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.
Ce cours fait partie de la Spécialisation Python, Bash and SQL Essentials for Data Engineering
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À propos de ce cours
4 906 consultations récentes
Dates limites flexibles
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Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
Niveau intermédiaire
Beginner level experience in Python and Linux.
Approx. 15 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
Construct Python Microservices with FastAPI
Build a Command-Line Tool in Python using Click
Compare multiple ways to set up and use a Jupyter notebook
Compétences que vous acquerrez
- Web Application
- Python Programming
- Jupyter notebooks
- Cloud-Hosted Notebooks
- Command-Line Interface
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
Niveau intermédiaire
Beginner level experience in Python and Linux.
Approx. 15 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université Duke
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
Programme de cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
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.
4 heures pour terminer
8 vidéos (Total 27 min), 2 lectures, 1 quiz
5 heures pour terminer
Cloud-Hosted Notebooks
This week, you will learn how to create and use a Cloud-based notebook in Google Colab and AWS Sagemaker.
5 heures pour terminer
6 vidéos (Total 19 min), 1 lecture, 7 quiz
3 heures pour terminer
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.
3 heures pour terminer
11 vidéos (Total 78 min)
3 heures pour terminer
Python Packaging and 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.
3 heures pour terminer
18 vidéos (Total 65 min), 1 lecture, 1 quiz
À propos du Spécialisation Python, Bash and SQL Essentials for Data Engineering
If you are interested in developing the skills needed to be a data engineer, the Python, Bash and SQL Essentials for Data Engineering Specialization is a great place to start. We live in a world that is driven by big data - from what we search online to the route we take to our favorite restaurant, and everything in between. Businesses and organizations use this data to make decisions that impact the ways in which we navigate our lives. How do engineers collect this data? How can this data be organized so that it can be appropriately analyzed? A data engineer is specialized in this initial step of accessing, cleaning and managing big data.
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