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AWS for Data Science: Certifications, Tools, Services

29th Feb, 2024
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    AWS for Data Science: Certifications, Tools, Services

    Today, data is everything, and every technology runs around managing, storing, accessing, and processing this data. After the introduction of cloud computing, the need for managing expanding data is getting more critical. But running complex algorithms on personal machines is getting challenging due to memory and computing constraints.

    People started leveraging the cloud's power to run complex algorithms to solve these challenges. AWS has changed the life of data scientists by making all the data processing, gathering, and retrieving easy. Cloud computing has been in the market for so long and has helped several businesses to scale and perform better. Today, companies are migrating to the cloud or expanding their cloud subscription.

    One popular cloud computing service is AWS (Amazon Web Services). Its increasing popularity and global adoption have raised a need to excel in this skill. Many people are going for Data Science Courses in India to leverage the true power of AWS. As a data scientist, if you are just starting with AWS, this guide will help you understand the process to get there. 

    What is Amazon Web Services (AWS)?

    AWS was launched by Amazon as a cloud computing platform, offering various services like IaaS, PaaS, SaaS, and others. It offers services based on the cloud pay-as-per-go model, where you only have to pay for the used services without incurring additional charges for unused services.

    Amazon Web Service

    In 2006, Amazon launched AWS to handle its online retail operations. AWS comes with three significant products that have helped their business to excel.

    1. EC2 (Amazon Elastic Compute Cloud) 

    It allows the users to take virtual machines/servers and run applications. Amazon will charge you based on your used computing power and the server's capacity. 

    2. Glacier 

    It is an affordable online file storage web service. It is specially designed for businesses with long-term storage requirements of inactive data that is not accessed frequently. 

    3. S3 (Amazon Simple Storage Services) 

    With S3, you can store objects using a web service interface, ensuring scalability and high-speed access to the data.

    But what are the reasons that make AWS popular globally? Below are the benefits of why data scientists across the globe highly adopt AWS.

    • Security: it ensures high-end security to meet different business demands. 
    • Compliance: AWS is well-known for its rich controls, auditing, and security accreditations. 
    • Scalability: these services allow your business to grow and scale, as it provides resources whenever required for the proper functioning of your apps. 
    • Pay-as-per-go: AWS is highly adopted because it allows the business to pay only for the services used. You only have to pay for extended services if your business grows and requires more resources. 

    Data scientists are leveraging the primary benefit of AWS. Today, there is a considerable demand for AWS-certified data scientists in the market. If you are looking for a new job role in the data scientist category, you must go for a best Data Science Bootcamp.

    But do you know what certifications AWS provides for data scientist to upgrade their skills and work with AWS smoothly? If not, we have listed a few certifications you must consider for new opportunities.

    Top AWS Data Science Certifications?

    However, Amazon offers several certifications, but only a few are suitable for Data Scientists. If you are interested in gaining more detailed knowledge about how AWS is helpful for data scientists, you can get certifications in different levels per your choice. The choice depends on your requirement, what you want to learn, and what field you want to choose.

    So, let’s see the AWS certifications for data science that will help data scientists to upskill.  

    1. Foundational

    This certification is for those who want basic knowledge to enter the data scientist field with AWS. This certification suits those with a minimum experience of 6 months with AWS. Then you must go for the below certification. 

    AWS Certified Cloud Practitioner: It is an entry-level certification that checks your knowledge about basic understanding of the AWS cloud. This AWS certification data science includes the following- 

    • Understanding of AWS architecture. 
    • The value proposition of the AWS cloud 
    • AWS services and their use cases 
    • Security and Compliance 
    • Core deployment approach 
    • Cloud costs and billing practices 
    • Exam Format: Multiple choice & Multiple response questions 
    • Exam Duration: 90 minutes 
    • Exam Cost: 100 USD 

    2. Associate

    It is recommended for those with an intermediate level of experience developing web apps. Below is the associate certificate you must consider for upskilling your current role.

    AWS Certified Solutions Architect (SAA-C03): This certification allows you to learn how to design and implement AWS applications. It also explains how you can create hybrid systems using AWS components. It might help many data scientist broaden their skills, but it is not recommended. 

    This certification covers the following things- 

    • Working on network technologies in AWS 
    • Creating secure applications 
    • Deploying hybrid systems. 
    • How to design highly available, scalable, and performant systems, implement and deploy applications in AWS, deploy data security practices, and cost optimization approach.  
    • Exam Format: Multiple-choice, multiple-response 
    • Exam Duration: 130 minutes  
    • Exam Cost: 150 USD

    3. Professional 

    Below is the right AWS professional certificate in data science for it. This certification is challenging, as it includes various processes such as provision, operation, and management of applications on the AWS platform. It is one step further, but you need to be technically strong and understand what DevOps is and how it works. 

