{"id":25642001,"url":"https://github.com/adityav42/deloitte-forage-virtual-internship","last_synced_at":"2025-10-29T15:18:53.232Z","repository":{"id":274562743,"uuid":"923321593","full_name":"AdityAV42/Deloitte-Forage-Virtual-internship","owner":"AdityAV42","description":"About Submission for Deloitte's STEM Virtual Program on Forage, focusing on data analysis, forensic technology, and cybersecurity. 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As part of this program, participants assist **Daikibo Industrials**, a global leader in heavy machinery manufacturing, in tackling various operational challenges.\n\nThis repository documents my completion of **three courses** in the Deloitte Virtual Internship on Forage.\n\n## Course 1: Deloitte - Technology\n\n### Task 1: Coding\nDaikibo is integrating **Industrial Internet-of-Things (IIoT)** devices to monitor and analyze their manufacturing processes. However, their infrastructure handles telemetry data in two different formats. The challenge is to merge these data files efficiently.\n\n**Key Learnings:**\n- Manipulating JSON files in Python\n- Writing and executing unit tests using the `unittest` framework\n\n### Task 2: Development\nReal-time monitoring and smart alerts enhance operational efficiency. Daikibo's tech team needs a **real-time manufacturing status dashboard**. The first step in this project involves drafting a **development proposal**.\n\n**Key Learnings:**\n- Writing clear and persuasive software development proposals\n\n## Course 2: Deloitte - Cyber\n\n### Task 1: Cybersecurity\nA major news outlet has leaked sensitive information about Daikibo Industrials, revealing a **production halt** impacting supply chains. The client suspects a **security breach** in their new status dashboard.\n\n**Key Learnings:**\n- Analyzing web log files to detect anomalies\n- Identifying suspicious activities through log analysis\n\n**Findings:**\nThe dashboard is hosted on Daikibo’s intranet, with remote access restricted to **VPN tunneling**. Log analysis revealed that a user with IP `192.168.0.101` initially accessed the dashboard normally but later exhibited **automated hourly access** to status updates across four factories—indicating potential unauthorized activity.\n\n## Course 3: Deloitte - Data Analytics\n\n### Task 1: Data Analysis\nDaikibo's tech team compiled **one month’s telemetry data** (May 2021) from four factories:\n- **Daikibo Factory Meiyo** (Tokyo, Japan)\n- **Daikibo Factory Seiko** (Osaka, Japan)\n- **Daikibo Berlin** (Berlin, Germany)\n- **Daikibo Shenzhen** (Shenzhen, China)\n\nEach site operates **nine types of machines**, reporting status updates every 10 minutes. The goal is to analyze:\n1. **Which location experienced the most machine failures?**\n2. **Which machines had the highest failure rate at that location?**\n\n**Key Learnings:**\n- Utilizing **Tableau** to create interactive dashboards\n- Extracting insights from large datasets\n\n### Task 2: Forensic Technology\nDue to **internal concerns over gender pay inequality**, Daikibo wants to quantify the fairness of compensation across various job roles and locations. The **Forensic Technology team** has developed an algorithm to measure gender pay equality.\n\n**Key Learnings:**\n- Applying **Excel functions** for data analysis and pattern recognition\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadityav42%2Fdeloitte-forage-virtual-internship","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadityav42%2Fdeloitte-forage-virtual-internship","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadityav42%2Fdeloitte-forage-virtual-internship/lists"}