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Curriculum
- 7 Sections
- 52 Lessons
- 45 Days
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- Week 1: Python Programming Foundations (Days 1–7)8
- 1.1Python Introduction & Setup (Python, Jupyter, VS Code)
- 1.2Data Types, Operators, and Type Casting
- 1.3String Handling
- 1.4Conditional Statements (if, if-else, elif)
- 1.5Loops (for, while, break, continue)
- 1.6Functions: def, lambda, recursion
- 1.7Data Structures: List (creation, methods, slicing)
- 1.8✅ Outcomes: Solid understanding of Python syntax, logic building, and core data structures.
- Week 2: Python Advanced Concepts (Days 8–14)8
- 2.1Tuples, Sets, Dictionaries (methods and use cases)
- 2.2Python Modules and Packages
- 2.3OOP in Python (Classes, Objects, Inheritance, Encapsulation, Polymorphism)
- 2.4Exception Handling (try-except, finally, raise)
- 2.5File Handling (open, read, write)
- 2.6Mini Project: Student Management System (CRUD operations with File Handling)
- 2.7Python full revision and Q&A
- 2.8✅ Outcomes: Hands-on coding confidence with reusable modular code and real-world object-oriented structures.
- Week 3: Data Analysis Basics (Days 15–21)8
- 3.1NumPy Arrays (creation, slicing, operations)
- 3.2Pandas (DataFrames, Series, filtering, cleaning)
- 3.3Data Cleaning (NaN handling, renaming, sorting)
- 3.4Matplotlib: Basic charts with labels and legends
- 3.5Seaborn: Bar, Histogram, Box plots
- 3.6Using Jupyter Notebook for Analysis
- 3.7Mini Project 1: EDA on Real Dataset (e.g., Titanic)
- 3.8✅ Outcomes: Data preprocessing, analysis, and visualization skills with Python.
- Week 4: Introduction to Machine Learning (Days 22–28)8
- 4.1ML Basics: Types, Workflow, scikit-learn intro
- 4.2Linear Regression: Model creation & prediction
- 4.3Logistic Regression: Classification & Confusion Matrix
- 4.4K-Nearest Neighbors (KNN): Classification
- 4.5K-Means Clustering: Unsupervised ML
- 4.6Model Evaluation: Overfitting, Cross-validation
- 4.7Mini Project 2: House Price Prediction (Regression)
- 4.8✅ Outcomes: Hands-on understanding of supervised and unsupervised ML with projects.
- Week 5: OpenCV & Computer Vision (Days 29–35)9
- 5.1OpenCV Basics: Read, display, and save images
- 5.2Image Processing: Resize, grayscale, thresholding
- 5.3Drawing and Text on Images
- 5.4Webcam Video Capture & Real-Time Feed
- 5.5Face Detection using Haar Cascades
- 5.6Mini Project 3: Real-Time Face Detection
- 5.7Hand Gesture Recognition
- 5.8Mini Project 4: Gesture-Based Controls
- 5.9✅ Outcomes: Practical computer vision projects using OpenCV and camera/video streams.
- Week 6: Excel, SQL Integration & Final Project (Days 36–45)6
- 6.1MS Excel: Charts, formulas, data import, pivot tables
- 6.2Excel Data Cleaning & Analysis
- 6.3SQL Basics: SELECT, WHERE, ORDER BY, GROUP BY
- 6.4Python-SQL Integration: Run queries from Python
- 6.5Final Project Work (Days 41–42): Use Pandas, SQL, OpenCV, Excel to perform a real-world data analysis or prediction task
- 6.6Wrap-Up (Days 43–45): Final Project Submission, Certification Ceremony, Feedback, Career Tips
- Key Projects Recap\\5
- 7.1Mini Project 1: Exploratory Data Analysis (Pandas + Matplotlib/Seaborn)
- 7.2Mini Project 2: House Price Prediction (Regression Model)
- 7.3Mini Project 3: Real-Time Face Detection (OpenCV + Haar Cascades)
- 7.4Mini Project 4: Hand Gesture Recognition (OpenCV)
- 7.5Final Project: Real-world Data Analysis or ML Model (using Excel + SQL + Python)