From 7a15ad6eb9e134cde1d8f32c8a09fca91734faef Mon Sep 17 00:00:00 2001 From: AswinBarath Date: Thu, 18 Sep 2025 14:00:59 +0530 Subject: [PATCH] Add Intro Notes and Hello World Jupyter Notebook --- 0.Introduction/1.Introduction.md | 83 +++++++++ 0.Introduction/2.Data_and_information.md | 119 +++++++++++++ 0.Introduction/3.Environment_setup.md | 161 ++++++++++++++++++ 0.Introduction/3.Hello_world.ipynb | 89 ++++++++++ .../4.Python_Developer_Environment_Setup.md | Bin 0 -> 7079 bytes .../5.Join_our_Online_Community_(Discord).md | 23 +++ 6 files changed, 475 insertions(+) create mode 100644 0.Introduction/1.Introduction.md create mode 100644 0.Introduction/2.Data_and_information.md create mode 100644 0.Introduction/3.Environment_setup.md create mode 100644 0.Introduction/3.Hello_world.ipynb create mode 100644 0.Introduction/4.Python_Developer_Environment_Setup.md create mode 100644 0.Introduction/5.Join_our_Online_Community_(Discord).md diff --git a/0.Introduction/1.Introduction.md b/0.Introduction/1.Introduction.md new file mode 100644 index 0000000..56ca94a --- /dev/null +++ b/0.Introduction/1.Introduction.md @@ -0,0 +1,83 @@ +# Python for Data Analytics and Visualization - Course Introduction + +## Welcome & Instructor Introduction + +- **Instructor:** Zain from Job Ready Programmer +- **Experience:** Data consultant with 13+ years of Python experience +- **LinkedIn:** [linkedin.com/in/zain-ashfaq/](https://www.linkedin.com/in/zain-ashfaq/) + +## Why Data Science? + +- **Top Career Choice:** Data scientist has been the #1 job for years consecutively +- **Growing Field:** Continuously expanding as more data becomes available +- **High Compensation:** Companies like Apple, Amazon, and Facebook pay top dollar +- **Benefits:** + - Higher salaries + - Exposure to cutting-edge problems + - Opportunity to solve real-world challenges + +## Course Overview + +This course covers Python libraries divided into two main categories: + +### 1. Data Analytics Libraries + +- **NumPy** - Array creation, operations, and I/O +- **SciPy** - Scientific computing +- **Pandas** - Data manipulation and analysis + +### 2. Visualization Libraries + +- **Matplotlib** - Basic and detailed plotting +- **Seaborn** - Statistical plotting +- **Plotly** - Interactive visualizations + +## Course Content Structure + +### Part 1: Working with Data + +#### NumPy + +- Creating arrays +- Array operations +- Reading and writing array data +- Dedicated package for numerical arrays + +#### Pandas + +- Creating and operating on DataFrames +- Using location indices to find data +- Data cleaning and issue resolution + +### Part 2: Data Visualization + +#### Matplotlib + +- Quick data plotting +- Detailed plotting capabilities +- Creating subplots +- Advanced plotting features + +#### Seaborn (Statistical Plotting Library) + +- Distribution plots +- Categorical plots +- Matrix plots +- Better understanding of data patterns + +#### Plotly (Interactive Visualization) + +- Interactive plots +- Interactive statistical visualizations +- Enhanced user engagement with data + +## Course Benefits + +- **Foundation Building:** Sets up learners for advanced topics like machine learning +- **Career Preparation:** Equips students for the data-intensive world +- **Skill Development:** Builds comprehensive data analytics toolkit +- **Continued Learning:** Gateway to follow-on data courses at Job Ready Programmer + +## Next Steps + +This course is designed to prepare you for the data-driven future and provide a solid foundation for advanced data science topics. diff --git a/0.Introduction/2.Data_and_information.md b/0.Introduction/2.Data_and_information.md new file mode 100644 index 0000000..992e158 --- /dev/null +++ b/0.Introduction/2.Data_and_information.md @@ -0,0 +1,119 @@ +# Data and Information - Fundamentals of Data Analytics + +## Welcome & Instructor Introduction + +**Instructor:** Zane - Data Consultant +**Purpose:** Understanding basic concepts before diving into technical aspects + +## What is Data? + +### Definition + +- **Data:** Small, isolated piece of fact that doesn't make sense on its own +- **Example:** Age information without knowing who it belongs to +- **Characteristics:** + - Isolated pieces of information + - Not very useful in isolation + - Abundant in our surroundings + - Requires synthesis to become useful + +### The Power of Connection + +Data becomes more meaningful when connected together: + +#### Car Information Example + +- **Speed alone:** Limited usefulness +- **Speed + Direction:** Better understanding +- **Speed + Direction + Location:** Complete picture + +## Data Structures + +### Vectors + +**Definition:** Multiple