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README.md

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 # Python-Data-Science-Onboarding
-Coming Soon
+
+Welcome to the WMU DSC/Developer Club!<br>
+This repository is designed to help new members get familiar with the tools and workflows commonly used in our data science projects.
+<br>
+<br>
+
+
+
+## 🚀 Who is this for?
+
+This tutorial assumes you already have *basic Python knowledge*, including:
+
+- Using numpy and pandas for data handling  
+- Knowing what a .ipynb Jupyter Notebook file is  
+- Using scikit-learn to build simple machine learning models
+
+<details>
+<summary><b>❓Don't know Python yet? No problem!❓</b></summary>
+<br>
+
+>  **Start with the resources below before continuing:**<br>
+> &emsp;&emsp;[W3Schools Python Tutorial](https://www.w3schools.com/python/)  
+> &emsp;&emsp;[Google's Python Class](https://developers.google.com/edu/python)  
+> &emsp;&emsp;[Python for Beginners (YouTube)](https://www.youtube.com/watch?v=K5KVEU3aaeQ&t=56s)  
+</details>
+
+
+<details>
+<summary> <b>❓Python Installation Guide For Beginners❓</b></summary>
+<br>
+
+> ### To follow along with the notebooks in this repository, you need Python installed on your machine.
+> ### 🎥 How to Install Python
+> &emsp;&emsp;  [For macOS](https://www.youtube.com/watch?v=nhv82tvFfkM)  
+> &emsp;&emsp;  [For Windows](https://www.youtube.com/watch?v=YagM_FuPLQU)<br><br>
+> 📌 *Important*: During installation, make sure to check:  
+>  *“Add Python to PATH”*
+
+###  Verify Your Installation
+
+After installing, open a terminal (or Command Prompt on Windows), and run:
+
+```bash
+python --version
+pip --version
+```
+</details>
+<br>
+<br>
+
+
+
+## 📦 Recommended Libraries
+
+In Python, you install packages by running:
+```bash
+pip install <package-name>
+```
+
+Before you dive into the notebooks, make sure you have the core data-science libraries installed. You can install them all at once via pip:
+
+```bash
+pip install \
+  numpy \
+  pandas \
+  matplotlib \
+  seaborn \
+  scikit-learn \
+  notebook
+```
+<br>
+<br>
+
+
+
+## 📘 Core Topics
+
+<details>
+<summary> <b>🔥Understanding Jupyter Notebooks (.ipynb)</b>🔥</summary>
+What are text vs code cells, how to run them, and best practices for documenting your analysis.
+# 📝 Jupyter Notebook Quickstart Guide
+
+This guide will introduce you to Jupyter Notebook—from “what it is” to how to install and use it locally or in the cloud—then walk you through basic operations, hands-on examples, Markdown usage, and sharing.
+
+---
+
+## 🔍 What Is Jupyter Notebook?
+
+Jupyter Notebook is an interactive computing environment where you can combine live code, equations, visualizations, and narrative text in a single document (`.ipynb`). It’s widely used for data analysis, teaching, and rapid prototyping.
+
+- **Key Features**  
+  - Interactive code execution  
+  - Rich text via Markdown (headings, lists, LaTeX)  
+  - Inline data visualizations  
+  - Easy sharing and reproducibility  
+
+---
+
+## ⚙️ Installation & Access
+
+### 1. Install Locally
+
+You’ll need Python installed first. Then:
+
+```bash
+# Install Jupyter Notebook via pip
+pip install notebook
+```
+Or, if you use Conda:
+```bash
+conda install -c conda-forge notebook
+```
+After installation, launch the notebook server:
+```bash
+jupyter notebook
+```
+Your default browser will open at http://localhost:8888, showing the notebook dashboard.
+
+### 2. Use JupyterLab (Optional)
+For a more full-featured interface:
+
+```bash
+pip install jupyterlab
+jupyter lab
+```
+### 3. Cloud / Web Options
+Google Colab
+
+1. Go to colab.research.google.com
+2. Sign in with your Google account
+3. Open or upload any .ipynb file
+</details>
+
+
+
+<details>
+<summary> <b>🔥Data Handling with NumPy & Pandas🔥</b></summary>
+ Learn how to load, clean, and manipulate data using NumPy arrays and Pandas DataFrames.
