One of the major shifts within the data science industry over the years has been the move towards products and services that operate on a cloud-based system of storage and computing. Cloud-based systems store information, data, and digital products across offsite servers maintained by a third-party company instead of an individual computer or device. Cloud-based systems allow for greater mobility and flexibility when it comes to working on projects, accessing information and data, and collaborating with others.

Many of the top technology and software companies have introduced or integrated cloud-based computing systems. Amazon Web Services (AWS) has positioned itself as the premier cloud provider for businesses worldwide. Data Scientists who are invested in the latest software in the industry should be knowledgeable of the cloud-based data science tools which are currently being offered through Amazon Web Services.

What is Amazon Web Services?

Although Amazon has become widely recognized as a platform for buying and selling goods, the company has always prioritized innovation and expansion in the development of products and services across industries. As a major multinational conglomerate, Amazon has access to one of the largest stores of consumer insights and user data, which the company has leveraged in the development of several tools and technologies which respond to this collection.

Amazon Web Services (AWS) is one of many holdings that the company has within the software and technology industry. AWS offers services and data-driven solutions for multiple industries, with products geared towards government, financial services, gaming, and even advertising and marketing. This collection of tools has allowed Amazon to make an important space for itself within the future of information and data, as well as contributing to the development and expansion of the data science industry.

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Data Science Tools from Amazon Web Services

Of the many industries for which Amazon Web Services has developed tools and services, the importance of information and data science continues to stand out. Most of the products and services offered through AWS are centered around using information and data to solve problems. Specific data science tools were created for the purpose of developing machine learning models, managing big databases, and ensuring the security of global networks and computer systems.

Data Analysis and Organization Software

One of the many ways that Data Scientists can utilize Amazon Web Services is through the data analytics tools offered by the company. These tools are mostly geared towards businesses that need to offer both predictive and prescriptive analytics to clients (making projections about the future and decisions about the current moment in time). For example, Amazon Quicksight can be used to create visually interesting dashboards and reports for sharing findings from business intelligence and analytics software.

Products like Amazon Athena can be used for the organization of data in the cloud, as this software was created for querying SQL databases through a serverless system. The AWS Data Pipeline also makes use of cloud computing by allowing Data Scientists to seamlessly move data from one AWS product to another. Many of these tools are also excellent for those that are familiar with other data science tools, such as SQL and NoSQL databases, Apache Hadoop, and Apache Kafka.

Machine Learning and Modeling Tools

Another area where AWS has created a significant amount of momentum is in machine learning and artificial intelligence. Many of the products offered by AWS fall under this category. Amazon Deep Learning AMI is compatible with many Python-based software systems and libraries that offer resources for developing deep learning models. This tool offers developers and Data Scientists pre-built models and environments where they can hone their skills in machine learning.

Amazon SageMaker can be utilized by analysts, developers, and Data Scientists to build, train, and deploy machine learning models, while Amazon DevOps is focused on testing those models for glitches. Data scientists and analysts interested in business intelligence products can also rely on Amazon Forecast to use machine learning for time-based predictive analytics. Many of these tools are not only geared towards experts in artificial intelligence but also allow beginners in the field to practice their skills building machine learning models.

Cybersecurity and Cloud-Based Databases

Amazon Web Services prides itself on the security of its databases, offering many of its services to governments and organizations that engage with global flows of confidential information. These database management systems can be differentiated as SQL or NoSQL, with Amazon Aurora being the relational database management system that uses the SQL programming language, while Amazon DynamoDB is used for NoSQL database management through a key-value based system.

Data scientists that need to manage multiple databases can also utilize AWS systems that focus on data warehouses and lakes, as each of these systems can combine multiple databases within the same system through secure cloud-based storage. For example, Amazon Redshift can be used to create data warehouses of databases that are similar in the type of data being collected and stored. In addition, AWS Lake Formation can be used for the creation of big database management systems, like a data lake. Especially for Data Scientists who are interested in data infrastructure, these AWS products offer high-quality security and data mobility.

Interested in learning more about Amazon Web Services?

As one of the top cloud computing platforms, Amazon Web Services offers numerous tools and technologies for the collection and storage of information and data. Noble Desktop offers live online AWS classes useful for students and professionals interested in data infrastructure, cybersecurity, and software development. The Cloud Computing with AWS course introduces data science students and professionals to the role of cloud computing platforms in data storage and organization through hands-on exercises and experience with real-world examples and problem-solving. Noble Desktop’s Cybersecurity Bootcamp teaches multiple strategies for protecting a computer network through developing skills in Python, Linux, and AWS.