AOLP

• Python Basics & Data Types
• Pandas & NumPy for Data Manipulation
• Data Cleaning & Preprocessing

• Descriptive & Inferential Statistics
• Data Visualization (Matplotlib, Seaborn)
• Correlation Analysis & Hypothesis Testing

• Supervised & Unsupervised Learning
• Regression, Classification, Clustering
• Model Evaluation Metrics

• Feature Engineering & Selection
• Ensemble Techniques (Random Forest, XGBoost)
• Predictive Modeling & Business Applications

• Business Analytics Project Implementation
• Predictive Modeling Case Studies
• Data Storytelling & Visualization

• Resume & LinkedIn Positioning
• Mock Interviews & Case Studies
• Portfolio Showcase

Duration

5–6 Months

Live Sessions

2–3 Sessions Per Week

Mode

Online (Live + Recording Access)

Assignments

Weekly Practical Implementation Tasks

Projects

3+ Portfolio-Ready Projects

By the end of this program, you will:

✔ Build 3–5 portfolio-ready projects
✔ Master Python & ML techniques
✔ Apply predictive modeling to real datasets
✔ Confidently appear for Data Science roles
✔ Accelerate career into Analytics / Data Science

✔ Resume Review & Optimization
✔ LinkedIn Profile Enhancement Guidance
✔ Interview Question Bank
✔ Private Community Access
✔ 30-Day Post-Training Support