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Python & Data Science Masterclass

From Fundamentals to Advanced AI, Backend Development & Databases

DURATION: SIX MONTHS

COMPREHENSIVE CURRICULUM | REAL-WORLD PROJECTS | 1:1 MENTORSHIP

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Course Overview

Python Programming

  • Core Python Language Fundamentals
  • Object-Oriented Programming (OOP)
  • Algorithms & Data Structures
  • File Handling & Exception Handling
  • Advanced Topics (Decorators, Generators)
  • Database Connectivity & Web Scraping

Data Science & AI

  • Statistics & Probability
  • Data Cleaning & Preprocessing
  • Machine Learning & Deep Learning
  • ANN, CNN, RNN, GAN, Autoencoders
  • Generative AI & LLM Fine-tuning
  • RAG Implementation & Agent AI

Detailed Curriculum

PART 1: INTRODUCTION TO PYTHON

1. Introduction to Programming and Python

  • What is Programming?
  • Python's History and Popularity
  • Setting up Python Environment

2. Basic Concepts and Syntax

  • Variables, Data Types, and Operators
  • Control Flow (if-else, loops)
  • Input/Output Operations

PART 2: DATA TYPES AND DATA STRUCTURES

1. Data Types and Expressions

  • Numeric Types (int, float, complex)
  • Strings and String Manipulation
  • Boolean Values and Logical Operators

2. Lists and Tuples

  • Lists: Ordered, mutable collections
  • List Comprehensions
  • Tuples: Ordered, immutable collections

3. Dictionaries and Sets

  • Dictionaries: Key-value pairs
  • Dictionary Methods
  • Sets: Unordered unique elements

PART 3: FUNCTIONS AND MODULES

1. Functions and Scope

  • Defining Functions
  • Function Parameters
  • Lambda Functions
  • Scope (Local, Global, Nonlocal)

2. Modules and Packages

  • Creating and Importing Modules
  • Standard Library Modules
  • Packages and __init__.py

PART 1: INTRODUCTION TO DATA SCIENCE

Introduction to Data Science

  • Understand the role of Data Science
  • Explore the impact of Data Science globally
  • Learn about critical skills needed for Data Scientists

PART 2: CORE SUBJECTS IN DATA SCIENCE

1. Statistics and Probability

  • Descriptive Statistics (Mean, Median, Variance)
  • Probability Distributions (Normal, Binomial)
  • Inferential Statistics (Hypothesis Testing)

2. Data Cleaning and Preprocessing

  • Handle Missing Data (Imputation, Removal)
  • Standardize and Normalize Features
  • Deal with Outliers (Detection, Treatment)

3. Programming and Coding

  • Master Python for Data Analysis
  • Use Libraries (Pandas, NumPy, Matplotlib)
  • Write Efficient Code for Data Manipulation

PART 3: EXPLORATORY DATA ANALYSIS (EDA)

1. Data Visualization

  • Create Plots (Scatter, Histograms, Box Plots)
  • Understand Relationships between Variables
  • Identify Patterns and Trends

2. Descriptive Statistics

  • Summary Statistics (Mean, Median, Mode)
  • Correlation Analysis
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  • Distribution Analysis

PART 4: MACHINE LEARNING

1. Supervised Learning

  • Regression (Linear, Polynomial)
  • Classification (Logistic Regression, Decision Trees)
  • Support Vector Machines (SVM), K-Nearest Neighbors (KNN)

2. Unsupervised Learning

  • Clustering (K-means, Hierarchical)
  • Dimensionality Reduction (PCA)

BACKEND DEVELOPMENT WITH FASTAPI

FastAPI Fundamentals

  • Introduction to FastAPI and its advantages
  • Setting up FastAPI environment
  • Creating your first API endpoint
  • Path parameters and query parameters

Advanced FastAPI Features

  • Request body and Pydantic models
  • Response models and status codes
  • Dependency injection system
  • Middleware and CORS

Authentication & Deployment

  • JWT authentication
  • OAuth2 with Password (and hashing)
  • Deploying FastAPI applications
  • Dockerizing FastAPI apps

DATABASES

PostgreSQL

  • Relational database concepts
  • PostgreSQL installation and setup
  • SQL queries (SELECT, INSERT, UPDATE, DELETE)
  • Joins, indexes, and transactions

MongoDB

  • NoSQL database concepts
  • MongoDB installation and setup
  • CRUD operations
  • Aggregation framework

Database Integration

  • Connecting Python to PostgreSQL
  • Connecting Python to MongoDB
  • ORM (SQLAlchemy for PostgreSQL)
  • ODM (MongoEngine for MongoDB)

Deep Learning & Generative AI

Artificial Neural Networks (ANN)

  • Inspired by the human brain
  • Backpropagation algorithm
  • Activation functions (ReLU, Sigmoid, Tanh)
  • Building and training neural networks

Convolutional Neural Networks (CNN)

  • Specialized for image processing
  • Convolutional and pooling layers
  • Object detection (YOLO, Faster R-CNN)
  • Transfer learning with pre-trained models

Recurrent Neural Networks (RNN)

  • Designed for sequential data
  • LSTM and GRU architectures
  • Time series forecasting
  • Natural language processing

Generative Adversarial Networks (GAN)

  • Generator and discriminator networks
  • Training GANs effectively
  • Applications in image generation
  • Style transfer and image-to-image translation

Autoencoders

  • Encoder-decoder architecture
  • Dimensionality reduction
  • Anomaly detection
  • Denoising autoencoders

Large Language Models (LLM)

  • Transformer architecture
  • Fine-tuning pre-trained models
  • Prompt engineering techniques
  • Evaluation metrics for LLMs

RAG Implementation

  • Retrieval-Augmented Generation
  • Combining LLMs with knowledge bases
  • Vector databases for retrieval
  • Implementing RAG pipelines

Agent AI & Chatbots

  • Building conversational agents
  • Dialog management systems
  • Integration with messaging platforms
  • Evaluation of chatbot performance

Backend Development & Databases

FastAPI Backend Development

  • Modern, fast web framework for APIs
  • Automatic interactive API documentation
  • Type hints and data validation
  • Async support for high performance
  • Authentication and authorization
  • Testing and deployment strategies

Databases

PostgreSQL

  • Advanced relational database features
  • Stored procedures and functions
  • Performance optimization
  • Integration with FastAPI

MongoDB

  • Document-oriented database
  • Schema design best practices
  • Sharding and replication
  • Integration with FastAPI

Key Libraries & Frameworks Covered

Pandas

Data manipulation and analysis with DataFrames

NumPy

Numerical computing with arrays and matrices

Matplotlib

Basic plotting and visualization

Seaborn

Advanced statistical data visualization

Scikit-learn

Machine learning algorithms and tools

TensorFlow

Deep learning framework by Google

PyTorch

Deep learning framework by Facebook

OpenCV

Computer vision and image processing

FastAPI

Modern, fast web framework for APIs

SQLAlchemy

Python SQL toolkit and ORM

MongoEngine

ODM for MongoDB in Python

Transformers

State-of-the-art NLP models

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