Data Science & Machine Learning

Python is the undisputed leader in Data Science and Artificial Intelligence. This dominance is not because Python is “fast”—it’s because Python acts as a user-friendly wrapper for high-performance libraries written in C and Fortran.

1. NumPy: The Foundation

NumPy provides the ndarray (N-dimensional array), which is the base for almost every other data science library.

Why not just use Python Lists?

Python lists are collections of “Pointers” to objects. NumPy arrays are blocks of contiguous memory. This allows for Vectorization: performing an operation on an entire array at once rather than looping through it.

import numpy as np

# A list of 1 million numbers
data = np.arange(1_000_000)

# NumPy squares every number simultaneously in C
# This is ~100x faster than a Python for loop!
squares = data ** 2

2. Pandas: The Data Swiss-Army Knife

Pandas introduces the DataFrame, which is essentially a high-performance Excel spreadsheet that lives inside your Python code.

import pandas as pd

df = pd.read_csv("sales_data.csv")

# Filter rows and calculate average in one line
avg_revenue = df[df["region"] == "North"]["revenue"].mean()

3. Scikit-Learn & AI

Once your data is clean, you use Scikit-Learn to build Predictive Models (Machine Learning).

from sklearn.linear_model import LinearRegression

model = LinearRegression()
# X = input features, y = target to predict
model.fit(X_train, y_train)
predictions = model.predict(X_test)

4. Under the Hood: The Scientific Stack

The Python Data ecosystem is often called the “SciPy Stack”:

NumPy: Fast math and arrays.
Pandas: Data manipulation and cleaning.
Matplotlib/Seaborn: Data visualization (graphs).
SciPy: Advanced statistics and signal processing.
PyTorch/TensorFlow: Deep Learning and Neural Networks.

5. Summary Table

Library	Primary Data Structure	Usage
NumPy	`ndarray`	Matrices, Math, Image data.
Pandas	`DataFrame`	CSVs, SQL tables, Time Series.
Matplotlib	`Figure`	Charts, Histograms, Scatter plots.
Scikit-Learn	`Estimator`	Linear Regression, Clustering.