Data and Visualization I: Structuring Information

Course overview

In this course, students learn the foundations of data. They build the ability to organize numbers and text systematically and turn them into graphs to discover patterns and meaning. After understanding the principles of visualization with turtle graphics, they make professional graphs with Matplotlib and analyze real music/fashion data.

Estimated time: 16-32 hours

The time required depends on the student's grade level, prior experience, and learning speed, and varies greatly. It also differs between just learning the basic concepts and moving on, versus additional practice problems or creative projects: taking those on makes a difference too. The times above are an average range, so please proceed flexibly at your child's pace.

What abilities will it build?

"the ability to structure data and represent it visually" is what students train. They build the foundations of data literacy, one of the most important competencies of the 21st century. They expand their thinking from single values to large datasets and adopt a data-driven mindset that replaces "gut feeling" with "evidence".

What principles will you learn?

(organizing data) With lists and dictionaries, they manage multiple pieces of data systematically. This is the basic structure of modern data systems such as databases, JSON, and APIs.
(comparison/trend analysis) They compare magnitudes with bar graphs and grasp change over time with line graphs. This is the core skill of turning numbers into visual patterns.

AI-era thinking: computing (problem-solving) | data-driven | probabilistic thinking

Data science thinking

Skill	Description	Example activity
📊 data and visualization	Turning numbers into graphs	Compare sales with a bar graph
🔍 Pattern discovery	Finding regularities in data	Analyze the trend of monthly UFO sightings
🧪 Hypothesis testing	Confirming a guess with data	Test "there are more UFO sightings in summer"
🔽 Data filtering	Extracting only the data that meets a condition	Select only data from 2020 onward

Mathematical connections

Math concept	Programming application	Learning benefit
Statistics	Calculating average, max, and min	`sum(data)/len(data)` -> average
ratios and percentages	Calculating pie chart proportions	Each part's % of the whole
Coordinate plane	Marking a location on a map by latitude/longitude	Extending the (x, y) coordinate concept
Correlation	Analyzing the relationship between two variables	The "music tempo <-> popularity" relationship

The scientific inquiry process

flowchart LR
    A[1. Question] --> B[2. Hypothesis]
    B --> C[3. Collect]
    C --> D[4. Analyze]
    D --> E[5. Conclusion]
    E -.->|new question| A

Steps	Example
Question	"Which brand of sneaker is the most expensive?"
Hypothesis	"Nike will be the most expensive"
Collect	Load price data from a CSV file
Analyze	Bar graph of average price by brand
Conclusion	"Actually, Yeezy was the most expensive"

Media literacy

What to check when looking at a graph:
- Does the Y axis start at 0? (potential distortion)
- Is the sample size large enough?
- Correlation ≠ causation

💻 Code examples & visualization

Example 1: Bar Graph - Matplotlib

import matplotlib.pyplot as plt

# Data
fruits = ['Apple', 'Banana', 'Orange', 'Grape']
sales = [45, 30, 25, 40]

# Create a bar graph
plt.bar(fruits, sales, color=['red', 'yellow', 'orange', 'purple'])
plt.title('Fruit Sales')
plt.xlabel('Fruit')
plt.ylabel('Sales')
plt.show()

Result:

Fruit Sales
│
50┤     ██
40┤ ██  ██          ██
30┤ ██  ██  ██      ██
20┤ ██  ██  ██      ██
10┤ ██  ██  ██      ██
 0┼─────────────────────
   Apple Banana Orange Grape

Example 2: Pie Chart - Visualizing Proportions

import matplotlib.pyplot as plt

# Data
labels = ['Gaming', 'YouTube', 'Studying', 'Exercise']
times = [3, 2, 4, 1]  # Time (unit: hours)

# Pie chart
plt.pie(times, labels=labels, autopct='%1.1f%%')
plt.title('Daily Activity Time Breakdown')
plt.show()

Result:

      Daily Activity Time Breakdown

          Studying
         (40%)
       ╱────────╲
     ╱            ╲
   Gaming            Exercise
  (30%)          (10%)
     ╲            ╱
       ╲────────╱
        YouTube
        (20%)

Example 3: Line Graph - Trend Analysis

import matplotlib.pyplot as plt

# Data
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
temperature = [2, 5, 12, 18, 23, 27]

# Line graph
plt.plot(months, temperature, marker='o', color='red')
plt.title('Monthly Average Temperature')
plt.xlabel('Month')
plt.ylabel('Temperature (°C)')
plt.grid(True)
plt.show()

Result:

Monthly Average Temperature
│
30┤                    ●
25┤                ●
20┤            ●
15┤        ●
10┤
 5┤    ●
 0┤●
  ┼─────────────────────
   Jan Feb Mar Apr May Jun

Example 4: Map Visualization - Folium

import folium

# Create a map centered on Seoul
map = folium.Map(
    location=[37.5665, 126.9780],  # Seoul coordinates
    zoom_start=12
)

