AWS Certified Data Analytics Specialty (2023) Hands-on

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Beginner

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Overview

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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you’ll master AWS services essential for passing the AWS Certified Data Analytics Specialty exam. Starting with data collection, you'll use tools like Amazon Kinesis and SQS to manage real-time data streams. Through hands-on labs, you'll build scalable data pipelines and apply data ingestion strategies, gaining practical experience with AWS services that are directly relevant in professional environments. Next, you’ll dive into data storage and processing with Amazon S3, DynamoDB, and Redshift. Using case studies, you'll implement storage strategies, optimize performance, and ensure security. You’ll simulate real-world scenarios to efficiently manage and query data, preparing you for complex projects. With this knowledge, you’ll be equipped to design scalable, secure data architectures on AWS. Lastly, you’ll analyze and visualize data with Amazon QuickSight, OpenSearch, and Athena. By course completion, you’ll be ready for the AWS exam and gain hands-on skills to apply in real-world situations. This course is perfect for data engineers, analysts, and IT professionals seeking to enhance their AWS data analytics expertise. A basic understanding of AWS services is recommended.

Syllabus

Course 1: AWS Data Collection and Storage
Course 2: AWS Data Processing and Analysis
Course 3: AWS Visualization, Security, and Exam Preparation

Courses

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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course is designed to guide you through the essential AWS services for data collection and storage. You'll begin with in-depth exploration of data streaming using Amazon Kinesis. Learn to handle data producers and consumers, set up Kinesis Data Firehose, and integrate CloudWatch filters, all while gaining hands-on experience with real-time data ingestion. Practical exercises will help you understand key scaling and security features, ensuring that your data streams are managed efficiently. Next, you will focus on AWS storage solutions with a strong emphasis on Amazon S3. Explore storage classes, replication, versioning, and lifecycle policies, all critical to effective data management. Through hands-on labs, you’ll configure S3 buckets, implement encryption, and set up event notifications, providing you with the skills needed to manage vast amounts of data securely. DynamoDB services will be introduced for scalable data storage solutions, with hands-on practice in setting up databases and optimizing throughput for performance. By the end of this course, you’ll be able to create and maintain complex data pipelines, ensuring both scalability and security. You'll have the confidence to manage data at scale, implement efficient storage solutions, and optimize performance using AWS best practices, positioning yourself to handle any data challenges AWS environments may present. This course is ideal for data engineers, IT professionals, and AWS users who want to specialize in data collection and storage. Basic knowledge of AWS services is recommended, but no prior certification is required.
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course takes you through the complete process of data handling, starting with AWS data processing services. You’ll begin with AWS Lambda, learning how to integrate serverless functions and manage scalable data pipelines. With practical exercises, you’ll explore how AWS Glue helps automate data preparation and manage complex ETL jobs, making data lake partitioning and modification of Glue Data Catalog easy to understand. Hands-on experience with Glue Studio and DataBrew will further enhance your knowledge in preparing data for analysis. The course also delves into processing large datasets using Amazon EMR, where you’ll work with Apache Spark, Hive, and other tools in the Hadoop ecosystem. You’ll learn to optimize data processing with EMR, partition and store data efficiently, and integrate it with AWS services like Kinesis and Redshift. Exercises in Apache Spark will show you how to analyze data streams and deliver actionable insights in real time. Lastly, you'll focus on the analysis aspect using services like Kinesis Analytics, OpenSearch, and Athena. The course will guide you through setting up advanced analytics using Kinesis, creating real-time monitoring applications, and visualizing data using OpenSearch and QuickSight. By the end of this course, you’ll be well-equipped to build, process, and analyze data pipelines at scale using AWS’s powerful tools. This course is ideal for data engineers, IT professionals, and data analysts aiming to leverage AWS for data processing and analysis. Some familiarity with AWS services is recommended.
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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course is designed to help you master three essential areas of AWS: data visualization, security, and exam preparation. You'll begin with a deep dive into Amazon QuickSight, where you'll learn how to create powerful visualizations and dashboards. The hands-on exercises guide you through setting up QuickSight on top of a Redshift data warehouse, ensuring you gain real-world experience with AWS visualization tools. You’ll also explore other popular visualization options, including HighCharts and D3, helping you understand how to choose the right tools for your data. The next section focuses on AWS security. You’ll explore encryption techniques with KMS and CloudHSM, diving into how AWS manages and protects your data. Hands-on labs will help you implement security best practices, such as key rotation and identity federation. The course also covers critical services like AWS CloudTrail and VPC endpoints, ensuring your infrastructure remains secure and compliant. A detailed exploration of AWS Services Security provides a comprehensive understanding of how to protect your environment. Finally, the course culminates in exam preparation. You’ll be guided through expert tips, including how to save on exam costs and manage your time effectively during the exam. A walkthrough of the AWS certification signup process, coupled with practice exercises and state-of-learning checkpoints, will ensure that you are fully prepared for success. This course is ideal for IT professionals, cloud engineers, data analysts, and security specialists looking to enhance their AWS skills, particularly in visualization, security, and certification prep. A basic understanding of AWS services, cloud computing concepts, and general security principles is recommended to maximize the learning experience.