📚 data-science-handbook - Learn Data Science Step-by-Step

🛠️ Download & Install

To begin your journey in data science, visit this page to download: Download Page.

🚀 Getting Started

Welcome to the data-science-handbook. This handbook includes Jupyter notebooks and reusable code designed to help you learn and apply data science workflows. You will find simple examples and explanations that guide you through data visualization, exploratory data analysis, and machine learning.

1. Prerequisites

Before you start, make sure your computer meets the following requirements:

• Operating System: Windows, macOS, or a Linux distribution.
• Python Version: Python 3.6 or later.
• Software: You will need Jupyter Notebook installed. This can be done through Anaconda or directly via Python pip.

2. Install Jupyter Notebook

If you don’t have Jupyter Notebook installed, follow these steps:

• Using Anaconda:

  1. Download the Anaconda installer.
  2. Run the installer and follow the instructions.

• Using pip:

  1. Open your command line interface (Command Prompt, Terminal).
  2. Run the following command:

     pip install jupyter
     

3. Download the Handbook

Now, return to the Download Page to access the latest version of the handbook. Click on the release that matches your needs and download the file.

4. Unzip the Files

Once you have downloaded the zip file, locate it on your computer. Extract the contents into a folder of your choice. You should see multiple Jupyter notebooks and code files.

5. Launch Jupyter Notebook

To open Jupyter Notebook, follow these steps:

1. Open your command line interface.
2. Navigate to the folder where you extracted the handbook. Use the cd command followed by the path to your folder. For example:

   cd path/to/your/folder
   

3. Run the command:

   jupyter notebook
   

This command will open a new tab in your web browser showing the Jupyter dashboard. Here, you can see all the Jupyter notebooks that came with the handbook.

6. Open a Notebook

To start learning, click on a notebook you want to open. The notebooks contain explanations and code snippets to help you understand concepts.

7. Running Code Cells

In the notebook, you will encounter different types of cells:

• Markdown Cells: These contain descriptions and explanations. You can read through these for context.
• Code Cells: These contain Python code you can run. Click on the cell and press Shift + Enter to execute the code. You will see the results directly below the cell.

8. Save Your Work

As you explore the notebooks and make changes, remember to save your work frequently. Click on the disk icon in the toolbar or use the shortcut Ctrl + S (Windows/Linux) or Command + S (macOS).

🧩 Understanding the Handbook

This handbook is organized into sections. Each section covers a specific topic in data science:

• Data Visualization: Learn how to represent data graphically. Use libraries such as Matplotlib and Seaborn to create plots that help you convey insights.

• Exploratory Data Analysis (EDA): Discover techniques to analyze datasets, identify patterns, and glean insights. Learn how to use libraries like Pandas and NumPy effectively.

• Machine Learning: Understand basic machine learning concepts. Explore algorithms and methods that can help you make predictions based on your data.

📖 Additional Resources

Here are some helpful resources to deepen your understanding of data science:

• Documentation for Pandas: Learn more about data manipulation with Pandas Documentation.
• Matplotlib Guide: Explore data visualization techniques through the Matplotlib Documentation.
• Scikit-learn Guide: Familiarize yourself with machine learning concepts using the Scikit-learn Documentation.

📞 Get Help

If you encounter any issues while downloading or running the handbook, feel free to open an issue on the GitHub Issues page. Our community is here to help you.

Thank you for choosing the data-science-handbook! Enjoy your learning experience.