courses

author: Parul Pandey id: course-1 summary: Master the essentials of Pandas and NumPy. categories: python status: Published home_url: https://parulnith.github.io/courses/

Python for Data Science 101

Introduction

Welcome! In this module, we cover data manipulation basics - tools every Data Scientist needs. We will focus on the “Holy Trinity” of Python Data Science:

1. NumPy for numerical computing.
2. Pandas for data manipulation.
3. Matplotlib for basic visualization.

The Foundation: NumPy

Duration: 5:00

NumPy is the backbone of almost all data libraries in Python. It allows for fast operations on arrays.

import numpy as np

# Create a 1D array
arr = np.array([1, 2, 3, 4, 5])

# Perform vector operations
print(arr * 2) 

Data Manipulation: Pandas

Duration: 10:00

Pandas introduces the DataFrame, a powerful tool for handling tabular data.

# Loading a Dataset

import pandas as pd

# Creating a simple dataframe
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
print(df)

Basic Visualization

Duration: 8:00 Before using advanced libraries, it’s important to understand matplotlib.

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [10, 20, 25, 30])
plt.title("Simple Growth Chart")
plt.show()

Interactive Lab: Python 101

Duration: 10:00

To get hands-on experience, we have prepared an interactive Marimo notebook. This allows you to run code directly in your browser without installing anything.

🚀 Action Required

Positive

Launch the Interactive Notebook

Click the link below to open the lab in a new window. Once finished, return here to continue the tutorial.

👉 Open Marimo Notebook

Next Steps

You’ve successfully:

Learned how to use NumPy arrays.

Created your first Pandas DataFrame.

Visualized basic data points.

Next Step: Head over to Course 2: Visual Storytelling to learn how to make these charts look professional.