1 Python for Data Science 🐍

Master Python programming for machine learning, data analysis, and scientific computing.

1.1 Core Python Skills

1.1.1 Python Fundamentals

• Data types and structures
• Control flow and functions
• Object-oriented programming
• Error handling and debugging

1.1.2 Essential Libraries

• NumPy - Numerical computing and arrays
• Pandas - Data manipulation and analysis
• Matplotlib & Seaborn - Data visualization
• Scikit-learn - Machine learning toolkit

1.1.3 Data Science Workflow

1. Data Collection - APIs, web scraping, databases
2. Data Cleaning - Handling missing values, outliers
3. Exploratory Data Analysis - Statistical summaries, visualization
4. Feature Engineering - Creating and selecting features
5. Model Building - Training and evaluation
6. Deployment - Production-ready solutions

1.1.4 Advanced Python

• Jupyter Notebooks - Interactive development
• Virtual environments - Project management
• Git version control - Collaboration
• Testing and documentation - Best practices

1.2 Practical Examples

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
 
# Load and explore data
data = pd.read_csv('dataset.csv')
print(data.head())
print(data.info())
 
# Prepare features and target
X = data.drop('target', axis=1)
y = data['target']
 
# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)
 
# Train model
model = LinearRegression()
model.fit(X_train, y_train)
 
# Evaluate
score = model.score(X_test, y_test)
print(f"Model R² score: {score:.3f}")

Code your way to machine learning mastery with Python.