| Day 01 | 📘 Day 1: Python for Business Analytics - First Steps |
| Day 02 | 📘 Day 2: Storing and Analyzing Business Data |
| Day 03 | 📘 Day 3: Operators - The Tools for Business Calculation and Logic |
| Day 04 | 📘 Day 4: Working with Text Data - Strings |
| Day 05 | 📘 Day 5: Managing Collections of Business Data with Lists |
| Day 06 | 📘 Day 6: Tuples - Storing Immutable Business Data |
| Day 07 | 📘 Day 7: Sets - Managing Unique Business Data |
| Day 08 | 📘 Day 8: Dictionaries - Structuring Complex Business Data |
| Day 09 | 📘 Day 9: Conditionals - Implementing Business Logic |
| Day 10 | 📘 Day 10: Loops - Automating Repetitive Business Tasks |
| Day 11 | 📘 Day 11: Functions - Creating Reusable Business Tools |
| Day 12 | 📘 Day 12: List Comprehension - Elegant Data Manipulation |
| Day 13 | 📘 Day 13: Higher-Order Functions & Lambda |
| Day 14 | 📘 Day 14: Modules - Organizing Your Business Logic |
| Day 15 | 📘 Day 15: Exception Handling - Building Robust Business Logic |
| Day 16 | 📘 Day 16: File Handling for Business Analytics |
| Day 17 | 📘 Day 17: Regular Expressions for Text Pattern Matching |
| Day 18 | 📘 Day 18: Classes and Objects - Modeling Business Concepts |
| Day 19 | 📘 Day 19: Working with Dates and Times |
| Day 20 | 📘 Day 20: Python Package Manager (pip) & Third-Party Libraries |
| Day 21 | 📘 Day 21: Virtual Environments - Professional Project Management |
| Day 22 | 📘 Day 22: NumPy - The Foundation of Numerical Computing |
| Day 23 | 📘 Day 23: Pandas - Your Data Analysis Superpower |
| Day 24 | 📘 Day 24: Advanced Pandas - Working with Real Data |
| Day 25 | 📘 Day 25: Data Cleaning - The Most Important Skill in Analytics |
| Day 26 | 📘 Day 26: Practical Statistics for Business Analysis |
| Day 27 | 📘 Day 27: Data Visualization - Communicating Insights |
| Day 28 | 📘 Day 28: Advanced Visualization & Customization |
| Day 29 | 📘 Day 29: Interactive Visualization with Plotly |
| Day 30 | 📘 Day 30: Web Scraping - Extracting Data from the Web |
| Day 31 | 📘 Day 31: Working with Databases in Python |
| Day 32 | 📘 Day 32: Connecting to Other Databases (MySQL & MongoDB) |
| Day 33 | 📘 Day 33: Accessing Web APIs with requests |
| Day 34 | 📘 Day 34: Building a Simple API with Flask |
| Day 35 | 📘 Day 35: Flask Web Framework |
| Day 36 | 📘 Day 36 – Capstone Case Study |
| Day 37 | 📘 Day 37: Conclusion & Your Journey Forward |
| Day 38 | 📘 Day 38: Math Foundations - Linear Algebra |
| Day 39 | 📘 Day 39: Math Foundations - Calculus |
| Day 40 | 📘 Day 40: Introduction to Machine Learning & Core Concepts |
| Day 41 | 📘 Day 41 · Supervised Learning – Regression |
| Day 42 | 📘 Day 42 · Supervised Learning – Classification (Part 1) |
| Day 43 | 📘 Day 43 · Supervised Learning – Classification (Part 2) |
| Day 44 | 📘 Day 44: Unsupervised Learning |
| Day 45 | 📘 Day 45: Feature Engineering & Model Evaluation |
| Day 46 | 📘 Day 46: Introduction to Neural Networks & Frameworks |
| Day 47 | 📘 Day 47: Convolutional Neural Networks (CNNs) for Computer Vision |
| Day 48 | 📘 Day 48: Recurrent Neural Networks (RNNs) for Sequence Data |
