How to Learn Python for Data Science in 30 Days

30 days. No prior coding experience needed. Just consistency and the right order.

This is the exact plan I'd follow if I had to learn Python for Data Science from scratch — broken down week by week.

Week 1 — Python Basics (Days 1–7)

Focus: Get comfortable with Python syntax. Don't overthink it.

Day 1–2: Variables, data types, print statements

name = "Navya"
age = 22
is_student = True
print(f"My name is {name} and I am {age} years old.")

Day 3–4: Loops and conditionals

for i in range(1, 6):
    if i % 2 == 0:
        print(f"{i} is even")
    else:
        print(f"{i} is odd")

Day 5–6: Functions

def calculate_average(numbers):
    return sum(numbers) / len(numbers)

scores = [85, 90, 78, 92, 88]
print(calculate_average(scores))  # → 86.6

Day 7: Mini project — Build a simple calculator using functions.

Use case: Everything in data science runs on Python. This week gives you the foundation for everything else.

Week 2 — Python for Data (Days 8–14)

Focus: Learn the tools data analysts use every day.

Day 8–9: Lists, dictionaries, and tuples

student = {
    "name": "Navya",
    "scores": [85, 90, 78],
    "passed": True
}
print(student["scores"])  # → [85, 90, 78]

Day 10–11: NumPy basics

import numpy as np
data = np.array([10, 20, 30, 40, 50])
print(np.mean(data))   # → 30.0
print(np.std(data))    # → 14.14

Day 12–13: Pandas basics

import pandas as pd
df = pd.read_csv("students.csv")
print(df.head())
print(df.isnull().sum())

Day 14: Mini project — Load a CSV file, clean it, and answer 3 questions about the data.

Use case: Pandas and NumPy are used in every single data analysis project. This week is the most important one.

Week 3 — Data Analysis & Visualization (Days 15–21)

Focus: Turn data into insights and charts.

Day 15–16: Data cleaning with Pandas

df.dropna(inplace=True)
df["age"].fillna(df["age"].mean(), inplace=True)
df.drop_duplicates(inplace=True)

Day 17–18: Matplotlib basics

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [10, 20, 15, 25], marker="o")
plt.title("Sales Trend")
plt.show()

Day 19–20: Seaborn basics

import seaborn as sns
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()

Day 21: Mini project — Full EDA on the Titanic or Netflix dataset. Clean the data, visualize patterns, write 5 insights.

Use case: Visualization is how you communicate your analysis to people who don't read code.

Week 4 — Machine Learning Intro (Days 22–30)

Focus: Build your first real ML model.

Day 22–23: Scikit-learn basics

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Day 24–25: Your first model — Linear Regression

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))

Day 26–27: Classification — Logistic Regression

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
model = LogisticRegression()
model.fit(X_train, y_train)
print(accuracy_score(y_test, model.predict(X_test)))

Day 28–29: Model evaluation

from sklearn.metrics import classification_report
print(classification_report(y_test, model.predict(X_test)))

Day 30: Final project — Customer Churn Prediction or House Price Prediction. Clean data → build model → evaluate → push to GitHub.

Use case: Machine learning is what separates a data analyst from a data scientist. Even basic ML knowledge makes your resume stand out.

Quick Reference — Save This

One Important Rule

Don't skip the mini projects.

Watching tutorials without building anything is not learning. It's entertainment.

Every week has one small project for a reason — it forces you to use what you learned in a real context. That's where the actual learning happens.

Save this plan. Start Day 1 today.