data items combined together + +#### Example: Car A traveling information + +- Speed: 25 km/hour +- Direction: West +- Location: Main Street + +**Key Concepts:** + +- **Vector:** Collection of related data points +- **Dimensions:** Each type of data (speed, direction, location) +- Provides comprehensive information about a single entity + +### Matrices + +**Definition:** Multiple vectors combined together + +#### Example: Information about multiple cars + +- Car A: Speed, Direction, Location +- Car B: Speed, Direction, Location +- Car C: Speed, Direction, Location + +**Benefits:** + +- Bigger picture understanding +- Complete map of multiple entities +- Comprehensive data overview + +### Table Representation + +**Structure:** + +- **Rows:** Individual entities (Car A, Car B, Car C) +- **Columns:** Dimensions (Speed, Direction, Location) +- **Format:** Traditional table layout + +## Important Terminology + +### Interchangeable Terms + +| Term 1 | Term 2 | Definition | +|--------|--------|------------| +| **Vector** | **Series** | Set of data items for one entity | +| **Matrix** | **Data Frame** | Collection of vectors/series | + +**Key Understanding:** + +- Vector = Series (both represent data sets) +- Matrix = Data Frame (both represent collections of data sets) +- These terms will be used interchangeably in data analytics discussions + +## From Data to Information + +### The Progression + +1. **Data:** Isolated facts +2. **Information:** Connected data that provides meaning +3. **Insights:** Connected information revealing trends +4. **Predictions:** Using insights to forecast future outcomes + +### The Value Chain + +```text +Data → Information → Insights → Predictions +``` + +**Benefits of Connection:** + +- Isolated values have limited use +- Connected data provides context +- Information reveals patterns +- Insights enable decision-making +- Predictions guide future actions + +## Key Takeaways + +- **Data Structure Hierarchy:** Data → Vectors → Matrices +- **Terminology:** Understand interchangeable terms for better comprehension +- **Value Creation:** Connection transforms data into actionable information +- **Foundation:** These concepts are essential for data analytics understanding + +## Next Steps + +These fundamental concepts will be referenced throughout the course to help make data analytics discussions more meaningful and accessible. diff --git a/0.Introduction/3.Environment_setup.md b/0.Introduction/3.Environment_setup.md new file mode 100644 index 0000000..59f5b7e --- /dev/null +++ b/0.Introduction/3.Environment_setup.md @@ -0,0 +1,161 @@ +# Environment Setup - Python Data Analytics Development + +## Overview & Course Goals + +**Instructor:** Zain +**Programming Language:** Python +**Course Objectives:** + +- Learn data analytics and machine learning techniques +- Understand the underlying concepts and processes +- Work with visual plots and interactive notebooks + +## Why Anaconda? + +### Key Benefits + +- **Graphical User Interface:** Visual interface for better learning experience +- **Jupyter Notebook Integration:** Interactive development environment +- **Pre-installed Python:** No separate Python installation required +- **Built-in Data Analytics Packages:** Includes essential libraries out-of-the-box + +### Pre-installed Packages + +- **NumPy** - Numerical computing +- **Pandas** - Data manipulation and analysis +- **Matplotlib** - Data visualization +- **Scikit-learn** - Machine learning library + +## Installation Guide + +### Step 1: Download Anaconda + +1. **Search:** Go to Google and search for "Anaconda" +2. **Official Website:** Click on [Anaconda.com](https://www.anaconda.com/) - "The world's most popular data science platform" +3. **Navigation:** Go to "Get Started" → "See all Anaconda products" +4. **Choose Edition:** Select the **Free Individual Edition** +5. **Download Page:** Click "Learn More" to access the download page + +### Step 2: Select Your Installer + +**Important:** Choose the installer that matches your operating system: + +- **Windows:** Download the graphical installer +- **macOS:** Download the macOS installer +- **Linux:** Download the Linux installer + +**Python Version:** Download the latest Python version available (3.7+ recommended) + +### Step 3: Installation Process + +#### Windows Installation Steps + +1. **Run Installer:** Click on the downloaded file +2. **Welcome Screen:** Click "Next" to continue +3. **License Agreement:** Click "I Agree" to continue +4. **Installation Type:** + - **Recommended:** "Just Me" (easier, no admin password required) + - **Alternative:** "All Users" (requires administrator