+
+## 🔍 Library Overview
+
+Before we dive in, here's a quick intro to the two core libraries we’ll use:
+
+###  NumPy
+- **The fundamental package for numerical computing in Python.**
+- **Key features:**
+  - **Arrays:** Homogeneous, N-dimensional arrays (faster and more memory-efficient than Python lists)  
+  - **Vectorized ops:** Element-wise arithmetic without explicit loops  
+  - **Linear algebra & random:** Built-in support for matrix operations and pseudo-random number generation  
+
+###  Pandas
+- **A powerful data analysis and manipulation library built on top of NumPy.**  
+- **Key features:**
+  - **DataFrame:** 2D tabular data structure with labeled axes (rows & columns)  
+  - **IO tools:** Read/write CSV, Excel, SQL, JSON, and more  
+  - **Series:** 1D labeled array, great for time series and single-column tables  
+  - **Grouping & aggregation:** Split-apply-combine workflows for summarizing data  
+
+
+
+### 1. What  
+> **What you will learn in this section.**  
+> By the end of this notebook, you will be able to:  
+> - Create and manipulate NumPy arrays of different shapes and dtypes  
+> - Perform element-wise arithmetic and universal functions
+> - Index, slice, and reshape arrays for efficient computation  
+
+---
+
+### 2. Why  
+> **Why this topic matters.**  
+> NumPy arrays are the foundation of nearly all scientific computing in Python.  
+> They provide:  
+> - **Speed:** Vectorized operations run much faster than Python loops  
+> - **Memory efficiency:** Compact storage of homogeneous data  
+> - **Interoperability:** A common data structure for libraries like Pandas, SciPy, and scikit-learn  
+
+---
+
+### 3. How  
+> **How to do it.**  
+> Follow these step-by-step examples:
+
+```python
+import numpy as np
+
+# 1) Create arrays
+a = np.array([1, 2, 3, 4])
+b = np.arange(0, 10, 2)          # [0, 2, 4, 6, 8]
+c = np.zeros((2, 3), dtype=int)  # 2×3 array of zeros
+
+# 2) Element-wise arithmetic
+sum_ab = a + b[:4]               # adds element by element
+prod_ab = a * b[:4]              # multiplies element by element
+
+# 3) Universal functions
+sqrt_b = np.sqrt(b)              # square root of each element
+exp_a  = np.exp(a)               # eᵃ for each element
+
+# 4) Indexing & slicing
+row = b[2:5]                     # slice subarray
+c[0, :] = row                    # assign a row
+
+# 5) Reshape & combine
+d = np.linspace(0, 1, 6).reshape(2, 3)
+stacked = np.vstack([c, d])      # vertical stack of two 2×3 arrays
+
+
+```
+</details>
+
+
+
+<details>
+<summary> <b>🔥Basic Machine Learning with scikit-learn🔥</b></summary>
+Build your first regression and classification models, split data, and evaluate performance.
+
+## 🔍 Library Overview
+scikit-learn is one of the most widely used ML libraries in Python.  
+It provides simple APIs for preprocessing, training models, and evaluating performance.
+
+### ✨ Key Features
+- Large collection of supervised & unsupervised algorithms  
+- Easy dataset splitting, scaling, and pipelines  
+- Built-in metrics for evaluation  
+- Works seamlessly with NumPy & pandas  
+
+---
+
+### 1. What
+> **What you will learn in this section.**  
+> By the end of this notebook, you will be able to:  
+> - Split data into train/test sets  
+> - Train a simple regression model  
+> - Train a classification model  
+> - Evaluate predictions using accuracy and error metrics  
+
+---
+
+### 2. Why
+> **Why this topic matters.**  
+> - Machine Learning is the core of many data science projects.  
+> - scikit-learn offers a consistent interface to try many models quickly.  
+> - Understanding the ML workflow (split → train → predict → evaluate) is essential.  
+
+---
+
+### 3. How
+> **How to do it.**  
+> Follow these hands-on examples:
+
+```python
+from sklearn.datasets import load_iris, make_regression
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.metrics import mean_squared_error, accuracy_score
+import numpy as np
+
+# --- Regression Example ---
+# Generate synthetic data
+X_reg, y_reg = make_regression(n_samples=100, n_features=1, noise=10, random_state=42)
+
+# Train/test split
+X_train, X_test, y_train, y_test = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)
+
+# Fit linear regression
+reg = LinearRegression()
+reg.fit(X_train, y_train)
+
+# Predict and evaluate
+y_pred = reg.predict(X_test)
+print("MSE (Regression):", mean_squared_error(y_test, y_pred))
+
+
+# --- Classification Example ---
+iris = load_iris()
+X_clf, y_clf = iris.data, iris.target
+
+X_train, X_test, y_train, y_test = train_test_split(X_clf, y_clf, test_size=0.2, random_state=42)
+
+clf = LogisticRegression(max_iter=200)
+clf.fit(X_train, y_train)
+
+y_pred = clf.predict(X_test)
+print("Accuracy (Classification):", accuracy_score(y_test, y_pred))
+
+```
+</details>
+
+