What Is Data Science?

Introduction

Imagine being a detective, but instead of clues like footprints, you use numbers, words, and pictures. That’s data science!
Data science is about using data to answer questions and make better decisions.
In this PyVerse lesson, you’ll learn what data science is, see real examples, and try small Python activities yourself.

What Is Data?

Data is information. It can be numbers, words, dates, or choices.

Examples: daily temperatures, favorite snacks from a class survey, steps counted by a fitness watch, song ratings.

A dataset is a collection of data about the same topic.

Example: a table with columns like Day, Temperature, and Rain.

Real-World Examples of Data Science

Streaming and music apps: Suggest movies or songs based on what you like.
Sports: Coaches track player speed, passes, and shots to improve training.
Health: Fitness apps show your step trends and sleep patterns.
Environment: Cities use air quality data to send alerts on smoky days.
Transportation: Bus systems adjust routes using rider counts and delay data.
School life: Cafeterias plan how many sandwiches to make by looking at past lunch choices.

How Data Scientists Work (Step by Step)

Ask a clear question
Example: What snack is most popular in our class?
Collect data
Example: Survey everyone’s favorite snack.
Clean the data
Fix mistakes like “appl” vs “apple,” remove duplicates.
Explore the data
Count, sort, find minimum/maximum, average, and simple patterns.
Visualize
Make a small chart to see the trend quickly.
Decide and act
Use what you learned to make a choice (buy more of the popular snack).

Tools You’ll Use

Python: the main language for data science.
Helpful libraries (optional for beginners): pandas for tables, matplotlib for charts.

Python Code Examples

Example 1: Class Snack Survey (Pure Python)

Goal: Count votes and find the most popular snack.

# Count favorite snacks and print a simple text chart
from collections import Counter

snacks = [
    "Chips", "Apple", "Chips", "Cookie", "Apple",
    "Chips", "Banana", "Cookie", "Apple", "Chips"
]

# Count how many times each snack appears
counts = Counter(snacks)
print("Snack counts:", counts)

# Find the most popular snack
most_common_snack, votes = counts.most_common(1)[0]
print(f"Most popular snack: {most_common_snack} with {votes} votes")

# Simple text bar chart
print("\nSnack popularity:")
for item, count in counts.items():
    bar = "█" * count  # one block per vote
    print(f"{item:8} | {bar} ({count})")

Example 2: Tiny Weather Data (pandas + optional chart)

Goal: Find average temperature and list hot days.

# If you don't have these, install in a terminal:
# pip install pandas matplotlib

import pandas as pd

# Create a small dataset
data = [
    {"day": "Mon", "high": 26, "rain_mm": 2},
    {"day": "Tue", "high": 29, "rain_mm": 0},
    {"day": "Wed", "high": 31, "rain_mm": 1},
    {"day": "Thu", "high": 28, "rain_mm": 5},
    {"day": "Fri", "high": 27, "rain_mm": 0},
]

df = pd.DataFrame(data)

# Calculate average high temperature
avg_high = df["high"].mean()
print("Average high temperature:", round(avg_high, 1))

# Find hot days (30C or higher)
hot_days = df[df["high"] >= 30]
print("\nHot days (30C or higher):")
print(hot_days[["day", "high"]])

# Optional: make a line chart (works in many local/online environments)
try:
    import matplotlib.pyplot as plt
    df.plot(x="day", y="high", kind="line", marker="o", title="Daily High Temperatures (C)")
    plt.ylabel("Degrees C")
    plt.tight_layout()
    plt.show()
except Exception as e:
    print("\nChart not shown (environment may not support plotting):", e)

Example 3: Simple Recommendation Idea

Goal: Suggest what the user might like based on time spent.

# Minutes you spent listening to each genre this week
listening_minutes = {
    "Pop": 120,
    "HipHop": 80,
    "Rock": 45,
    "Classical": 15
}

# Recommend the genre with the most minutes
favorite_genre = max(listening_minutes, key=listening_minutes.get)
print(f"You seem to enjoy {favorite_genre} the most this week!")

