GenAI Powered Data Analytics Job Simulation 🚀 Project Overview

This repository contains my work for the GenAI Powered Data Analytics Job Simulation (Forage, July–August 2025). Acting as a Data Analyst for Geldium’s Risk Assessment Team, I used Generative AI prompting (ChatGPT/Codey) to perform structured analytics tasks. The project focused on understanding business problems, framing prompts, and interpreting AI outputs into actionable insights, predictive strategies, and stakeholder-ready reports.

🎯 Certificate Details

Name: Asma Shaik Program: GenAI Powered Data Analytics Job Simulation Completion Date: August 6, 2025 Issued by: Forage CEO Signature: Tom Brunskill, Co-Founder of Forage Verification Codes: Enrollment Code: gNPLN3bCyCZ5CkicC User Code: HETDpnpbvvSewXCQh

📂 Repository Structure ├── Certification/ │ ├── 01_EDA_Summary_Report.docx │ ├── 02_Predictive_Modeling_Plan.txt │ ├── 03_Business_Summary_Report.docx │ ├── 04_AI_Powered_Collections_Strategy.pptx │ └── Certificate_of_Completion.pdf ├── task1/ # Files and materials provided by Forage (EDA task) ├── task2/ # Files and materials provided by Forage (Predictive modeling task) ├── task3/ # Files and materials provided by Forage (Business summary task) ├── task4/ # Files and materials provided by Forage (Collections strategy task) ├── dataset_tcs_project.xlsx # Dataset used for analysis └── README.md

🛠️ Skills & Tools Applied

Generative AI Prompting – translating business problems into structured AI queries Exploratory Data Analysis (EDA) – dataset assessment, missing values, correlations, anomalies Predictive Planning – credit risk modeling approaches, evaluation, and fairness considerations Business Data Storytelling – predictive insights into a decision-maker framework AI Strategy Design – scalable, fair collections framework via Agentic AI

✨ Key Deliverables

EDA Summary Report – assessed data completeness, key risk factors, and anomalies Predictive Modeling Plan – GenAI-assisted design of delinquency forecasting approach Business Summary Report – predictive insights and actionable recommendations for collections AI-Powered Collections Strategy (PPT) – AI-driven system design, responsible guardrails, and expected business impact

📈 Key Takeaways

Gained hands-on experience in AI-assisted analytics workflows Strengthened prompt engineering for structured data and business tasks Learned to balance technical insights with ethical, business-ready reporting

Understood how Generative AI can augment predictive modeling and strategy design

🔗 This project demonstrates how Generative AI can serve as a co-analyst — converting prompts into EDA, predictive models, and business strategies for real-world use cases.