| Day 49 | 📘 Day 49: Natural Language Processing (NLP) |
| Day 50 | 📘 Day 50: MLOps - Model Deployment |
| Day 51 | 📘 Day 51 – Regularised Models |
| Day 52 | 📘 Day 52 – Ensemble Methods |
| Day 53 | 📘 Day 53 – Model Tuning and Feature Selection |
| Day 54 | 📘 Day 54 – Probabilistic Modeling |
| Day 55 | 📘 Day 55 – Advanced Unsupervised Learning |
| Day 56 | 📘 Day 56 – Time Series and Forecasting |
| Day 57 | 📘 Day 57 – Recommender Systems |
| Day 58 | 📘 Day 58 – Transformers and Attention |
| Day 59 | 📘 Day 59 – Generative Models |
| Day 60 | 📘 Day 60 – Graph and Geometric Learning |
| Day 61 | 📘 Day 61 – Reinforcement and Offline Learning |
| Day 62 | 📘 Day 62 – Model Interpretability and Fairness |
| Day 63 | 📘 Day 63 – Causal Inference and Uplift Modeling |
| Day 64 | 📘 Day 64 – Modern NLP Pipelines |
| Day 65 | 📘 Day 65 – MLOps Pipelines and CI/CD Automation |
| Day 66 | 📘 Day 66 – Model Deployment and Serving Patterns |
| Day 67 | 📘 Day 67 – Model Monitoring and Reliability Engineering |
| Day 68 | 📘 Day 68 – BI Analyst Foundations |
| Day 69 | 📘 Day 69 – BI Strategy and Stakeholders |
| Day 70 | 📘 Day 70 – BI Metrics and Data Literacy |
| Day 71 | 📘 Day 71 – BI Data Landscape Fundamentals |
| Day 72 | 📘 Day 72 – BI Data Formats and Ingestion |
| Day 73 | 📘 Day 73 – BI SQL and Databases |
| Day 74 | 📘 Day 74 – BI Data Preparation and Tools |
| Day 75 | 📘 Day 75 – BI Visualization and Dashboard Principles |
| Day 76 | 📘 Day 76 – BI Platforms and Automation Tools |
| Day 77 | 📘 Day 77 – BI Domain Analytics and Value Drivers |
| Day 78 | 📘 Day 78 – BI Experimentation and Predictive Insights |
| Day 79 | 📘 Day 79 – BI Storytelling and Stakeholder Influence |
| Day 80 | 📘 Day 80 – BI Data Quality and Governance |
| Day 81 | 📘 Day 81 – BI Architecture and Data Modeling |
| Day 82 | 📘 Day 82 – BI ETL and Pipeline Automation |
| Day 83 | 📘 Day 83 – BI Cloud and Modern Data Stack |
| Day 84 | 📘 Day 84 – BI Career Development and Capstone |
| Day 85 | 📘 Day 85 – Advanced SQL and Performance Tuning |
| Day 86 | 📘 Day 86 – BI in the Cloud |
| Day 87 | 📘 Day 87 – Data Governance and Security |
| Day 88 | 📘 Day 88 – Capstone Project - Part 1 |
| Day 89 | 📘 Day 89 – Capstone Project - Part 2 |
| Day 90 | 📘 Day 90 – Career Workshop and Next Steps |
| Day 91 | 📘 Day 91: Relational Databases |
| Day 92 | 📘 Day 92: Data Definition Language (DDL) |
| Day 93 | 📘 Day 93: Data Manipulation Language (DML) |
| Day 94 | 📘 Day 94: Data Query Language (DQL) |
| Day 95 | 📘 Day 95: Joins |
| Day 96 | 📘 Day 96: Subqueries |
| Day 97 | 📘 Day 97: Views |
| Day 98 | 📘 Day 98: Indexes |
| Day 99 | 📘 Day 99: Transactions |
| Day 100 | 📘 Day 100: Stored Procedures |
| Day 101 | 📘 Day 101: Triggers |
| Day 102 | 📘 Day 102: Common Table Expressions (CTEs) |
| Day 103 | 📘 Day 103: Pivoting Data |
| Day 104 | 📘 Day 104: Database Design and Normalization |
| Day 105 | 📘 Day 105: JSON in SQL |
| Day 106 | 📘 Day 106: XML in SQL |
| Day 107 | 📘 Day 107: SQL Security |
| Day 108 | 📘 Day 108: SQL Performance Tuning |