Python, Data Science & Full Stack Web Development Training

Key Topics & Practical Exercises:

Variables, primitive data types, lists, tuples, sets, dictionaries, conditional branching, loops, functions, and lambda expressions.

Key Topics & Practical Exercises:

Classes, objects, inheritance, polymorphism, encapsulation, magic methods, decorators, generators, list comprehensions, and exception handling.

Key Topics & Practical Exercises:

N-dimensional arrays, vectorization, slicing, indexing, Pandas Series & DataFrames, data cleaning, handling missing values, filtering, grouping, and merging datasets.

Key Topics & Practical Exercises:

Statistical data visualization with Matplotlib and Seaborn, histograms, scatter plots, box plots, correlation heatmaps, and feature engineering.

Key Topics & Practical Exercises:

Supervised learning (linear regression, logistic regression, decision trees, random forests), unsupervised learning (K-means clustering), model evaluation metrics, and cross-validation.

Key Topics & Practical Exercises:

ES6+ syntax (destructuring, async/await, fetch), React component lifecycle, useState, useEffect, props, state management, and Tailwind CSS responsive styling.

Key Topics & Practical Exercises:

RESTful architecture, Express routing, middleware, authentication with JWT, password hashing with bcrypt, and API endpoint documentation.

Key Topics & Practical Exercises:

CRUD operations with MongoDB (Mongoose ODM) and relational database design with PostgreSQL, schema validation, indexing, and aggregation pipelines.

Key Topics & Practical Exercises:

Deploying a full stack application with an integrated predictive machine learning API to cloud platforms (Vercel, Render, AWS), Git workflow, and interview preparation.