Course Duration: 24 weeks (6 months)

Course Level: Intermediate to Advance

Weeks 1-2: Introduction and Advanced Python Concepts

Course Overview and Setup, Review of Python Fundamentals (Data types, Control Flow, Functions), Object-Oriented Programming (OOP) in Python, Advanced Data Structures (Lists, Tuples, Sets, Dictionaries), Exception Handling and Debugging Techniques

Weeks 3-4: Functional and Concurrent Programming

Functional Programming Paradigms (Map, Filter, Reduce, Lambda Functions), Generators and Iterators, Decorators and Context Managers, Multithreading and Multiprocessing, Asynchronous Programming (Asyncio, Futures, and Coroutines)

Weeks 5-6: File Handling and Serialization

Advanced File Handling (Reading, Writing, and Working with Files), Working with CSV, JSON, and XML files, Data Serialization and Deserialization using Pickle and Shelve, Working with Config Files (INI, YAML)

Weeks 7-8: Introduction to GUI Programming

Overview of GUI frameworks (Tkinter, PyQt, Kivy), Building Basic GUI with Tkinter, Event Handling and Widgets (Buttons, Labels, Text, Frames), Layout Management (Grid, Pack, Place), Menus, Dialog Boxes, and Message Boxes

Weeks 9-10: Advanced GUI Development

Advanced Tkinter Widgets (Treeview, Canvas, Text, Toplevel), Custom Widgets and Styling with Themes (ttk), Building a Multi-Window GUI Application
Integrating Tkinter with Other Libraries (Matplotlib, Pillow), Introduction to PyQt and Kivy

Weeks 11-12: Database Programming Fundamentals

Introduction to Databases and SQL, Setting up SQLite Database, CRUD Operations (Create, Read, Update, Delete), Working with SQLAlchemy (ORM), Database Connectivity with MySQL/PostgreSQL

Weeks 13-14: Advanced Database Programming

Database Design and Normalization, Transactions, Joins, and Indexes, Stored Procedures and Triggers, Working with NoSQL Databases (MongoDB, Redis), Database Performance Optimization Techniques

Weeks 15-16: Integrating Databases with GUI

Connecting Python GUI with SQLite, Building a Database-driven Tkinter Application, Implementing Search, Filter, and Pagination in GUI, Data Visualization in GUI with Matplotlib, Handling Database Errors and Connection Management

Weeks 17-18: Web Scraping and Data Handling

Introduction to Web Scraping (BeautifulSoup, Scrapy, Selenium), Data Extraction from HTML, XML, and JSON, Automating Data Collection and Processing
Handling Dynamic Content and Captchas, Data Cleaning and Preprocessing with Pandas

Weeks 19-20: Advanced Networking and APIs

Socket Programming in Python, Working with RESTful APIs (Requests, JSON), Building a Simple Web Service using Flask/Django, OAuth and API Authentication Techniques, Integrating Web Services with GUI Applications

Weeks 21-22: Testing, Debugging, and Best Practices

Writing Unit Tests with unittest and pytest, Debugging Techniques and Tools (PDB, Logging), Code Quality and PEP 8 Standards, Version Control with Git/GitHub, Continuous Integration/Continuous Deployment (CI/CD)

Weeks 23-24: Capstone Project

Project Planning and Requirement Analysis, Designing and Developing an End-to-End Application, Implementing GUI, Database, and API Integration, Code Review and Optimization, Final Project Presentation and Code Submission

This course will provide a deep dive into advanced Python programming, with a strong focus on GUI development and database integration, preparing students to build robust and interactive applications.

Course Duration: 24 weeks (6 months)

Course Level: Intermediate to Advance

Week 1-2: Introduction to Python for Data Science

Python basics and setup (Anaconda, Jupyter Notebooks), Data types, variables, and operators, Control flow (loops, conditionals), Functions and modules,  Introduction to libraries: NumPy, Pandas

Week 3-4: Data Manipulation with Pandas

DataFrames and Series, Data cleaning and preprocessing, Handling missing data, Merging, joining, and concatenating data, Grouping and aggregation

Week 5-6: Data Visualization

Introduction to Matplotlib and Seaborn, Creating plots: line, bar, scatter, histograms, Customizing plots (labels, colors, themes), Advanced visualizations (heatmaps, pair plots), Plotly for interactive visualizations

Week 7-8: Exploratory Data Analysis (EDA)

Understanding data distributions, Detecting outliers and anomalies, Correlation analysis, Feature engineering and selection, Dimensionality reduction (PCA, t-SNE)

Week 9-10: Probability and Statistics for Data Science

Descriptive statistics (mean, median, mode, variance), Probability distributions (normal, binomial), Hypothesis testing (t-test, chi-square), Confidence intervals, Bayesian inference

Week 11-12: Introduction to Machine Learning

Supervised vs. unsupervised learning, Train-test split and cross-validation, Evaluation metrics (accuracy, precision, recall, F1-score), Introduction to Scikit-Learn, Linear regression and logistic regression

Week 13-14: Supervised Learning Algorithms

Decision trees and random forests, Support vector machines (SVM), k-Nearest Neighbors (k-NN), Gradient boosting and ensemble methods, Model tuning and hyperparameter optimization

Week 15-16: Unsupervised Learning Algorithms

Clustering (k-means, hierarchical clustering), Principal Component Analysis (PCA), Anomaly detection, Association rule mining, Dimensionality reduction techniques

Week 17-18: Natural Language Processing (NLP)

Text preprocessing (tokenization, stemming, lemmatization), Bag of Words and TF-IDF, Sentiment analysis, Word embeddings (Word2Vec, GloVe), Text classification using machine learning

Week 19-20: Introduction to Deep Learning

Basics of neural networks, Introduction to TensorFlow and Keras, Building a simple neural network, Activation functions and loss functions, Training and evaluating a neural network

Week 21: Introduction to Computer Vision

Basics of image processing (OpenCV), Image filtering, transformations, and edge detection, Understanding image data and formats, Histogram equalization and contour detection, Feature extraction using HOG, SIFT, and ORB

Week 22: Deep Learning for Computer Vision

Convolutional Neural Networks (CNNs) architecture, Image classification with CNNs, Data augmentation and transfer learning, Object detection (YOLO, Faster R-CNN), Image segmentation (U-Net, Mask R-CNN)

Week 23: Advanced AI and Computer Vision Applications

Video analysis and action recognition, Face detection and recognition, Generative Adversarial Networks (GANs) for image generation, Applications in medical imaging and autonomous vehicles, Ethics in computer vision (bias, surveillance)

Week 24: Capstone Project and Presentation

Selecting a real-world dataset (vision-related option included), Defining the problem statement, Data exploration and preprocessing, Model building, evaluation, and tuning, Final project presentation and feedback