🧠 DATA SCIENCE MASTER TUTORIAL (PART 1)

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📊 Data Science Master Tutorial – Part 1

Welcome to the ultimate Data Science series on Darchumstech. In this guide, you’ll learn everything from scratch—tools, theory, and real code examples. Let's begin!

Data Science combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

  • It involves data cleaning, exploration, modeling, and visualization.
  • Popular tools: Python, R, SQL, Jupyter, Pandas, Matplotlib

Applications: fraud detection, recommendation engines, predictive analytics, and more.

We’ll use Python and Jupyter Notebook (via Anaconda) for hands-on projects.

🔧 Installation:

# Step 1: Download Anaconda from https://www.anaconda.com
# Step 2: Open Anaconda Navigator
# Step 3: Launch Jupyter Notebook

Test your setup with:

print("Hello, Data Science!")

Data can come from files, web scraping, APIs, or databases.

📁 Read CSV file with Pandas:

import pandas as pd

df = pd.read_csv("data.csv")
print(df.head())

🌐 Get data from a URL:

url = "https://example.com/data.csv"
df = pd.read_csv(url)

Cleaning includes handling missing values, duplicates, and fixing data types.

# Check for missing values
print(df.isnull().sum())

# Fill missing with median
df['Age'].fillna(df['Age'].median(), inplace=True)

# Drop duplicates
df.drop_duplicates(inplace=True)

Always inspect your dataset before modeling.

💻 Try It Yourself:






















































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