    AWS Certified DevOps Engineer: It focuses majorly on continuous delivery (CD) and automation. You will learn to create an automated process to complete tedious manual tasks and simplify the CI/CD process.  

    This data science AWS certification will cover the following knowledge- 

    • Basic knowledge of CD methodologies. 
    • Implementing and managing the CD systems. 
    • Setting up, monitoring, and managing the logging systems on AWS. 
    • Implement scalable and self-healing systems. 
    • Designing and managing tools enabling you to automate production operations.
    • Exam Format: Multiple-choice, multiple answers 
    • Exam Duration: 180 minutes 
    • Exam cost: 300 USD

    These certifications not only allow you to learn basic knowledge but also help you to work with various AWS tools. Due to such demanding skills for a data scientist, AWS data scientist's salary is getting bigger. In India, the demand for AWS for data science is also getting huge and growing daily. Let’s see what AWS tools are available for data scientists.

    AWS Data Science Tools of 2023

    AWS offers a wide range of tools that helps data scientist to streamline their work. Data scientists widely adopt these tools due to their immense benefits. Below are some tools. 

    1. Data Storage

    Data scientists can use Amazon Redshift. It allows you to execute complex queries on structured and unstructured data. Another data-related tool is AWS glue, allowing analysts and data scientists to manage and search for the appropriate data. With AWS Glue, you can create a unified catalog within the data lake for faster access. 

    2. Machine learning

    Today, there is a surge in demand for AWS machine learning data scientists. Amazon offers a fully managed machine learning service named Amazon SageMaker. It allows the data scientist to run it on EC2. Data scientists use this tool to build, train, deploy machine learning models, and scale business operations.

    3. Analytics

    Another essential tool being offered by Amazon for a data scientist is-

    • Amazon Athena is a query service for analyzing the data in Amazon S3 or Glacier. It works using standard SQL queries. 
    • Amazon Elastic MapReduce (EMR) helps efficiently process and analyze big data using servers like Spark and Hadoop. 
    • Amazon Kinesis aggregates and processes the streaming data in real time. 

    You must apply for KnowledgeHut’s Data Science Course in India to learn more about these tools and services. AWS has a lot in its bag and many services to offer to make a data scientist's life easier.

    Top 5 AWS Data Science Services

    Below are essential AWS data science services that any data scientist must use for seamless working around data using data science with AWS.

    Top 5 AWS Data Science Services

    Below is the list of AWS services for data science.

    1. Amazon EMR

    It is an AWS data science platform for easy execution and processing of big data frameworks, such as Apache, Hadoop and Spark. It helps data scientists to process big data. With Amazon EMR, you can quickly transform and migrate big data between AWS databases and data stores.

    A. Storage: Different file systems are present in the storage layer. Thus we need different storage options for different types of data.

    • Hadoop Distributed File System (HDFS) is a scalable file system that stores multiple copies of data across instances in a cluster. It ensures that there is no data loss in case of any failure. You can use it to cache temporary results for managing your workloads. 
    • EMR file system allows direct access to the Amazon S3 data. You can use both S3 and HDFS for your cluster’s file system.  

    B. Data processing frameworks

    These are the engines to process and analyze the data running on YARN. Different frameworks have different capabilities, and you can choose the interfaces and languages your applications use for easy interaction with the data. Some open-source frameworks supported by Amazon EMR are:

    • Hadoop MapReduce is a programming framework for distributed computing where you have to specify the Map and Reduce functions to handle all the logic of your distributed applications. The Map function helps map the data to temporary results, and the Reduce function combines them to generate the final result. 
    • Apache Spark - a cluster framework for processing big data. Being high performance distributed processing system, it can easily handle all the data sets with services like in-memory caching. 

    2. AWS Glue

    AWS Glue is an ETL service that manages the data. It is an affordable service that allows data scientists to classify, clean, and transfer data. It is serverless with a Data Catalog, a scheduler, and an ETL engine for producing Scala or Python code.

    AWS Glue is well-suited for handling semi-structured data, offering dynamic frames you can use in ETL scripts.

    The AWS Glue interface allows you to discover different data sources, transform data, and monitor the entire ETL process. It lets you move data to different targets, set up jobs, and execute them on demand. To access Glue from AWS services, you can use AWS Glue API.  

