{"id":20430660,"url":"https://github.com/ashishsingh789/quantium_data-analysis-_virtual-internship","last_synced_at":"2026-04-07T23:31:39.462Z","repository":{"id":258524818,"uuid":"860992932","full_name":"AshishSingh789/Quantium_Data-Analysis-_virtual-internship","owner":"AshishSingh789","description":"Completed a job simulation focused on Data Analytics and Commercial Insights for the data science team. 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This project showcases my data analytics and commercial insights skills through various tasks, primarily focusing on customer analytics, transaction datasets, and uplift testing.\n\n\nOverview\n\nThis 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.\n\nKey Highlights:\n\nData Analytics and Customer Insights: Worked on analyzing transaction datasets to extract key insights and provide actionable recommendations.\nBenchmarking and Uplift Testing: Conducted detailed benchmarking of stores for trial store layout testing and performed uplift analysis to assess the impact.\nReporting for Commercial Applications: Delivered data-driven reports for Category Managers to assist in making strategic decisions backed by evidence.\nSkills Demonstrated\nData Preparation \u0026 Cleaning:\nIt was efficiently processed and cleaned transaction datasets to ensure data integrity for analysis.\n\nCustomer Analytics:\nWe have performed detailed customer segmentation and behavioral analysis to derive actionable insights.\n\nUplift Testing:\nIdentified benchmark stores and conducted uplift analysis to evaluate the performance of trial store layouts.\n\nCommercial Reporting:\nCreated comprehensive reports that summarized the insights and provided recommendations to the business stakeholders (e.g., Category Managers).\n\nFiles in This Repository\ndata_preparation.ipynb\nContains code related to data cleaning and preparation for analysis.\n\ncustomer_analytics.ipynb\nCustomer segmentation and analysis to uncover trends in transaction data.\n\nuplift_testing.ipynb\nUplift testing analysis based on benchmark stores.\n\nfinal_report.pdf\nA comprehensive report summarizing insights and recommendations for the Category Manager.\n\nTools and Technologies Used\n\nPython (Pandas, NumPy, Matplotlib, Seaborn): For data analysis and visualization.\n\nJupyter Notebooks: For interactive coding and analysis.\n\nSQL: For querying large datasets and extracting necessary information.\n\nExcel: For data manipulation and presentation.\n\nHow to Use This Repository\n\nClone the Repository:\n\nbash\nCopy code\ngit clone https://github.com/YourUsername/quantium-data-analytics.git\ncd quantium-data-analytics\n\nExplore the Notebooks: Open the Jupyter Notebooks (.ipynb files) to view and run the analysis step by step.\n\nReview the Final Report: The final report provides a comprehensive summary of the findings and commercial recommendations.\n\nConclusion\nThis 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.\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishsingh789%2Fquantium_data-analysis-_virtual-internship","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashishsingh789%2Fquantium_data-analysis-_virtual-internship","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishsingh789%2Fquantium_data-analysis-_virtual-internship/lists"}