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https://github.com/adityav42/deloitte-forage-virtual-internship

About Submission for Deloitte's STEM Virtual Program on Forage, focusing on data analysis, forensic technology, and cybersecurity.
https://github.com/adityav42/deloitte-forage-virtual-internship

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About Submission for Deloitte's STEM Virtual Program on Forage, focusing on data analysis, forensic technology, and cybersecurity.

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# Deloitte-Forage-Virtual-Internship

This repository contains my submissions for the **Deloitte STEM Virtual Program**. As part of this program, participants assist **Daikibo Industrials**, a global leader in heavy machinery manufacturing, in tackling various operational challenges.

This repository documents my completion of **three courses** in the Deloitte Virtual Internship on Forage.

## Course 1: Deloitte - Technology

### Task 1: Coding
Daikibo 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.

**Key Learnings:**
- Manipulating JSON files in Python
- Writing and executing unit tests using the `unittest` framework

### Task 2: Development
Real-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**.

**Key Learnings:**
- Writing clear and persuasive software development proposals

## Course 2: Deloitte - Cyber

### Task 1: Cybersecurity
A 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.

**Key Learnings:**
- Analyzing web log files to detect anomalies
- Identifying suspicious activities through log analysis

**Findings:**
The 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.

## Course 3: Deloitte - Data Analytics

### Task 1: Data Analysis
Daikibo's tech team compiled **one month’s telemetry data** (May 2021) from four factories:
- **Daikibo Factory Meiyo** (Tokyo, Japan)
- **Daikibo Factory Seiko** (Osaka, Japan)
- **Daikibo Berlin** (Berlin, Germany)
- **Daikibo Shenzhen** (Shenzhen, China)

Each site operates **nine types of machines**, reporting status updates every 10 minutes. The goal is to analyze:
1. **Which location experienced the most machine failures?**
2. **Which machines had the highest failure rate at that location?**

**Key Learnings:**
- Utilizing **Tableau** to create interactive dashboards
- Extracting insights from large datasets

### Task 2: Forensic Technology
Due 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.

**Key Learnings:**
- Applying **Excel functions** for data analysis and pattern recognition

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