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Industry-Driven Retail Analytics: Real Indian Data Science Case Studies

Discover how Indian retail brands use data science for recommendations, fraud detection, and demand forecasting.

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Industry-Driven Retail Analytics: Real Indian Data Science Case Studies

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  1. Industry-Driven Retail Analytics: Real Indian Data Science Case Studies

  2. Why Retail & E-commerce Depend on Data Science • Massive customer and transaction data • Personalization at scale • Demand forecasting & inventory optimization • Fraud detection and trust building • Pricing intelligence & revenue growth

  3. Case Study 1: Recommendation Systems (Flipkart Example) • Collaborative & content-based filtering • User behavior tracking • Real-time personalization engines • Increased conversion rates • Skills: ML models, feature engineering

  4. Case Study 2: Fraud Detection & Fake Review Analytics • Behavioral anomaly detection • Trust scoring systems • Pattern recognition algorithms • Reduced fraudulent transactions • Skills: anomaly detection, graph analytics

  5. Case Study 3: Demand Forecasting & Supply Chain Optimization • Time-series forecasting models • Automated inventory planning • Reduced stock-outs & wastage • Improved supply chain efficiency • Skills: predictive modeling, automation

  6. Case Study 4: Pricing & Campaign Optimization • Price elasticity analysis • A/B testing for promotions • Customer segmentation models • Higher campaign ROI • Skills: experimentation & business analytics

  7. What Learners Must Master • Python, SQL & ML frameworks • Data preprocessing & feature engineering • Model deployment basics • Business storytelling with data • End-to-end retail analytics projects

  8. Career Takeaways for Data Science Aspirants • Retail & e-commerce offer high-demand roles • Practical projects increase employability • Industry-aligned learning is critical • A strong portfolio improves job prospects • Choose a data science course in Bangalore that offers real Indian case studies for practical learning.

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