    3. Amazon Sagemaker

    This fully managed MLOps solution for building and training machine learning (ML) models for seamless deployment to the production environment.

    SageMaker has built-in ML algorithms that seamlessly handle big data in distributed environments.

    4. Amazon Kinesis

    Amazon Kinesis Video Streams is a fully managed service primarily used for managing live video streaming to the AWS Cloud. You can store video data and access video content after it is uploaded to the cloud. Not only this, you can process them in real-time to generate better results. Companies focus on including videos on their websites, newsletters, and blogs to have more impact. 

    5. Amazon Quicksight

    QuickSight dsshboards can be easily accessible from mobile or network devices. It is a fully managed, cloud-based (BI) service combining data from multiple sources and bringing it to a single dashboard. It provides security, built-in redundancy, global availability, and a wide range of management tools for the seamless management of large numbers of users.

    How can Amazon Web Services Help You?

    Below are how AWS can help you and your business. 

    • It is easy to use, start, install, configure, and get along with. AWS’s interactive user interface allows even beginners to use it effortlessly. 
    • There are no capacity limits to AWS. Any business of any size can use it to scale its business. 
    • It improves your business’s performance while ensuring agility. AWS load balancing service helps in reducing the time to process your requests and provide adequate results. 
    • With AWS data science high-end architecture security, all the data is secured from malicious activities.  

    Why Do Companies Emphasize AWS Knowledge for Their Data Scientists?

    Below are the significant reasons companies should promote AWS training to upskill employees, especially data scientists. Due to the following reasons, it is essential to learn AWS for data science. 

    1. Customization

    You can customize the process to meet different and changing business requirements. You can also use and place the AWS tags to track the cost, security, and automation.

    2. Flexibility and scalability

    AWS services are flexible and scalable that can fit your specific needs. Today, no businesses face any type of restrictions regarding physical computing infrastructure, servers, and storage on demand. It made the systems scalable by providing services that can quickly scale up and down resources capacity as per the needs. Not only this, it is up to you what service you want and which service to switch off from your plan anytime.

    3. Security

    It is one of the top priorities for every enterprise. Understanding this concern, AWS offers high-end data privacy and security to its customers without worrying about the size of your business. It comes with extensive security support offering real-time information about any malicious activity and helping to find potential vulnerabilities. It uses various methods to ensure security, such as physical security, fine-grained access, data locality control, and IAM (Identity and Access Management). for controlling who can access the data.

    4. Scheduling

    With a scheduling facility, you can plan for any activity to trigger without even monitoring it. It helps in scheduling jobs that take much manual effort. It saves effort and time, ensuring productivity among team members.

    5. Recovery

    For every business, data is everything, and each task depends on the stored data. But what if that data is lost and cannot be recovered? In that case, the business has to face a massive loss that nobody wants. Thus, AWS facilitates data recovery services, allowing you to take backups or create rollback points, so you can recover the lost data for the seamless working of the business without losing access to your data.


    Nowadays, data is getting bigger and has become the oil driving the entire market. With the high migration to the cloud, each business and application is scaling and expanding globally. It has raised challenges for data scientists to get hold of such massive data. But with AWS services, tools, and certifications, they can better manage the data.

    All you need is a little background on AWS data science training. Then the next step is to opt for the proper AWS data science certification that will help you to upgrade your skills and move in the right direction for better AWS data science jobs. Today, data science on AWS is taking the market to the next level.

    Frequently Asked Questions (FAQs)

    1Does AWS Come Under Data Science?

    No, but there is a close relationship between data science and AWS. Data Scientists work closely with different data types stored in the cloud. 

    2What Does an AWS Data Scientist Do?

    Below is the AWS data scientist job description: 

    • Uses a range of techniques, tools, and technologies for data science.  
    • They pick suitable combinations of AWS services for faster and more accurate results. 
    • They are responsible for engineering, analysis, machine learning, and core data science methodologies.  
    3Is AWS Data Science Certification Worth It?

    The AWS Data Analytics certification is worth it for data scientists, who process, analyze and visualize the data they work with. They maintain the data storage options and solutions, and AWS Data Analytics might not be worth it. 

    4Which AWS Certification Is Required for a Data Scientist?

    Below are the recommended certifications: 

    1. AWS Certified Cloud Practitioner 
    2. AWS Certified Solutions Architect (SAA-C03) 
    3. AWS Certified DevOps Engineer

    Aashiya Mittal


    Aashiya has worked as a freelancer for multiple online platforms and clients across the globe. She has almost 4 years of experience in content creation and is known to deliver quality content. She is versed in SEO and relies heavily on her research capabilities.

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