Connection Between Data Science and Cloud Computing!

Data science is now essential for insightful decision-making in nearly all sectors, but the associated resource costs create a substantial scalability problem. This article explores the crucial link between cloud computing and data science, demonstrating how cloud platforms enable scalable data science applications.

Table of Content

• What is Data Science?
• What is Cloud Computing?
• How is Data Science related to Cloud Computing?

What is Data Science?

Data Science is a field that uses scientific methods and algorithms to extract inferences from huge amounts of data. It includes steps like data cleaning, analyzing and predicting of data. Along with the same, data science also works with Artificial Intelligence and Generative AI. Additionally, written and verbal communication, probability and statistics, and business domain are also required. Data scientists use tools like R, Python, SQL and other big data frameworks for the analysis of large datasets. Data Scientists play an important role in converting raw data to meaningful data

What is the Cloud Computing?

Cloud computing refers to the providing of computing services like storage, servers, databases, networking etc. over the internet (called the cloud). It gives the companies the scalable and reliable environments of managing their resources without having to worry about the maintenance of any physical hardware which makes it an important part of modern technology. Several cloud providers like Microsoft Azure, Amazon Web Services, Google etc. offer various cloud services. Now you would be wondering how they can be related to each other. Let us find out all about it in this blog!

Become a Professional Data Scientist

With Our Industry-Leading Certification in Data Science

Explore Program

How Is Data Science Related to the Cloud?

In case you’re well acquainted with the Data Science process, you would realize that, regularly, the vast majority of the Data Science processes is completed on the local computer of a Data Scientist. Mostly, R and Python would be installed along with the IDE used by the Data Scientist. The other essential development environment setup include related packages which need to be installed either by means of Anaconda, like package manager, or by introducing individual packages, manually.

When the development environment is setup, the Data Science process begins, with data being the main thing required throughout.

The iterative workflow process steps include:

• Building, approving, and testing models, for example, recommendations and predictive models
• Wrangling, parsing, munging, transforming, and cleaning data
• Mining and analyzing data, for example, summary statistics, Exploratory Data Analysis (EDA), etc.
• Gaining data
• Tuning and enhancing models or deliverables

Issues with the local system:

• The processing power (CPU) of the development environment can’t perform tasks in an adequate measure of time. There are instances where it doesn’t run at all.
• To a production environment, the deliverable should be deployed and should be incorporated as a component into a bigger application (for instance, web application and SaaS platform).
• Datasets being too large won’t fit into the development environment’s system memory (RAM) for analytics or for model training.
• It is preferable to utilize a quicker, and all the more capable, machine (CPU and RAM) and not force the essential load on the local development machine.

At the point when these circumstances emerge, there are various choices available. Rather than utilizing the local development machine of the Data Scientist, individuals offload the computing work to either a cloud-based virtual machine (for instance, AWS EC2, AWS Elastic Beanstalk, etc.) or an on-premises machine. The advantage of utilizing virtual machines, and auto-scaling is that it can handle the increase and decrease of the computing resources such as storage and computing power as per the requirement.

Besides the customized cloud-based or production Data Science solutions and devices, there are many cloud and service-based offerings accessible from eminent vendors too, which frequently function admirably with tools like Jupyter Notebook. These are accessible to a great extent as machine learning, big data, and artificial intelligence APIs and incorporate choices like the Databricks, Google Cloud Platform Datalab, AWS Artificial Intelligence stage, and many such options.

Conclusion

In conclusion, the connection between data science and cloud computing is transforming how the data scientists are handling huge data sets and complex models by using more scalable, flexible and cost-effective resources. The tools and services required for data processing and machine learning, cloud computing removes the hardware barrier thus allowing the data scientists to focus on innovation. If you want to know more about data science with cloud computing, check out our amazing Data Science Course.