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Web Applications and Command-Line Tools for Data Engineering

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
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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.

Enroll now

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

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
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Traffic lights

Read about what's good
what should give you pause
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

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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
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
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
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
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
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
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
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

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:

Reading list

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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
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
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
Share this
Share to help others discover this course.
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