KPMG Job Simulation - Introduction to Technology

Welcome to my repository for the KPMG Job Simulation powered by Forage. This simulation consists of five distinct tasks, each designed to provide hands-on experience in various technology and business disciplines. I completed all five tasks and received a certificate, which you can view on my LinkedIn profile.

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Overview

While all tasks were informative, I chose to highlight the two tasks that I found most relevant and enjoyable based on my interests and career focus. These tasks are:

1. Cloud Engineering Task
2. Data Analytics Task

Below, you'll find detailed information about these tasks and their associated files. This README will help you navigate the repository and understand the work involved in each task.

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Folder Structure

1. Cloud Engineering Task

This folder contains:

• Task Information: Includes files with background information and task details for the Cloud Engineering project.
  • Company background.pdf
  • KPMG - Job Simulation - Task4.pdf
• Pink Bird’s Solution: Contains the proposed architecture and supporting files for the Cloud Engineering task.
  • PinkBird's Web Application Proposal.pdf (Proposal document)
  • pinkbird_web_application_architecture.py (Python script for generating architecture diagram)
  • pinkbird_web_application_architecture.png (Generated architecture diagram)

2. Data Analytics Task

This folder contains:

• Task Information: Contains documents outlining the task requirements and company background.
  • Company background.pdf
  • KPMG - Job Simulation - Task2.pdf
• Dataset: Includes the raw sales data used for analysis.
  • Company Sales Dataset.csv
• Executive Summary: Contains the final executive summary of the data analysis.
  • Wondermarket_Executive_Summary.pdf
• Wondermarket Dataset Analysis: Contains the analysis results, including scripts and visualizations.
  • Results Images (Folder with analysis visualizations)
  • Top_5_Products_by_Profit.csv (File with top products data)
  • Wondermarket_Task_KPMG.py (Python script for analysis)

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Summary of What I Learned

• Cloud Engineering: Developed expertise in creating scalable and efficient cloud architectures tailored to business needs.
• Data Analytics: Improved proficiency in analyzing sales data, identifying key performance metrics, and generating actionable insights to drive business strategies.

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What I Enjoyed the Most

• Data Analytics Task: This task was particularly enjoyable as it allowed me to dive deep into data analysis, visualize key metrics, and provide strategic recommendations based on how a real-world data would look like. The challenge of uncovering insights and presenting them in a clear, actionable format was both rewarding and engaging.

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Conclusion

This repository showcases my work from the KPMG Job Simulation, highlighting my skills in cloud engineering and data analytics. I hope you find the information useful and insightful.

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Usage

To explore the projects, navigate to the respective folders to access the detailed files and analyses.

• For the Cloud Engineering Task, review the proposed architecture and solution documents.
• For the Data Analytics Task, check out the dataset, analysis scripts, and final executive summary.

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Contribution

Feel free to reach out if you have any questions or need further information about the projects. Contributions and feedback are welcome. Please fork this repository, make your changes, and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

Please follow these steps to contribute to the project:

1. Fork the repository and clone it to your local machine.
2. Create a new branch
3. Make your changes and test them thoroughly.
4. Commit your changes: git commit -am 'Add some feature'.
5. Push to the branch: git push origin feature/my-new-feature.
6. Submit a pull request.
7. I will review your changes and work with you to integrate them into the project.

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License

This project is licensed under the MIT License.