If you aspire to become a proficient data engineer, the Python, Bash and SQL Essentials for Data Engineering Specialization is an invaluable starting point. In today's data-driven world, businesses and organizations rely on data to make critical decisions. This specialization equips you with the essential skills necessary to thrive in this field.
Through a series of modules, you will delve into Python and Pandas, Linux and Bash, scripting with Python and SQL, and web applications and command-line tools for data engineering. This hands-on approach, led by professional data engineers, ensures that you can immediately apply the acquired techniques in your professional or academic endeavors. Don't miss the opportunity to launch your career in data engineering with Duke University's Python, Bash and SQL Essentials for Data Engineering Specialization.
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Python, Bash and SQL Essentials for Data Engineering comprises modules covering Python and Pandas for data engineering, Linux and Bash for data engineering, scripting with Python and SQL for data engineering, and web applications and command-line tools for data engineering.
Python and Pandas for Data Engineering equips you with the essential skills to set up a provisioned Python project environment and utilize Pandas libraries to manipulate data structures. Additionally, you will learn to write Python code using Vim and Visual Studio Code.
Linux and Bash for Data Engineering focuses on utilizing Linux tools to build data engineering solutions and developing Bash syntax to configure and control Linux environments. You will gain practical knowledge to operate effectively within the Linux environment.
Scripting with Python and SQL for Data Engineering enables you to extract data from various sources, map it to Python data structures, and design scripts to connect and query SQL databases. Additionally, you will apply scraping techniques to extract data from websites, enhancing your data engineering capabilities.
Web Applications and Command-Line Tools for Data Engineering encompasses constructing Python microservices with FastAPI, building command-line tools in Python using Click, and comparing multiple methods to set up and use a Jupyter notebook. These skills are crucial for seamless data engineering processes.
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