Python for Data Science: Analysis, Visualization & ML

Unlock the power of data with Python. This module guides you through essential libraries and techniques for data manipulation, statistical analysis, compelling visualizations, and an introduction to machine learning concepts.

1. Python Fundamentals for Data Analysis

Before diving into complex data, ensure your Python basics are solid. This section provides a quick recap or introduction to variables, data types, control flow, functions, and basic data structures essential for any data-related task.

# Define a list of numbers
numbers = [10, 20, 30, 40, 50]

# Add a new number
numbers.append(60)
print(f"List after append: {numbers}")

# Remove a number
numbers.remove(20)
print(f"List after remove: {numbers}")

# Calculate the sum
total = sum(numbers)
print(f"Sum of numbers: {total}")
            

2. Data Manipulation with Pandas

Pandas is the workhorse of data science in Python. Learn to create and manipulate DataFrames, import data from various sources (CSV, Excel), clean messy datasets, filter, group, and merge data effectively for analysis.

import pandas as pd

# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 28],
        'City': ['New York', 'San Francisco', 'Los Angeles', 'New York']}
df = pd.DataFrame(data)

print("Original DataFrame:\n", df)

# Filter data
filtered_df = df[df['Age'] > 29]
print("\nFiltered by Age > 29:\n", filtered_df)

# Group by City and calculate mean age
avg_age_by_city = df.groupby('City')['Age'].mean()
print("\nAverage Age by City:\n", avg_age_by_city)
            

3. Data Visualization with Matplotlib & Seaborn

Visualizing data is key to understanding it. This section covers creating various plots like line plots, scatter plots, bar charts, and histograms using Matplotlib, and making them more aesthetically pleasing and informative with Seaborn.

4. Introduction to Machine Learning (Scikit-learn)

Get a taste of Machine Learning. This section introduces fundamental concepts of supervised and unsupervised learning, and demonstrates how to build a simple predictive model using Scikit-learn, a popular ML library.

from sklearn.linear_model import LinearRegression
import numpy as np

# Sample data: X as features, y as target
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) # Must be 2D array
y = np.array([2, 4, 5, 4, 5])

# Create and train the model
model = LinearRegression()
model.fit(X, y)

# Make a prediction
prediction = model.predict(np.array([[6]]))
print(f"Prediction for X=6: {prediction[0]:.2f}")
            

Module Summary

You've successfully navigated the core aspects of Python for Data Science, from foundational Python scripting and powerful data manipulation with Pandas to creating insightful visualizations and taking your first steps into Machine Learning with Scikit-learn. These skills are invaluable for anyone working with data.

Next Steps & Related Modules

Deepen your Python knowledge with more advanced programming modules, or explore the dedicated Machine Learning Fundamentals module for a comprehensive dive.