Data Science Master Notes and Code

This repository contains comprehensive notes and code written during my journey to learn Data Science. It is organized into various sections covering essential topics and concepts, providing a valuable resource for anyone interested in mastering Data Science.

Table of Contents

1. Introduction
2. Python Basics
3. Data Analysis
4. Data Visualization
5. Machine Learning
6. Deep Learning
7. Resources

Introduction

Welcome to my Data Science repository! This collection includes all the notes and code I have accumulated while learning Data Science. The purpose of this repository is to serve as a reference for myself and others interested in this field.

Python Basics

This section covers the fundamental concepts of Python programming necessary for data science, including:

• Variables and Data Types
• Control Structures
• Functions
• Libraries: NumPy, Pandas

Data Analysis

In this section, you will find notes and code related to data analysis, including:

• Data Cleaning
• Data Manipulation
• Exploratory Data Analysis (EDA)

Data Visualization

This section includes techniques and code for data visualization using Python libraries such as:

• Matplotlib
• Seaborn
• Plotly

Machine Learning

This section covers various machine learning algorithms and their implementation, including:

• Supervised Learning
• Unsupervised Learning
• Model Evaluation and Tuning

Deep Learning

This section delves into deep learning concepts and their practical applications using frameworks like TensorFlow and Keras, including:

• Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

Resources

Here you will find a list of resources, including books, tutorials, and articles that have been instrumental in my learning journey.

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Note: This repository is a work in progress and will be updated continuously as I learn more about data science.