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Ramesh Sannareddy,
Joseph Santarcangelo,
and
Abhishek Gagneja
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data.
You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data.
Read more
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data.
You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data.
Read more
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data.
You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data.
By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming.
Upon completion of this course, you will also have a great new addition to your portfolio!
PRE-REQUISITE:
**Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
Register for this course and see more details by visiting:
OpenCourser.com/course/v92f0p/python
What's inside
Syllabus
Extract, Transform, Load (ETL)
Module 1 introduces you to Extract, Transform, and Load operations basics.
You will learn to extract required information from web pages using web scraping techniques and APIs. You will also access databases using Python and save the processed information as a table in a database.
Read more
Syllabus
Extract, Transform, Load (ETL)
Module 1 introduces you to Extract, Transform, and Load operations basics.
You will learn to extract required information from web pages using web scraping techniques and APIs. You will also access databases using Python and save the processed information as a table in a database.
Read more
Final Project
In this lesson, you will complete two projects, one for practice and one for assessment to apply what you’ve learned. These projects have you implement your skills learned in the previous course and the last module regarding the Extract, Transform, and Load process using web scraping and accessing databases using REST APIs and Python.
[Optional] Python Coding Practices and Packaging Concepts
In this bonus module, you will become familiar with the best practices for coding as documented in the Python Enhancement Proposal (PEP8) style guide. You will learn about static code analysis, ensuring that your code adheres to the coding rules. Next, you will learn how to create and run unit tests. Finally, you will learn how to create, verify, and run Python packages.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces the foundational skills and practices for working with data in Python
Emphasizes hands-on learning through practical projects, providing valuable experience in data engineering techniques
Requires prior proficiency in Python, making it suitable for learners with some foundational knowledge in Python programming
Covers essential topics in data extraction, transformation, and loading (ETL), providing practical skills for working with data in various formats
Provides opportunities to enhance professional skills, such as web scraping and working with APIs
Focuses on applying skills in a practical setting, rather than theoretical knowledge
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Reviews summary
Comprehensive python project course for data engineering
Learners say this course provides a comprehensive and practical Python-based experience for data engineers. The course covers hands-on labs and a project, where learners can apply their skills to an ETL process. Though the course is well-received, some learners have reported issues with instructors, difficult exams, and a lack of engaging assignments.
Students generally find the deadlines to be manageable.
Students generally find the certificate to be valuable.
Assignments are generally well-received, but some students find them to be unclear.
"Many of the assignment instructions are unclear"
"Should update contents as the names of api changes."
"need more exercises, to let me sharpen their new skill."
Students generally find the projects to be engaging.
"Great Project work!"
"i really love the project, i will wished there were more projects"
"It was great experience. Really loved it , learnt a lot in this. "
Instructors are described as knowledgeable, but some students have had negative experiences with them.
"The questions were not clear. Labs poorly formulated"
"Need to explain more, it literally has no explanation at all"
"Frustratingly obtuse directions. Labs and final project instructions littered with errors and contradicting instructions."
Exams are described as difficult by some students.
"The questions were not clear. Labs poorly formulated"
"Frustratingly obtuse directions. Labs and final project instructions littered with errors and contradicting instructions."
"Complete waste of time, got stuck with running codes as I am unable to use watson studio."
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 Python Project for Data Engineering with these
activities:
Review Python basics
Show steps
Establish a stronger foundation by revisiting the basics of Python, ensuring readiness for the course's Python-centric content.
Browse courses on
Python
Show steps
-
Revisit variables, data types, and operators.
-
Practice writing simple functions.
Review Python Data Science Handbook
Show steps
Expand knowledge and enhance understanding of Python data science concepts by reviewing a comprehensive reference book on the subject.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Read selected chapters relevant to data engineering.
-
Take notes and highlight key concepts.
Design a data extraction workflow
Show steps
Demonstrate understanding of data extraction techniques by creating a comprehensive workflow that outlines how data will be acquired, processed, and stored.
Browse courses on
Data Extraction
Show steps
-
Define the data sources and identify the necessary extraction methods.
-
Design a data cleaning and transformation plan.
-
Create a detailed flowchart or document outlining the workflow.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore web scraping with Python
Show steps
Gain practical experience in web scraping, a key skill for data engineers, using guided tutorials to enhance understanding.
Browse courses on
Web Scraping
Show steps
-
Identify a resource with valuable data for scraping.
-
Use Python libraries like BeautifulSoup or Selenium for scraping.
-
Extract and clean the desired data.
Collaborate with peers on a data engineering project
Show steps
Reinforce learning and expand perspectives by collaborating with peers on a practical data engineering project, fostering teamwork and knowledge sharing.
Browse courses on
Data Engineering
Show steps
-
Form a group with classmates.
-
Choose a data engineering project.
-
Divide tasks and work together to complete the project.
