**Day 1: Introduction to Data Science**

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In the first day of this tutorial, you will learn the basics of Data Science.

- What is Data Science?

- Definition of Data Science
- Why Data Science is important?
- Applications of Data Science

- Basics of Statistics

- Types of Data
- Descriptive Statistics
- Inferential Statistics

- Steps in Data Science Process
- Data Collection
- Data Cleaning
- Data Exploration
- Data Analysis
- Data Visualization
- Model Building
- Model Deployment

**Day 2: Data Analysis and Visualization**

Data Analysis is the process of cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. Data Visualization is the representation of data in graphical or pictorial format to communicate insights effectively. In the second day of this tutorial, you will learn the basics of Data Analysis and Visualization.

- Data Analysis

- Data Wrangling
- Data Transformation
- Data Modeling
- Data Interpretation

- Data Visualization

- Introduction to Data Visualization
- Types of Charts
- Best Practices for Data Visualization
- Data Visualization Tools

**Day 3: Machine Learning**

Machine Learning is a subfield of Data Science that involves the use of algorithms and statistical models to enable machines to learn from data and improve their performance on a task. In the third day of this tutorial, you will learn the basics of Machine Learning.

- Introduction to Machine Learning

- What is Machine Learning?
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning

- Machine Learning Algorithms

- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines
- K-Nearest Neighbors

- Evaluation and Deployment of Machine Learning Models

- Model Evaluation Metrics
- Hyperparameter Tuning
- Model Deployment

By the end of this 3-day tutorial, you should have a basic understanding of the Data Science process, Data Analysis and Visualization, and Machine Learning. Keep practicing and exploring to become a proficient Data Scientist.

## Data Science Tutorial Summary

This tutorial is designed to provide beginners with an overview of the field of Data Science. It is a 3-day tutorial that covers the basics of Data Science, Data Analysis and Visualization, and Machine Learning.

On Day 1, learners are introduced to Data Science and its importance in various fields. They also learn the basics of Statistics and the Data Science Process.

On Day 2, learners are introduced to Data Analysis and Visualization. They learn about Data Wrangling, Data Transformation, Data Modeling, and Data Interpretation. They also learn about the different types of charts and best practices for Data Visualization.

On Day 3, learners are introduced to Machine Learning. They learn what Machine Learning is, the different types of Machine Learning, and the different algorithms used in Machine Learning. They also learn about Model Evaluation Metrics, Hyperparameter Tuning, and Model Deployment.

By the end of this tutorial, learners should have a basic understanding of Data Science, Data Analysis and Visualization, and Machine Learning. They should also have an idea of how to start exploring these fields further.

## What is Data Science?

Data Science is an interdisciplinary field that involves using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. The field of Data Science encompasses a range of disciplines, including statistics, computer science, mathematics, and domain-specific knowledge.

Data Science involves several stages, including data collection, data cleaning, data exploration, data analysis, and data visualization. The goal of Data Science is to uncover patterns and relationships in data, make predictions and inform decision-making.

Data Science has numerous applications across a range of industries, including healthcare, finance, marketing, and transportation. It is a rapidly growing field that is driven by the increasing availability of data and the need for organizations to make data-driven decisions.