Data Science & ML Ecosystem

Introduction

Python dominates data science with NumPy, Pandas, Matplotlib, scikit-learn — and PyTorch / TensorFlow for deep learning.

Understanding the topic

Core concepts to understand:

• NumPy — n-d arrays, vectorized math.
• Pandas — tabular data (DataFrames).
• Matplotlib/Plotly — viz.
• scikit-learn — classic ML.

Syntax reference

Visual flow / code:

import pandas as pd
import numpy as np

df = pd.read_csv("sales.csv")
df["total"] = df["qty"] * df["price"]

# Group + aggregate
summary = df.groupby("region")["total"].sum()

# NumPy: vectorized math (100× faster than loops)
a = np.array([1, 2, 3])
b = a * 2 + 1   # [3, 5, 7]

# Plot
df.plot(x="date", y="total")

Execution workflow

Real-world use

Pandas is the lingua franca of analytics — every data team uses it. Polars is the new fast competitor (Rust-backed).

Best practices

• Vectorize — avoid .apply() when possible.
• Use Polars for huge datasets.
• Profile before optimizing.

Hands-on exercise

Interview preparation — practice these questions:

• NumPy vs Python list?
• What is broadcasting?
• Pandas vs Polars?

Summary

In summary: Vectorize → speed. Pandas = standard tabular tool.