Solve Python coding challenges
Show steps
Sharpen Python proficiency and strengthen problem-solving skills by engaging in coding challenges specifically designed for data manipulation tasks.
Browse courses on
Python
Show steps
-
Find online platforms or resources offering Python coding challenges.
-
Attempt to solve the challenges independently.
-
Review solutions and identify areas for improvement.
Contribute to a Python data engineering project
Show steps
Gain valuable real-world experience and make meaningful contributions to the data engineering community by participating in an open-source project.
Browse courses on
Data Engineering
Show steps
-
Identify open-source Python data engineering projects.
-
Select a project to contribute to based on interests and skill level.
-
Follow the project's guidelines and contribute code or documentation.
Volunteer with a data-driven organization
Show steps
Gain practical experience and contribute to a meaningful cause by volunteering with an organization leveraging data for societal impact.
Browse courses on
Data Engineering
Show steps
-
Research organizations working in data-driven domains.
-
Identify volunteer opportunities that align with interests and skills.
-
Apply to volunteer and contribute time and expertise.
Review Python basics
Show steps
Establish a stronger foundation by revisiting the basics of Python, ensuring readiness for the course's Python-centric content.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit variables, data types, and operators.
- Practice writing simple functions.
Review Python Data Science Handbook
Show steps
Expand knowledge and enhance understanding of Python data science concepts by reviewing a comprehensive reference book on the subject.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters relevant to data engineering.
- Take notes and highlight key concepts.
Design a data extraction workflow
Show steps
Demonstrate understanding of data extraction techniques by creating a comprehensive workflow that outlines how data will be acquired, processed, and stored.
Browse courses on
Data Extraction
Show steps
Show steps
Show steps
- Define the data sources and identify the necessary extraction methods.
- Design a data cleaning and transformation plan.
- Create a detailed flowchart or document outlining the workflow.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore web scraping with Python
Show steps
Gain practical experience in web scraping, a key skill for data engineers, using guided tutorials to enhance understanding.
Browse courses on
Web Scraping
Show steps
Show steps
Show steps
- Identify a resource with valuable data for scraping.
- Use Python libraries like BeautifulSoup or Selenium for scraping.
- Extract and clean the desired data.
Collaborate with peers on a data engineering project
Show steps
Reinforce learning and expand perspectives by collaborating with peers on a practical data engineering project, fostering teamwork and knowledge sharing.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Form a group with classmates.
- Choose a data engineering project.
- Divide tasks and work together to complete the project.
Solve Python coding challenges
Show steps
Sharpen Python proficiency and strengthen problem-solving skills by engaging in coding challenges specifically designed for data manipulation tasks.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find online platforms or resources offering Python coding challenges.
- Attempt to solve the challenges independently.
- Review solutions and identify areas for improvement.
Contribute to a Python data engineering project
Show steps
Gain valuable real-world experience and make meaningful contributions to the data engineering community by participating in an open-source project.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Identify open-source Python data engineering projects.
- Select a project to contribute to based on interests and skill level.
- Follow the project's guidelines and contribute code or documentation.
Volunteer with a data-driven organization
Show steps
Gain practical experience and contribute to a meaningful cause by volunteering with an organization leveraging data for societal impact.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Research organizations working in data-driven domains.
- Identify volunteer opportunities that align with interests and skills.
- Apply to volunteer and contribute time and expertise.
Career center
Learners who complete Python Project for Data Engineering will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
As a Data Engineer, you use programming languages like Python to work with data. These languages help you extract data from a variety of sources, transform that data into a standard format, and load that data into a database. This course can help you develop your skills in Python, providing you with hands-on experience in data extraction, transformation, and loading. This course may be useful for learning some of the basics of the field of Data Engineering.
Data Scientist
Data Scientist
Data Scientists analyze data to provide insights for businesses. They use a variety of programming languages, including Python, to work with data. This course can help you learn the basics of Python and data analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software. They may specialize in a variety of areas, including data engineering and data analysis. This course can help you learn Python and the basics of data engineering.
Data Analyst
Data Analyst
Data Analysts study data using programming languages like Python to discover trends and patterns. These analysts help businesses make informed decisions based on data. This course can help you learn the basics of Python and data analysis, providing you with some of the skills needed for this career.
Database Administrator
Database Administrator
Database Administrators manage databases and ensure that data is secure and accessible. They may also help businesses design and implement new databases. This course can help you learn the basics of database management.
Web Developer
Web Developer
Web Developers design and develop websites. They may specialize in a variety of areas, including front-end development and back-end development. This course can help you learn Python and the basics of web development.
Marketing Analyst
Marketing Analyst
Marketing Analysts study data to help businesses understand their customers and market their products and services. They may specialize in a variety of areas, including data analysis and data visualization. This course can help you learn the basics of data analysis.
