[Virtual Presenter] Our team explored how we utilized various AWS services to drive insights and improve collaboration in Deliverable 3.. 

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Team Members : Venkata Mahalakshmi Vishnumolakala Mounika Muddypaka Yaswanth Kollu Abinaya Odeti Ashish Redyy Naini

[Audio] Our team's goal was to use AWS tools and services to analyze a dataset, extract valuable insights, and present those findings in a clear and actionable manner. The dataset we chose focuses on online courses, offering information on enrollments, completion rates, pricing, and ratings across different platforms and categories. Our main objectives were to prepare the data, gain meaningful insights through AWS services, and create informative visualizations for stakeholders.. 

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This project is part of the AWS Academy Data Engineering Course, which is designed to introduce students to the practical application of data engineering in real-world scenarios. Through this project, our team aimed to leverage AWS tools and services to analyze a dataset, gain insights, and present actionable results using modern data processing and visualization methods. The dataset chosen focused on online course platforms, providing key insights into enrollments, completion rates, course pricing, and ratings across various platforms and course categories.
 Our objectives were:
 To process, clean, and transform the data. To gain actionable insights from the data using AWS services. To visualize the data in an insightful way for stakeholders.

[Audio] We analyzed the data using AWS Athena, performing SQL-like queries directly on the data stored in S3, which enabled us to quickly identify trends and generate summaries.. 

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Data Collection & Preparation
 AWS S3: The dataset was stored and organized in Amazon S3 for easy access and management. AWS Glue: Used to clean and transform the data by creating a catalog and setting up ETL (Extract, Transform, Load) jobs.
 2. Data Analysis
 AWS Athena: We used Amazon Athena to perform SQL-like queries directly on the data stored in S3, allowing us to quickly analyze trends and generate summaries.
 3. Data Visualization
 AWS QuickSight: For visualizing the results, we used AWS QuickSight to create interactive dashboards that displayed key metrics such as Completion Rates, Price distribution, and Enrollments across platforms.

[Audio] We applied machine learning models using Amazon SageMaker, which enabled us to predict course popularity based on historical data. This insight allowed us to identify trends and patterns that informed our decision-making processes.. 

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4. Machine Learning Insights
 Amazon SageMaker: Leveraged SageMaker for applying machine learning models that helped us predict course popularity based on historical data.
 5. Final Reporting & Insights
 AWS Lambda: For automated data processing and report generation, we used AWS Lambda to trigger functions based on new data uploads.
 6. Collaboration and Version Control
 AWS CodeCommit: Used for version control, enabling seamless collaboration and project management across team members.

[Audio] Data preprocessing is crucial. We discovered this early on in our project. When we failed to properly clean and transform our data, problems emerged. Missing values and inconsistent formats significantly affected our analysis. However, by dedicating time to data quality, we witnessed positive outcomes. AWS tools are highly scalable. We successfully integrated various AWS tools, including Athena, Glue, QuickSight, and SageMaker. This enabled us to efficiently scale our analysis. With Athena's serverless model, we could effortlessly query large datasets, without concerning ourselves with infrastructure management.. 

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Data Preprocessing is Key:  One of the first lessons we learned was the importance of cleaning and transforming data before analysis. Issues like missing values and inconsistent formats had a significant impact on our analysis, and investing time in data quality paid off.
 AWS Tools are Highly Scalable:  The integration of AWS tools (Athena, Glue, QuickSight, and SageMaker) allowed us to scale our analysis efficiently. Athena’s serverless model made querying large datasets seamless without worrying about managing infrastructure.

[Audio] Data insights drive better decisions by providing actionable information that can inform business strategies. By analyzing course data, we discovered correlations between course pricing and completion rates, revealing opportunities for improvement. This knowledge enables course creators to design more effective offerings, ultimately driving better outcomes.. 

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Data Insights Drive Better Decisions:  By visualizing the course data, we could uncover actionable insights, such as the correlation between course pricing and completion rates, that could help course creators design better offerings.
 Collaboration is Crucial:  Using tools like AWS CodeCommit and Lambda for automation greatly improved our workflow, making it easier to collaborate, maintain code quality, and streamline the reporting process.
 Machine Learning Takes Time: Implementing machine learning models using SageMaker was an insightful experience, but we learned that fine-tuning models and understanding their outputs can take significant time, which is critical for real-world data engineering projects.

[Audio] By integrating data engineering processes with AWS tools, we have gained a deeper understanding of how to transform raw data into valuable insights. This project has demonstrated the potential of AWS services in managing massive datasets and enabling real-time decision-making that can impact business strategies and outcomes. The expertise we have acquired will be highly beneficial in future data-driven initiatives and in tackling data-related challenges efficiently.. 

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In conclusion, this project has provided us with a deeper understanding of how data engineering processes can be integrated with AWS tools to turn raw data into meaningful insights. It also showcased the power of AWS services in handling large datasets and making real-time decisions that can influence business strategies and outcomes. The skills we’ve gained will be invaluable in future data-driven projects and in understanding how to approach data challenges effectively.

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Deliverable 3
Project Group 13


Our team's goal was to use A-W-S tools and services to analyze a dataset, extract valuable insights, and present those findings in a clear and actionable manner. The dataset we chose focuses on online courses, offering information on enrollments, completion rates, pricing, and ratings across different platforms and categories. Our main objectives were to prepare the data, gain meaningful insights through A-W-S services, and create informative visualizations for stakeholders.

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We analyzed the data using A-W-S Athena, performing SQL-like queries directly on the data stored in S3, which enabled us to quickly identify trends and generate summaries.

We applied machine learning models using Amazon SageMaker, which enabled us to predict course popularity based on historical data. This insight allowed us to identify trends and patterns that informed our decision making processes.

Data preprocessing is crucial. We discovered this early on in our project. When we failed to properly clean and transform our data, problems emerged. Missing values and inconsistent formats significantly affected our analysis. However, by dedicating time to data quality, we witnessed positive outcomes. A-W-S tools are highly scalable. We successfully integrated various A-W-S tools, including Athena, Glue, QuickSight, and SageMaker. This enabled us to efficiently scale our analysis. With Athena's serverless model, we could effortlessly query large datasets, without concerning ourselves with infrastructure management.

Data insights drive better decisions by providing actionable information that can inform business strategies. By analyzing course data, we discovered correlations between course pricing and completion rates, revealing opportunities for improvement. This knowledge enables course creators to design more effective offerings, ultimately driving better outcomes.

By integrating data engineering processes with A-W-S tools, we have gained a deeper understanding of how to transform raw data into valuable insights. This project has demonstrated the potential of A-W-S services in managing massive datasets and enabling real time decision making that can impact business strategies and outcomes. The expertise we have acquired will be highly beneficial in future data driven initiatives and in tackling data related challenges efficiently.

Deliverable 3 Project Group 13

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