password) +5. **Installation Location:** Leave default path as is +6. **Advanced Options:** + - ✅ **Check "Add Anaconda to PATH"** (despite "not recommended" warning) + - This makes it easier for system to find the installation +7. **Install:** Click "Install" and wait for completion +8. **Completion:** + - Click "Next" → "Next" + - Optionally uncheck "Learn more about Anaconda" and tutorial + - Click "Finish" + +## Getting Started with Anaconda + +### Launching Anaconda Navigator + +1. **Access:** Go to Start Menu → Type "Anaconda" → Open "Anaconda Navigator" +2. **Interface:** The Navigator provides access to various tools: + - **Jupyter Notebook** - Interactive coding environment + - **Jupyter Lab** - Advanced notebook interface + - **Spyder** - IDE for Python development + - **Other tools** - Additional development applications + +### Using Jupyter Notebook + +#### Launching Jupyter + +1. **From Navigator:** Click "Launch" under Jupyter Notebook +2. **Browser Interface:** Opens in your default web browser +3. **File System:** Shows your computer's directory structure (Desktop, Downloads, Documents) + +#### Creating Your First Notebook + +1. **Navigation:** Browse to your desired folder (e.g., Downloads) +2. **New Notebook:** Click "New" → "Python 3" notebook +3. **Interface:** Opens a new tab with an interactive Python notebook + +#### Basic Jupyter Operations + +**Running Code:** + +- Type Python code in cells +- **Method 1:** Click "Run" button +- **Method 2:** Press `Shift + Enter` (recommended shortcut) + +**Example Code to Test:** + +```python +# Basic output +print("Hello World") + +# Variable assignment and display +print("My name is Zain") + +# Mathematical operations +a = 1 + 3 +print(a) # Shows output: 4 + +# Direct value display (without print) +a # Shows: 4 +``` + +## Essential Tools & Resources + +### Official Links + +- **[Anaconda Distribution](https://www.anaconda.com/products/distribution)** - Main download page +- **[Jupyter Notebook](https://jupyter.org/)** - Official Jupyter project +- **[Python](https://www.python.org/)** - Official Python website +- **[NumPy](https://numpy.org/)** - Numerical computing library +- **[Pandas](https://pandas.pydata.org/)** - Data analysis library +- **[Matplotlib](https://matplotlib.org/)** - Plotting library +- **[Scikit-learn](https://scikit-learn.org/)** - Machine learning library + +### Documentation Resources + +- **[Anaconda Documentation](https://docs.anaconda.com/)** - Complete Anaconda guide +- **[Jupyter Notebook Documentation](https://jupyter-notebook.readthedocs.io/)** - Notebook user guide +- **[Python Documentation](https://docs.python.org/)** - Official Python docs + +## Verification & Next Steps + +### Testing Your Setup + +1. **Launch Jupyter Notebook** successfully +2. **Create a new notebook** +3. **Run basic Python commands** (print statements, variables) +4. **Verify output display** in the notebook interface + +### What's Next + +- **Course Progression:** Ready for data analytics and machine learning development +- **Learning Path:** Will explore Python commands and data science packages +- **Practical Application:** Use different packages for various analytical tasks + +## Troubleshooting Tips + +- **Path Issues:** If Anaconda commands aren't recognized, ensure PATH was added during installation +- **Browser Problems:** Jupyter opens in your default browser; try a different browser if issues occur +- **Performance:** Close unnecessary applications if Anaconda runs slowly +- **Updates:** Keep Anaconda updated for the latest features and security patches + +## Conclusion + +Your development environment is now ready for comprehensive data analytics and machine learning work with Python, Jupyter notebooks, and all essential data science libraries pre-installed. diff --git a/0.Introduction/3.Hello_world.ipynb b/0.Introduction/3.Hello_world.ipynb new file mode 100644 index 0000000..ab45e2b --- /dev/null +++ b/0.Introduction/3.Hello_world.ipynb @@ -0,0 +1,89 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "f30d5262-5025-4556-9187-89ded9b350a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello world\n" + ] + } + ], + "source": [ + "print(\"hello world\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f79f13f-cfdb-4339-acf6-183519715271", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "my name is Zain\n" + ] + } + ], + "source": [ + "print(\"my name is Zain\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ab38f556-13d3-43b6-8a18-5420ab190cc7", + "metadata": {}, + "outputs": [], + "source": [ + "a = 1 + 3" + ] + }, + { + "cell_type": "code", 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