https://github.com/ashishsingh789/quantium_data-analysis-_virtual-internship

Completed a job simulation focused on Data Analytics and Commercial Insights for the data science team. Developed expertise in data preparation and customer analytics, utilizing transaction datasets to extract valuable insights and deliver data-driven commercial recommendations
https://github.com/ashishsingh789/quantium_data-analysis-_virtual-internship

data datawrangling matplotlib pandas pandas-dataframe presentation programming python python-library

Last synced: 27 days ago
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Completed a job simulation focused on Data Analytics and Commercial Insights for the data science team. Developed expertise in data preparation and customer analytics, utilizing transaction datasets to extract valuable insights and deliver data-driven commercial recommendations

• Host: GitHub
• URL: https://github.com/ashishsingh789/quantium_data-analysis-_virtual-internship
• Owner: AshishSingh789
• Created: 2024-09-21T17:55:56.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-13T08:58:20.000Z (about 1 year ago)
• Last Synced: 2024-10-19T12:04:28.770Z (about 1 year ago)
• Topics: data, datawrangling, matplotlib, pandas, pandas-dataframe, presentation, programming, python, python-library
• Language: Jupyter Notebook
• Homepage:
• Size: 15.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **Quantium Data Analytics Job Simulation - September 2024**

Welcome to my project repository for the Quantium Data Analytics Job Simulation completed in September 2024. This project showcases my data analytics and commercial insights skills through various tasks, primarily focusing on customer analytics, transaction datasets, and uplift testing.

Overview

This repository contains the work completed during the Quantium Data Analytics Virtual Job Simulation, which is designed for aspiring data analysts and data scientists to develop practical experience in data analytics.

Key Highlights:

Data Analytics and Customer Insights: Worked on analyzing transaction datasets to extract key insights and provide actionable recommendations.

Benchmarking and Uplift Testing: Conducted detailed benchmarking of stores for trial store layout testing and performed uplift analysis to assess the impact.

Reporting for Commercial Applications: Delivered data-driven reports for Category Managers to assist in making strategic decisions backed by evidence.

Skills Demonstrated

Data Preparation & Cleaning:

It was efficiently processed and cleaned transaction datasets to ensure data integrity for analysis.

Customer Analytics:

We have performed detailed customer segmentation and behavioral analysis to derive actionable insights.

Uplift Testing:

Identified benchmark stores and conducted uplift analysis to evaluate the performance of trial store layouts.

Commercial Reporting:

Created comprehensive reports that summarized the insights and provided recommendations to the business stakeholders (e.g., Category Managers).

Files in This Repository

data_preparation.ipynb

Contains code related to data cleaning and preparation for analysis.

customer_analytics.ipynb

Customer segmentation and analysis to uncover trends in transaction data.

uplift_testing.ipynb

Uplift testing analysis based on benchmark stores.

final_report.pdf

A comprehensive report summarizing insights and recommendations for the Category Manager.

Tools and Technologies Used

Python (Pandas, NumPy, Matplotlib, Seaborn): For data analysis and visualization.

Jupyter Notebooks: For interactive coding and analysis.

SQL: For querying large datasets and extracting necessary information.

Excel: For data manipulation and presentation.

How to Use This Repository

Clone the Repository:

bash

Copy code

git clone https://github.com/YourUsername/quantium-data-analytics.git

cd quantium-data-analytics

Explore the Notebooks: Open the Jupyter Notebooks (.ipynb files) to view and run the analysis step by step.

Review the Final Report: The final report provides a comprehensive summary of the findings and commercial recommendations.

Conclusion

This project highlights my ability to work with real-world datasets and deliver data-driven insights to help businesses make informed decisions. I gained significant expertise in customer analytics, uplift testing, and the generation of meaningful reports for commercial applications.