Data Science Mastery: Complete Data Science Bootcamp 2025

Learn Data Science from Scratch: Build, Analyze, and Deploy AI-Powered Solutions

What you'll learn

Understand Data Science Workflow: Master the end-to-end data science lifecycle, from data collection to model deployment.

Data Collection Techniques: Learn to gather data from APIs, databases, and web scraping.

Data Preprocessing: Clean and preprocess raw data for analysis and modeling.

Exploratory Data Analysis (EDA): Uncover patterns and trends in datasets using visualization tools.

Feature Engineering: Create and optimize features to improve model performance.

Machine Learning Models: Build regression, classification, and clustering models using scikit-learn.

Deep Learning Techniques: Train neural networks with TensorFlow and PyTorch.

Model Deployment: Serve AI models using Flask, FastAPI, and Docker.

Big Data Handling: Work with large datasets using tools like Hadoop and Spark.

Ethical AI Practices: Understand data privacy, bias mitigation, and AI governance.

Requirements

Basic Computer Skills: Familiarity with using computers, installing software, and navigating file systems.

Fundamental Programming Knowledge (Optional): Basic understanding of programming concepts like variables, loops, and functions (Python preferred).

Mathematics Fundamentals: High-school-level understanding of algebra, statistics, and basic probability.

Logical Thinking: Ability to approach problems methodically and think critically.

A Stable Computer Setup: A computer with at least 8GB RAM (16GB recommended), 50GB free storage, and the ability to install Python and relevant libraries.

Curiosity and Passion for Learning: An eagerness to learn, experiment, and explore the exciting world of Data Science.

Time Commitment: Willingness to dedicate 10-15 hours per week to lessons, exercises, and projects.

Description

In a world driven by data, the ability to extract meaningful insights and build intelligent systems is no longer optional—it’s essential. "Data Science Mastery: From Fundamentals to Real-World Applications" is a comprehensive course designed to take you from a beginner to a confident data scientist, equipped with the skills to thrive in today’s data-driven industries. Whether you're a student, a professional looking to transition careers, or a tech enthusiast eager to explore data science, this course offers a step-by-step roadmap tailored to your learning needs.Starting with the basics of data collection and preprocessing, you’ll learn how to gather raw data from multiple sources, clean and prepare it for analysis, and uncover hidden patterns using exploratory data analysis (EDA). You’ll dive deep into feature engineering, where you'll transform raw data into meaningful variables that power predictive models. Visualization techniques using tools like Matplotlib and Seaborn will help you communicate your findings effectively.As the course progresses, you’ll explore machine learning algorithms, learning to build regression, classification, and clustering models. With hands-on projects, you'll implement these concepts using scikit-learn, TensorFlow, and PyTorch. You'll gain a strong foundation in deep learning, including neural networks and advanced architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).But data science doesn’t stop at building models—it extends to model evaluation, deployment, and serving real-time predictions. You'll learn how to deploy your models using tools like Flask, Docker, and FastAPI, ensuring they are production-ready. Additionally, this course emphasizes ethical AI practices, guiding you on topics like bias mitigation, transparency, and compliance with data privacy regulations.By the end of this course, you’ll have built an impressive portfolio of projects, demonstrating your ability to tackle real-world data problems and deliver actionable insights. Whether your goal is to become a Data Scientist, Machine Learning Engineer, or AI Specialist, this course equips you with the knowledge, tools, and confidence to excel in the ever-evolving field of data science.Get ready to transform data into decisions, insights, and innovation—the future starts here!

Overview

Section 1: Data Science Modules - Introduction and Brief Overview

Lecture 1 What Will We Cover

Lecture 2 Module 1: Data Collection – The Foundation of Data Science

Lecture 3 Mod 2: Data Cleaning and Preprocessing– Turning Raw Data into Usable Insights

Lecture 4 Module 3: Data Exploration and Analysis (EDA)

Lecture 5 Module 4: Feature Engineering – Transforming Data into Insights

Lecture 6 Module 5: Data Visualization – Communicating Insights Effectively

Lecture 7 Module 6: Machine Learning and Modeling – Building Intelligent Systems

Lecture 8 Module 7: Model Evaluation and Validation – Ensuring Reliable Predictions

Lecture 9 Module 8: Model Deployment –Bringing Machine Learning Models to Life

Lecture 10 Module 9: Big Data Technologies– Managing and Analyzing Massive Datasets

Lecture 11 Module 10: Data Ethics and Governance –Responsible AI and Data Practices

Lecture 12 Module 11: Business Understanding and Domain Expertise

Lecture 13 Mod 12: Communication and Storytelling– Turning Data into Impactful Narratives

Lecture 14 Whats Next: Bootcamp Deep Dive

Section 2: Week 1: Python Programming Basics

Lecture 15 Introduction to Week 1 Python Programming Basics

Lecture 16 Day 1: Introduction to Python and Development Setup

Lecture 17 Day 2: Control Flow in Python

Lecture 18 Day 3: Functions and Modules

Lecture 19 Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)

Lecture 20 Day 5: Working with Strings

Lecture 21 Day 6: File Handling

Lecture 22 Day 7: Pythonic Code and Project Work

Section 3: Week 2: Data Science Essentials

Lecture 23 Introduction to Week 2 Data Science Essentials

Lecture 24 Day 1: Introduction to NumPy for Numerical Computing

Lecture 25 Day 2: Advanced NumPy Operations

Lecture 26 Day 3: Introduction to Pandas for Data Manipulation

Lecture 27 Day 4: Data Cleaning and Preparation with Pandas

Lecture 28 Day 5: Data Aggregation and Grouping in Pandas

Lecture 29 Day 6: Data Visualization with Matplotlib and Seaborn

Lecture 30 Day 7: Exploratory Data Analysis (EDA) Project

Section 4: Week 3: Mathematics for Machine Learning

Lecture 31 Introduction to Week 3 Mathematics for Machine Learning

Lecture 32 Day 1: Linear Algebra Fundamentals

Lecture 33 Day 2: Advanced Linear Algebra Concepts

Lecture 34 Day 3: Calculus for Machine Learning (Derivatives)

Lecture 35 Day 4: Calculus for Machine Learning (Integrals and Optimization)

Lecture 36 Day 5: Probability Theory and Distributions

Lecture 37 Day 6: Statistics Fundamentals

Lecture 38 Day 7: Math-Driven Mini Project – Linear Regression from Scratch

Section 5: Week 4: Probability and Statistics for Machine Learning

Lecture 39 Introduction to Week 4 Probability and Statistics for Machine Learning

Lecture 40 Day 1: Probability Theory and Random Variables

Lecture 41 Day 2: Probability Distributions in Machine Learning

Lecture 42 Day 3: Statistical Inference - Estimation and Confidence Intervals

Lecture 43 Day 4: Hypothesis Testing and P-Values

Lecture 44 Day 5: Types of Hypothesis Tests

Lecture 45 Day 6: Correlation and Regression Analysis

Lecture 46 Day 7: Statistical Analysis Project – Analyzing Real-World Data

Aspiring Data Scientists: Individuals who want to start a career in data science but don’t know where to begin.,Students and Graduates: Learners from diverse educational backgrounds looking to add data science to their skill set.,Professionals Seeking a Career Switch: Working professionals aiming to transition into data-centric roles like Data Analyst, Machine Learning Engineer, or AI Specialist.,Tech Enthusiasts: Curious minds eager to understand how data can drive decisions and power intelligent systems.,Business Professionals: Decision-makers and managers looking to leverage data insights to improve strategy and operations.,Freelancers and Entrepreneurs: Individuals aiming to build data-driven solutions or AI-powered products.