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Noah Gift,
Kennedy Behrman,
and
Alfredo Deza
In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.
Register for this course and see more details by visiting:
OpenCourser.com/course/tu7ipy/web
What's inside
Syllabus
Jupyter Notebooks
This week, you will learn how to install and run Jupyter on your local machine. Additionally, you will explore strategies to use code and text cells in a Jupyter notebook.
Read more
Syllabus
Jupyter Notebooks
This week, you will learn how to install and run Jupyter on your local machine. Additionally, you will explore strategies to use code and text cells in a Jupyter notebook.
Read more
Cloud-Hosted Notebooks
This week, you will learn how to create and use a Cloud-based notebook in Google Colab and AWS Sagemaker.
Python Microservices
This week, you will learn how to build a Python Microservice with FastAPI and deploy a containerized machine learning Microservice for data engineering.
Python Packaging and Rust Command Line Tools
This week, you will learn how to organize a Python project so you can build a powerful command-line tool. You will use Click, a useful command-line tool framework to enhance your tool. Finally, you will automate testing and quality control for publishing and sharing your tool with a registry.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Course focuses on skills that are highly relevant in a data engineering setting
Introduces cloud-hosted notebooks, which are commonly used in industry
Provides opportunities for hands-on practice through lab exercises
Save this course
Save Web Applications and Command-Line Tools for Data Engineering to your list so you can find it easily later:
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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:
Read 'Python for Data Analysis'
Show steps
Expand your knowledge of Python for data analysis in preparation for the course.
View
Python for Data Analysis
on Amazon
Show steps
-
Read chapters relevant to data engineering concepts
-
Take notes and summarize key concepts
Practice Python basics
Show steps
Refresh your memory on basic Python skills to prepare for the course.
Browse courses on
Python Basics
Show steps
-
Review variables, data types, and operators
-
Practice writing simple functions
-
Solve simple coding problems
Learn about Jupyter Notebooks
Show steps
Familiarize yourself with Jupyter Notebooks, the primary tool you'll use in the course.
Browse courses on
Jupyter Notebooks
Show steps
-
Follow a tutorial on setting up and using Jupyter Notebooks
-
Create a sample Jupyter Notebook and experiment with its features
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a Python study group
Show steps
Connect with fellow learners and discuss course concepts to enhance your understanding.
Browse courses on
Python
Show steps
-
Find or create a study group with other learners in the course
-
Meet regularly to discuss course topics, share ideas, and work on projects together
Solve coding challenges
Show steps
Practice your coding skills by solving challenges related to data engineering.
Browse courses on
Python Coding
Show steps
-
Choose a coding challenge platform (e.g., LeetCode, HackerRank)
-
Select challenges related to Python, data analysis, and data engineering
-
Solve the challenges and review your solutions
Create a Python Microservice
Show steps
Gain practical experience by building a Python Microservice for data engineering.
Show steps
-
Design the microservice's functionality and architecture
-
Implement the microservice using Python and a relevant framework
-
Test and deploy the microservice
Contribute to an open-source data engineering project
Show steps
Gain real-world experience by contributing to a data engineering project on GitHub.
Browse courses on
Open Source
Show steps
-
Find an open-source data engineering project on GitHub
-
Identify a feature or bug to work on
-
Fork the project, make changes, and submit a pull request
Develop a data engineering tool
Show steps
Demonstrate your skills by creating a useful tool for data engineering tasks.
Browse courses on
Python
Show steps
-
Identify a need or problem in data engineering
-
Design and implement a tool using Python
-
Test and document the tool
Read 'Python for Data Analysis'
Show steps
Expand your knowledge of Python for data analysis in preparation for the course.
View
Python for Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to data engineering concepts
- Take notes and summarize key concepts
Practice Python basics
Show steps
Refresh your memory on basic Python skills to prepare for the course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review variables, data types, and operators
- Practice writing simple functions
- Solve simple coding problems
Learn about Jupyter Notebooks
Show steps
Familiarize yourself with Jupyter Notebooks, the primary tool you'll use in the course.
Browse courses on
Jupyter Notebooks
Show steps
Show steps
Show steps
- Follow a tutorial on setting up and using Jupyter Notebooks
- Create a sample Jupyter Notebook and experiment with its features
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a Python study group
Show steps
Connect with fellow learners and discuss course concepts to enhance your understanding.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find or create a study group with other learners in the course
- Meet regularly to discuss course topics, share ideas, and work on projects together
Solve coding challenges
Show steps
Practice your coding skills by solving challenges related to data engineering.
Browse courses on
Python Coding
Show steps
Show steps
Show steps
- Choose a coding challenge platform (e.g., LeetCode, HackerRank)
- Select challenges related to Python, data analysis, and data engineering
- Solve the challenges and review your solutions
Create a Python Microservice
Show steps
Gain practical experience by building a Python Microservice for data engineering.
Show steps
Show steps
Show steps
- Design the microservice's functionality and architecture
- Implement the microservice using Python and a relevant framework
- Test and deploy the microservice
Contribute to an open-source data engineering project
Show steps
Gain real-world experience by contributing to a data engineering project on GitHub.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source data engineering project on GitHub
- Identify a feature or bug to work on
- Fork the project, make changes, and submit a pull request
Develop a data engineering tool
Show steps
Demonstrate your skills by creating a useful tool for data engineering tasks.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify a need or problem in data engineering
- Design and implement a tool using Python
- Test and document the tool
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:
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. Many Data Engineers have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about Python, Bash and SQL techniques in tackling real-world problems.