# Give a friendly suggestion
suggestions = {
    "Pop": "Check out this week's Top Hits playlist!",
    "HipHop": "Try a new freestyle session playlist.",
    "Rock": "Explore classic rock anthems.",
    "Classical": "Relax with a focus/classical mix."
}
print("Suggestion:", suggestions.get(favorite_genre, "Explore something new!"))

Your Turn: Small Practical Exercise

Scenario: You manage a tiny school store. Use data to answer questions.

Dataset:

sales = [
    {"item": "Pencil",   "price": 0.50, "units": 30},
    {"item": "Notebook", "price": 2.00, "units": 15},
    {"item": "Eraser",   "price": 0.25, "units": 40},
    {"item": "Marker",   "price": 1.50, "units": 10},
    {"item": "Pencil",   "price": 0.50, "units": 20}
]

Tasks:

What is the total revenue? (Hint: price × units, then add up.)
Which item sold the most units in total? (Hint: combine rows with the same item.)
List any items with revenue over $15.
Bonus: Print a text bar chart showing units sold per item.

Starter code (fill in the TODOs):

sales = [
    {"item": "Pencil",   "price": 0.50, "units": 30},
    {"item": "Notebook", "price": 2.00, "units": 15},
    {"item": "Eraser",   "price": 0.25, "units": 40},
    {"item": "Marker",   "price": 1.50, "units": 10},
    {"item": "Pencil",   "price": 0.50, "units": 20}
]

# 1) Total revenue
total_revenue = 0
for row in sales:
    # TODO: compute revenue for this row and add to total_revenue
    revenue = row["price"] * row["units"]
    total_revenue += revenue
print("Total revenue: $", round(total_revenue, 2))

# 2) Units sold per item (combine by item name)
units_per_item = {}
for row in sales:
    name = row["item"]
    units = row["units"]
    # TODO: add units to the correct item total
    units_per_item[name] = units_per_item.get(name, 0) + units

print("Units per item:", units_per_item)

# Find the best-selling item
best_item = max(units_per_item, key=units_per_item.get)
print("Best-selling item:", best_item, "with", units_per_item[best_item], "units")

# 3) Items with revenue > $15 (combine revenue per item)
revenue_per_item = {}
for row in sales:
    name = row["item"]
    revenue = row["price"] * row["units"]
    revenue_per_item[name] = revenue_per_item.get(name, 0) + revenue

print("Items with revenue > $15:")
for name, rev in revenue_per_item.items():
    if rev > 15:
        print("-", name, "-> $", round(rev, 2))

# 4) Bonus: Text bar chart for units
print("\nUnits sold (bar chart):")
for name, units in units_per_item.items():
    bar = "█" * (units // 5)  # one block per 5 units
    print(f"{name:8} | {bar} ({units})")

Tips for Success

Start with a small, clear question.
Keep your data tidy (consistent spelling and units).
Print intermediate results to check your work.
Visuals help you “see” the pattern quickly.

Recap

Data science uses data to answer questions and make decisions.
You learned the basic steps: ask, collect, clean, explore, visualize, decide.
You tried Python examples to count, average, and make simple recommendations.
With small steps and simple code, you can apply data science to real-life problems at school, at home, and beyond.

Short Summary

Data science is one of the most exciting fields in today’s tech-driven world — and this beginner-friendly PyVerse.io lesson makes it easy to understand! In Lesson 1: What Is Data Science? Real-World Examples, students learn how data helps solve everyday problems — from predicting weather to recommending songs. Through engaging examples and Python coding activities, learners explore how to collect, clean, and visualize data using libraries like pandas and matplotlib. Whether it’s finding the most popular snack in class or tracking temperature trends, this lesson builds the foundation for real-world data analysis skills. By the end, students understand the six essential steps of data science — from asking the right question to making informed decisions. Ideal for beginners, school students, and coding enthusiasts who want to start their data science journey confidently with PyVerse.io, where learning meets creativity!

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