Course Summary
Learn Python for Data Engineering and build scalable data pipelines. Gain hands-on experience with real-world datasets and tools such as Apache Spark, PySpark, and AWS S3.Key Learning Points
- Build scalable data pipelines using Python and Apache Spark
- Gain hands-on experience with real-world datasets
- Learn to work with PySpark and AWS S3
Related Topics for further study
Learning Outcomes
- Build scalable data pipelines using Python and Apache Spark
- Work with real-world datasets
- Learn to use PySpark and AWS S3 for data engineering
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python
- Familiarity with SQL and data manipulation
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
Similar Courses
- Python for Data Science
- Data Engineering, Big Data, and Machine Learning on GCP
- Data Engineering Foundations
Related Education Paths
Notable People in This Field
- Wes McKinney
- Hilary Mason
Related Books
Description
This mini-course is intended to apply foundational Python skills by implementing different techniques to collect and work with data. Assume the role of a Data Engineer and extract data from multiple file formats, transform it into specific datatypes, and then load it into a single source for analysis. Continue with the course and test your knowledge by implementing webscraping and extracting data with APIs all with the help of multiple hands-on labs. After completing this course you will have acquired the confidence to begin collecting large datasets from multiple sources and transform them into one primary source, or begin web scraping to gain valuable business insights all with the use of Python.
Knowledge
- Demonstrate your Skills in Python - the language of choice for Data Engineering
- Implement Webscraping, and use APIs to extract data in Python
- Play the role of a Data Engineer working on a real project to extract, transform and load data using Jupyter notebook and Watson Studio
Outline
- Python Project for Data Engineering
- Extract, Transform, Load (ETL)
- Course Introduction
- Project Overview
- Completing your project using Watson Studio
- Jupyter Notebook to complete your final project
- Hands-on Lab: Perform ETL
- Next Steps
- Practice Quiz
- Webscraping
- Extracting Data using API
Summary of User Reviews
Learn how to apply Python for data engineering with this Coursera course. Users have praised the course for its practicality and hands-on projects, resulting in a high overall rating.Key Aspect Users Liked About This Course
Many users appreciated the practicality of the course and the opportunity to apply what they learned through hands-on projects.Pros from User Reviews
- Hands-on projects provide a practical learning experience
- Clear explanations and well-structured course material
- Great for beginners looking to learn Python for data engineering
- Good pace and level of difficulty
- Instructor is knowledgeable and responsive to questions
Cons from User Reviews
- Some users felt that the course could benefit from more advanced topics
- Not enough emphasis on real-world applications
- Some users felt that the course could be more challenging
- Limited interaction with other students
- Lack of personalized feedback on projects
Ramesh Sannareddy
4.6 Raiting
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