Machine Learning Operations Engineer
Machine Learning Operations Engineer
Machine Learning Operations Engineers deploy, monitor, and maintain machine learning models. Many Machine Learning Operations Engineers have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about deploying models for machine learning tasks using Python.
Data Scientist
Data Scientist
Data Scientists collect, analyze, interpret, and present data. Many Data Scientists have a background in computer science, mathematics, or statistics, with at least a bachelor's degree. This course may be useful for you because it will teach you how to use Python, Bash and SQL techniques in tackling real-world problems. You will also learn how to use Python microservices to break up your data warehouse into small, portable solutions that can scale.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, test, and deploy machine learning models. A background in computer science is required to become a Machine Learning Engineer, and many have a master's or PhD degree. This course may be useful for you because it will teach you how to apply Python, Bash and SQL techniques in tackling real-world problems, including Jupyter Notebooks and Cloud-Hosted Notebooks. This foundation can help prepare you to build machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software. A background in computer science is required. Many have a bachelor's degree in computer science or a related field. This course may be useful for you because you will learn about deploying models for machine learning tasks using Python.
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data. Many Data Analysts have a bachelor's in a related field, such as computer science, mathematics, or statistics. This course may be useful for you because you will learn more about managing databases with SQL.
Database Administrator
Database Administrator
Database Administrators maintain, configure, and support databases, which are used to store and manage data. A background in computer science or a related field is required for this role and some Database Administrators may also have a master's degree. This course may be useful for you because you will learn more about managing databases with SQL.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. They ensure that software is built, tested, and deployed efficiently. Many DevOps Engineers have a background in computer science or a related field and may also have a master's degree. This course may be useful for you if you are interested in learning more about Python Microservices and Python Packaging.
Systems Engineer
Systems Engineer
Systems Engineers design, build, and maintain computer systems. A background in computer science or a related field is required. Many have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about Python Packaging and Rust Command Line Tools.
Cloud Engineer
Cloud Engineer
Cloud Engineers design, build, and maintain cloud computing systems. A background in computer science or a related field is required. Many have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about Cloud-Hosted Notebooks.
Product Manager
Product Manager
Product Managers work with stakeholders to define and document product requirements. A background in business or a related field is required for this role. This course may be useful if you are interested in learning more about using Python, Bash and SQL techniques in tackling real-world problems.
Business Analyst
Business Analyst
Business Analysts work with stakeholders to define and document business requirements. A background in business or a related field is required for this role. This course may be useful if you are interested in learning more about using Python, Bash and SQL techniques in tackling real-world problems.
Project Manager
Project Manager
Project Managers plan and execute projects. A background in business or a related field is required for this role. This course may be useful if you are interested in learning more about using Python, Bash and SQL techniques in tackling real-world problems.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. A background in writing or a related field is required for this role. This course may be useful if you are interested in learning more about creating and deploying models for machine learning tasks using Python.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test software to ensure that it meets requirements and is free of defects. A background in computer science or a related field is required for this role, and many also have a master's degree. This course may be useful if you are interested in learning more about automating testing and quality control.
For more career information including salaries, visit:
OpenCourser.com/course/tu7ipy/web
Reading list
We've selected 11 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
Web Applications and Command-Line Tools for Data Engineering.
Covers statistical learning concepts. It classic textbook in the field and is suitable for those who want to learn the fundamentals of statistical learning.
Covers deep learning concepts using Python. It provides a comprehensive overview of the field and is suitable for those with some prior knowledge of machine learning.
Covers machine learning concepts using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It is suitable for those who want to learn how to use these libraries for data analysis and machine learning.
Covers machine learning concepts using Python. It comprehensive guide to the field and is suitable for those who want to learn the fundamentals of machine learning.
Covers deep learning concepts using R. It comprehensive guide to the field and is suitable for those who want to learn the fundamentals of deep learning using R.
Covers Python libraries for machine learning, such as NumPy, Pandas, and Matplotlib. It is useful for those who want to learn how to use Python for data manipulation and analysis.
Covers machine learning concepts using R. It comprehensive guide to the field and is suitable for those who want to learn the fundamentals of machine learning using R.
Covers Python libraries for data science, such as NumPy, Pandas, and Matplotlib. It is useful for those who want to learn how to use Python for data manipulation and analysis.
Covers Python libraries for data analysis, such as NumPy, Pandas, and Matplotlib. It is useful for those who want to learn how to use Python for data manipulation and analysis.
Covers data science concepts from scratch. It is suitable for beginners and provides a solid foundation in data science.
Covers machine learning concepts. It is written for non-technical readers and is suitable for those who want to learn the basics of machine learning without getting too technical.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tu7ipy/web
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Effort
4 weeks of study, 3-6 hours / week
Level
Intermediate
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Kennedy Behrman
Alfredo Deza
Language
English
Transcripts
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This course belongs to a
collection
called
Python, Bash and SQL Essentials for Data Engineering. It's comprised of four courses:
- Linux and Bash for Data Engineering
- Web Applications and Command-Line Tools for Data Engineering (viewing)
- Scripting with Python and SQL for Data Engineering
- Python and Pandas for Data Engineering
Good to know
Course focuses on skills that are highly relevant in a data engineering setting
Introduces cloud-hosted notebooks, which are commonly used in industry
Provides opportunities for hands-on practice through lab exercises
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