Business Analyst
Business Analyst
Business Analysts help businesses understand their data and make informed decisions. They may specialize in a variety of areas, including data analysis and data visualization. This course can help you learn the basics of data analysis.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect data from unauthorized access and use. They may specialize in a variety of areas, including data security and network security. This course may be useful for learning some of the basics of data security.
Data Warehouse Architect
Data Warehouse Architect
Data Warehouse Architects design and implement data warehouses. They may specialize in a variety of areas, including data architecture and data modeling. This course may be useful for learning some of the basics of data warehouse architecture.
Product Manager
Product Manager
Product Managers manage the development and launch of products. They may specialize in a variety of areas, including data products and software products. This course may be useful for learning some of the basics of product management.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. This course may be useful for learning some of the basics of technical writing.
Systems Analyst
Systems Analyst
Systems Analysts study business processes and design and implement new systems. They may specialize in a variety of areas, including data systems and software systems. This course may be useful for learning some of the basics of systems analysis.
User Experience Designer
User Experience Designer
User Experience Designers design and develop user interfaces for software and other products. This course may be useful for learning some of the basics of user experience design.
Project Manager
Project Manager
Project Managers plan and execute projects. They may specialize in a variety of areas, including data projects and software projects. This course may be useful for learning some of the basics of project management.
For more career information including salaries, visit:
OpenCourser.com/course/v92f0p/python
Reading list
We've selected 19 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
Python Project for Data Engineering.
Provides a comprehensive introduction to Python libraries for data analysis, such as Pandas and NumPy. It covers data manipulation, cleaning, and analysis techniques, making it a useful resource for data engineers and data scientists.
Provides an introduction to data analysis using Python. It covers the basics of data analysis, including data exploration, data cleaning, and data visualization. It also covers more advanced topics, such as machine learning and deep learning.
This handbook provides a comprehensive overview of Python libraries and techniques for data science, including data exploration, manipulation, visualization, and machine learning. It valuable resource for data engineers and data scientists looking to strengthen their programming skills.
Provides a hands-on introduction to data analysis using Pandas. It covers the basics of data analysis, including data exploration, data cleaning, and data visualization. It also covers more advanced topics, such as machine learning and deep learning.
Provides a comprehensive introduction to data science. It covers the basics of data science, including data exploration, data cleaning, and data visualization. It also covers more advanced topics, such as machine learning and deep learning.
Provides a comprehensive introduction to machine learning using Python. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It also covers more advanced topics, such as deep learning and neural networks.
Save
Offers a practical introduction to data manipulation in Python, covering data cleaning, transformation, and aggregation. It is particularly useful for data engineers and analysts seeking to improve their data manipulation skills.
Provides a comprehensive introduction to deep learning using Python. It covers the basics of deep learning, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It also covers more advanced topics, such as natural language processing and computer vision.
While this book focuses on machine learning, it also covers data preprocessing, which is an essential aspect of ETL processes. It provides valuable insights into data cleaning, feature engineering, and model development, making it useful for data engineers involved in machine learning projects.
Explores Python applications in finance, including data analysis, risk management, and investment automation. While it does not specifically focus on ETL processes, it provides insights into data handling and manipulation techniques that are transferable to ETL tasks.
Save
Provides a comprehensive reference for Python. It covers the basics of Python, including syntax, data types, and control flow. It also covers more advanced topics, such as object-oriented programming, modules, and packages.
Save
Provides a fun and engaging introduction to Python. It covers the basics of Python, including syntax, data types, and control flow. It also covers more advanced topics, such as object-oriented programming, modules, and packages.
Save
Provides a deep dive into the Python language. It covers advanced topics, such as decorators, generators, and metaprogramming. It also provides insights into the design and implementation of the Python language.
Save
Provides a collection of recipes for solving common programming problems in Python. It covers a wide range of topics, including data manipulation, web development, and system administration.
Provides a practical guide to using Python in real-world projects. It covers topics such as web development, data analysis, and machine learning.
Save
Provides a guide to writing effective Python code. It covers topics such as code style, testing, and performance optimization.
Save
Provides an introduction to Python for beginners. It covers the basics of Python, including syntax, data types, and control flow.
Provides a practical guide to using Python to automate boring tasks. It covers topics such as web scraping, data analysis, and system administration.
Provides a fast-paced introduction to Python. It covers the basics of Python, including syntax, data types, and control flow.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/v92f0p/python
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Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Ramesh Sannareddy
Joseph Santarcangelo
Abhishek Gagneja
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
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Türkçe
Polski
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آذربایجان دیلی
Good to know
Introduces the foundational skills and practices for working with data in Python
Emphasizes hands-on learning through practical projects, providing valuable experience in data engineering techniques
Requires prior proficiency in Python, making it suitable for learners with some foundational knowledge in Python programming
Covers essential topics in data extraction, transformation, and loading (ETL), providing practical skills for working with data in various formats
Provides opportunities to enhance professional skills, such as web scraping and working with APIs
Focuses on applying skills in a practical setting, rather than theoretical knowledge
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