engineering
Read more
\- Overview of key concepts
\- Getting started with Jupyter notebooks
\- Code cells and text cells in Jupyter
\- Magics in Jupyter
\- Overview of Jupyter Lab
Module 2: Cloud-Hosted Notebooks (5 hours)
\- Introduction to Google Colab
\- Tour of Colab features
\- Data and documents in Colab
\- Introduction to AWS SageMaker
\- Tour of SageMaker Studio
\- Overview of SageMaker Pipelines
Module 3: Python Microservices (12 hours)
\- Introduction to building Python microservices
\- Benefits of microservices
\- Setting up Python project structure for CI
\- Building a random fruit web app with Python
\- Introduction to Python microservices with FastAPI
\- Building FastAPI microservices for ML predictions
\- Deploying a Python Lambda microservice
\- Introduction to building containerized microservices
\- Why use containers for microservices?
\- Deploying a containerized .NET 6 API
\- Deploying a containerized ML microservice
Module 4: Python Packaging and Rust Command-Line Tools (19 hours)
\- Introduction to Python packaging and command-line tools
\- Getting started with Python projects
\- Overview of command-line tool frameworks
\- Using Click to build a command-line tool
\- Exploring advanced command-line tool features
\- Introduction to packaging and distributing your Python project
\- Working with Python setup tools
\- Uploading to a Python registry
\- Introduction to continuous integration for command-line tools
\- Automating testing and publishing with GitHub Actions
\- Introduction to Rust command-line tools
\- Working with user input, output, modules in Rust
\- Optimizing Rust command-line tools
\- Big O notation final challenge
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides essential skills for modern data engineering
Instructors Noah Gift, Alfredo Deza, and Kennedy Behrman are well-known for their work in the field
Develops skills relevant to data engineers, scientists, and analysts
Taught by experts in the field with extensive experience
Covers cutting-edge tools and techniques used in the industry
Offers hands-on experience with industry-standard tools
Save this course
Save Web Applications and Command-Line Tools for Data Engineering to your list so you can find it easily later:
Save
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 Web Applications and Command-Line Tools for Data Engineering with these
activities:
Practice Data Wrangling Drills
Show steps
Enhance your ability to cleanse and prepare complex datasets
Browse courses on
Data Wrangling
Show steps
-
Solve Code Challenges on HackerRank
-
Work through Pandas tutorials
-
Experiment with Data Manipulation Libraries
Build Python Microservice Projects
Show steps
Reinforce your understanding of microservice development
Show steps
-
Create a Python web service using FastAPI
-
Deploy your microservice to a cloud platform
-
Monitor and troubleshoot your microservice
Explore Advanced Command-Line Tools
Show steps
Extend your knowledge of command-line tools beyond the basics
Browse courses on
Command-Line Tools
Show steps
-
Learn advanced features of Click
-
Build a Rust command-line application
-
Integrate Rust into your data engineering workflow
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a Data Engineering Solution
Show steps
Apply the skills you've learned to solve a real-world data engineering problem
Show steps
-
Identify a problem or opportunity
-
Design and implement a data engineering solution
-
Present your solution and its impact
Document Your Data Engineering Project
Show steps
Reinforce your understanding by documenting your data engineering solution
Show steps
-
Write a technical report summarizing your project
-
Create a presentation to showcase your findings
Tutor Beginner Data Engineers
Show steps
Solidify your understanding by sharing your knowledge and skills with others
Show steps
-
Identify opportunities to mentor beginner data engineers
-
Develop and deliver training sessions or workshops
Compile a Data Engineering Resources Guide
Show steps
Create a valuable resource for yourself and other data engineers
Show steps
-
Gather and organize useful resources
-
Create a documentation or website to share your guide
Practice Data Wrangling Drills
Show steps
Enhance your ability to cleanse and prepare complex datasets
Browse courses on
Data Wrangling
Show steps
Show steps
Show steps
- Solve Code Challenges on HackerRank
- Work through Pandas tutorials
- Experiment with Data Manipulation Libraries
Build Python Microservice Projects
Show steps
Reinforce your understanding of microservice development
Show steps
Show steps
Show steps
- Create a Python web service using FastAPI
- Deploy your microservice to a cloud platform
- Monitor and troubleshoot your microservice
Explore Advanced Command-Line Tools
Show steps
Extend your knowledge of command-line tools beyond the basics
Browse courses on
Command-Line Tools
Show steps
Show steps
Show steps
- Learn advanced features of Click
- Build a Rust command-line application
- Integrate Rust into your data engineering workflow
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a Data Engineering Solution
Show steps
Apply the skills you've learned to solve a real-world data engineering problem
Show steps
Show steps
Show steps
- Identify a problem or opportunity
- Design and implement a data engineering solution
- Present your solution and its impact
Document Your Data Engineering Project
Show steps
Reinforce your understanding by documenting your data engineering solution
Show steps
Show steps
Show steps
- Write a technical report summarizing your project
- Create a presentation to showcase your findings
Tutor Beginner Data Engineers
Show steps
Solidify your understanding by sharing your knowledge and skills with others
Show steps
Show steps
Show steps
- Identify opportunities to mentor beginner data engineers
- Develop and deliver training sessions or workshops
Compile a Data Engineering Resources Guide
Show steps
Create a valuable resource for yourself and other data engineers
Show steps
Show steps
Show steps
- Gather and organize useful resources
- Create a documentation or website to share your guide
Career center
Learners who complete Web Applications and Command-Line Tools for Data Engineering will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/6uly6t/web
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6uly6t/web
Share
Similar courses
Here are nine courses similar to
Web Applications and Command-Line Tools for Data Engineering.
Web Applications and Command-Line Tools for Data Engineering
OpenCourser.com/course/tu7ipy/web
Web Applications and Command-Line Tools for Data...
Most relevant
Python and Rust with Linux Command Line Tools
OpenCourser.com/course/tsuep2/python
Python and Rust with Linux Command Line Tools
Most relevant
Python and Rust with Linux Command Line Tools
OpenCourser.com/course/clfb2q/python
Python and Rust with Linux Command Line Tools
Most relevant
Python 3 Scripting for System Administrators
OpenCourser.com/course/pm18nw/python
Python 3 Scripting for System Administrators
Most relevant
Python 2.7 Scripting for System Administrators
OpenCourser.com/course/rf6mox/python
Python 2.7 Scripting for System Administrators
Most relevant
DevOps, DataOps, MLOps
OpenCourser.com/course/ywzncx/devops
DevOps, DataOps, MLOps
Most relevant
Python in Containers
OpenCourser.com/course/1xq1lz/python
Python in Containers
Most relevant
Linux and Bash for Data Engineering
OpenCourser.com/course/y6ozhr/linux
Linux and Bash for Data Engineering
Most relevant
Developing on AWS
OpenCourser.com/course/8tbb74/developing
Developing on AWS
Most relevant
Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Noah Gift
Alfredo Deza
Kennedy Behrman
Session
March 31, 2024
- March 31, 2025
Language
English
Transcript
English
Good to know
Provides essential skills for modern data engineering
Instructors Noah Gift, Alfredo Deza, and Kennedy Behrman are well-known for their work in the field
Develops skills relevant to data engineers, scientists, and analysts
Taught by experts in the field with extensive experience
Covers cutting-edge tools and techniques used in the industry
Offers hands-on experience with industry-standard tools
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser