About This Project

About This Project

Project Description
Maths, Reasoning & English Question Management Automation

I developed an automated question-management system using Google Apps Script, Google Docs, and Google Sheets. The system is designed to manage questions for Maths, Reasoning, and English in separate subject-wise documents and spreadsheet tabs.

Questions added to the respective Google Docs are automatically identified using their question IDs, such as M001/M002 for Maths, R001/R002 for Reasoning, and E001/E002 for English, and synchronized with the appropriate Google Sheet.

The system automatically extracts the question information, adds new questions to the correct subject sheet, maintains question IDs and descriptions, and creates an OPEN QUESTION link back to the original Google Doc.

What I Did

• Created separate Maths, Reasoning, and English question management workflows.
• Connected individual Google Docs with their corresponding Google Sheets.
• Developed Google Apps Script to automate the synchronization.
• Created logic to identify question IDs such as M003, M004, M005, etc.
• Automatically detected newly added questions.
• Automatically added new questions to the correct subject sheet.
• Prevented duplicate questions from being added.
• Extracted the question description from the Google Docs.
• Added OPEN QUESTION links back to the original document.
• Added default question status such as Draft.
• Created subject-wise organization so Maths questions go to Maths, Reasoning to Reasoning, and English to English.
• Updated the script to recognize different question formats such as M005., M005 -, and M005).

Technologies Used

Google Apps Script · Google Docs · Google Sheets · Google Workspace · JavaScript

My Role
Automation Developer

Tools & Technologies
Google Apps Script, Google Sheets, Google Docs

Key Features

• Automatic data synchronization
• Document generation
• Automated updates
• Workflow automation

🎥 Project Video

Video URL
https://www.youtube.com/watch?v=...

About This Project

Deloitte Data Analytics Job Simulation | Tableau | Excel | Data Visualization

Project Overview

• This project was completed as part of the Deloitte Data Analytics Job Simulation. The project involved analyzing real-world-style business datasets provided by Daikibo Industries to identify operational issues and investigate gender pay equality.
• Using Tableau and Excel, I analyzed industrial telemetry data to identify which factory experienced the highest machine downtime and which machine types contributed most to the downtime.
• I also analyzed employee compensation data and classified gender pay equality scores into different levels to help identify potential pay disparities.
• The project demonstrates practical skills in data analysis, data visualization, calculated fields, dashboard creation, business insights, and forensic analysis.
  

📊 Daikibo Industrial Data Analytics

Deloitte Data Analytics Job Simulation

An end-to-end data analytics project analyzing industrial telemetry and employee compensation data. I used Tableau to investigate factory downtime and machine failures, and Excel to classify gender pay equality scores.

🔧 Task 1 — Factory Downtime Analysis

Business Question

Which Daikibo factory experienced the most machine downtime?

Approach

I analyzed telemetry data collected from four Daikibo factories:

• Daikibo Factory Seiko — Osaka
• Daikibo Factory Meiyo — Tokyo
• Daikibo Factory Berlin — Berlin
• Daikibo Factory Shenzhen — Shenzhen

Each machine generated telemetry messages at regular intervals.

To estimate potential downtime, I created a Tableau calculated field called Unhealthy.

Calculated Field

IF [Status] = "unhealthy" THEN 10 ELSE 0 END

Each unhealthy telemetry message represents 10 minutes of potential downtime.

Visualization

Created an interactive Tableau bar chart:

Down Time per Factory

Key Finding

Daikibo Factory Seiko recorded the highest potential downtime at 480 minutes.

🔧 Task 2 — Machine Failure Analysis

After identifying the factory with the highest downtime, I filtered the analysis to Daikibo Factory Seiko.

I then analyzed downtime by machine/device type using the Tableau visualization:

Down Time per Device Type

Key Finding

LaserWelder was responsible for the highest potential downtime, with 480 minutes.

This helped identify the machine type that should receive priority for further investigation and maintenance.

⚖️ Task 3 — Gender Pay Equality Analysis

The second part of the project focused on analyzing Equality Scores across different factories and job roles.

The Equality Score ranges from -100 to +100, where 0 represents ideal equality.

I created an Equality Class to categorize the scores.

Purpose

This classification makes it easier to identify job roles and locations with significant compensation disparities and areas that may require further investigation.

🛠️ Skills & Technologies

Technical Skills

• Tableau Public
• Microsoft Excel
• Data Analysis
• Data Visualization
• Calculated Fields
• Interactive Dashboards
• Data Filtering
• Business Intelligence
• Exploratory Data Analysis
• Forensic Data Analysis

⭐ Portfolio Project Card

Daikibo Industrial Data Analytics

Analyzed industrial telemetry and employee compensation data as part of the Deloitte Data Analytics Job Simulation. Built interactive Tableau dashboards to identify factory downtime and machine-level failures, and used Excel to classify gender pay equality scores.

Tools:
Tableau Public · Excel · Data Analysis · Data Visualization · Dashboard Development

Key Results

480 min — Highest factory downtime
LaserWelder — Highest downtime device
4 factories — Analyzed
Gender pay equality — Classified across roles and locations