The use of cloud platforms in Data Science is to provide space to store data and computational resources to solve problems at scale. A company generates thousands to millions of data points according to the scale of its business on a daily basis. So, to store that data and solve problems using such large datasets, it needs a data infrastructure. That is where cloud platforms help by providing the space to store your data and the computational resources to solve problems on such large datasets. So, if you want to learn about cloud platforms for Data Science, this article is for you. In this article, I’ll take you through a complete roadmap to learn cloud platforms for Data Science with learning resources.
Roadmap to Learn Cloud Platforms for Data Science
Here’s a complete roadmap to learn cloud platforms for Data Science with learning resources.
Step 1: Understand the Basics of Cloud Computing
Acquire foundational knowledge of cloud computing, which includes understanding different service models like IaaS, PaaS, and SaaS, and how they are used in Data Science. Start with the core concepts of cloud computing including virtualization, cloud storage, cloud networking, and the different deployment models (public, private, hybrid).
Focus on how Data Scientists use the cloud, such as scalable computing resources for handling large datasets, and cloud-based machine learning algorithms.
Here are some resources you can follow to understand the basics of cloud computing:
Step 2: Choose a Cloud Provider
Select a cloud provider that aligns with your learning objectives or the specific tools and technologies you’re interested in mastering. Evaluate the leading providers (AWS, Azure, and Google Cloud) based on the strength of their data science tools, community support, documentation, and cost-effectiveness.
You can only choose one course or learn one cloud platform in detail. If you know at least one cloud platform in detail, it won’t take long to another one if the company you are working with uses a different one.
I’ll recommend you choose AWS or Google Cloud Platform as you will find the resources to learn about the cloud computing tools of these platforms easily.
Step 3: Set Up a Free Account
Gain practical access to cloud services without initial investment by utilizing the free tiers offered by providers. Sign up for free accounts which provide access to a range of services that are enough to start small to medium-sized projects.
Use the free credits to experiment with creating virtual machines, uploading data, and running simple Machine Learning models. Here are some examples:
Step 4: Learn Specific Cloud Services for Data Science
Dive into specific cloud services that are essential for storing data, processing big data, and building machine learning models. Learn about options like AWS S3, Azure Blob Storage, and Google BigQuery, which are fundamental for handling large datasets. Explore services like AWS SageMaker, Azure Machine Learning, and Google AI Platform to understand how they simplify the process of training and deploying models.
Below are the resources you can follow:
- AWS S3 Documentation
- Azure Blob Storage Documentation
- BigQuery Documentation
- AWS SageMaker Practical Course by Udemy
- Azure Machine Learning Documentation
- Google AI Platform Documentation
Step 5: Take Specialized Online Courses
Enhance your skills through structured learning paths designed to teach the practical application of data science on cloud platforms. Choose courses that offer hands-on labs and projects. It will help you apply what you learn in a controlled, educational environment before tackling real-world problems.
Many courses are designed to help you prepare for cloud certifications, which can be great for professional development. Below are some courses you can follow:
- Practical Data Science with Amazon SageMaker
- Designing and implementing a data science solution on Azure
- Google Cloud Big Data and Machine Learning Fundamentals
Step 6: Practical Projects and Hands-On Labs
Apply your theoretical knowledge in real-world scenarios to solidify your understanding and solve practical problems. Start with guided projects provided in online courses and then move to independent projects like analyzing public datasets or building predictive models.
Use the cloud services to experiment with different architectures and solutions, learning how to optimize costs and improve performance.
Summary
So, the use of cloud platforms in Data Science is to provide space to store data and computational resources to solve problems at scale. By following these steps and utilizing the provided resources, you can build a strong foundation in using cloud platforms for Data Science.
I hope you liked this article on a complete roadmap to learn cloud platforms for Data Science. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