# Add a marker
locations = [
    [37.5796, 126.9770, "Gyeongbokgung"],
    [37.5512, 126.9882, "Namsan Tower"],
    [37.5662, 126.9785, "Gwanghwamun"]
]

for loc in locations:
    folium.Marker(
        location=[loc[0], loc[1]],
        popup=loc[2],
        icon=folium.Icon(color='red')
    ).add_to(map)

map.save("seoul_map.html")

Run result (map):

┌─────────────────────────────┐
│     🔴 Gyeongbokgung       │
│         ╲                  │
│          ╲   Seoul map     │
│    🔴 Gwanghwamun          │
│              ╲             │
│               ╲            │
│                🔴 Namsan Tower │
└─────────────────────────────┘

Example 5: Dictionary - Structuring Data

# UFO sighting data (dictionary)
ufo_sighting = {
    "date": "2024-03-15",
    "location": "Seoul",
    "Latitude": 37.5665,
    "Longitude": 126.9780,
    "shape": "disc",
    "duration": "5 minutes"
}

# Access the data
print(f"UFO sighting location: {ufo_sighting['location']}")
print(f"UFO shape: {ufo_sighting['shape']}")

# Multiple sightings (list + dictionary)
ufo_data = [
    {"City": "Seoul", "count": 15},
    {"City": "Busan", "count": 8},
    {"City": "Daegu", "count": 5}
]

Data structure:

ufo_data = [
  ┌─────────────────────┐
  │ {"city":"Seoul",     │ ← ufo_data[0]
  │  "count": 15}        │
  ├─────────────────────┤
  │ {"city":"Busan",     │ ← ufo_data[1]
  │  "count": 8}         │
  ├─────────────────────┤
  │ {"city":"Daegu",     │ ← ufo_data[2]
  │  "count": 5}         │
  └─────────────────────┘

🎮 Hands-on Practice - Part 1: Data Structures

Exercise 1.1: Making a List

Fill in the blanks to complete the list!

Exercise 1.2: Manipulating a List

Fill in the blanks to modify the list!

Exercise 1.3: Completing a Dictionary

Fill in the blanks to complete the game character dictionary!

Exercise 1.4: Adding Data to a Dictionary

Fill in the blanks to add a new item to the dictionary!

Exercise 1.5: Calculating an Average

Fill in the blanks to calculate the average score!

🎮 Hands-on Practice - Part 2: Drawing Graphs

Exercise 2.1: Completing a Bar Graph

Fill in the blanks to complete the bar graph!

Exercise 2.2: Adding Color to a Bar Graph

Apply colors to the bar graph!

Exercise 2.3: Completing a Line Graph

Fill in the blanks to complete the line graph!

Exercise 2.4: Comparing Two Lines

Fill in the blanks to compare two students' scores!

Exercise 2.5: Completing a Pie Chart

Fill in the blanks to complete the pie chart!

Exercise 2.6: Highlighting a Pie Chart Slice

Fill in the blanks to highlight a specific item!

🎮 Hands-on Practice - Part 3: Data Analysis

Exercise 3.1: Finding the Max/Min Value

Fill in the blanks to find the max and min values!

Exercise 3.2: Filtering Data by a Condition

Fill in the blanks to filter data that meets a condition!

Exercise 3.3: Sorting Data

Fill in the blanks to sort the data!

Exercise 3.4: Finding the Max-Value Record

Fill in the blanks to find the item with the largest value!

🎮 Hands-on Practice - Part 4: Debugging Practice

Exercise 4.1: Fixing an Index Error

_____Replace it with the correct index to fix the error!

Exercise 4.2: Fixing a Dictionary Key Error

Enter the correct key name to fix the error!

Exercise 4.3: Fixing a Data Count Error

Match the data counts to fix the graph error!

📝 Fill-in-the-Blank Practice

List basics:

Creating a list: my_list =
Adding an item: my_list. (4)
Removing an item: my_list. (2)
List length: (my_list)
First item: my_list[ ]

Dictionary basics:

Creating a dictionary: my_dict = { }
Getting a value: my_dict[ ]
Adding/modifying a value: my_dict['새키'] =
All keys: my_dict. ()
All values: my_dict. ()

Matplotlib basics:

Bar chart: plt. (x, y)
Line graph: plt. (x, y)
Pie chart: plt. (sizes)
Show the graph: plt. ()
Add a title: plt. ('title')
x-axis label: plt. ('label')
Show the legend: plt. ()
Add a grid: plt. (True)

Data analysis:

Max value: (list)
Min value: (list)
Sum: (list)
Average: sum(list) / (list)
Sort: (list)

🎯 Quiz

Quiz 1: Choosing a Graph

Which graph is best for showing the proportion of a class's favorite subjects?

Quiz 2: Data Structure

Which data type is best for storing a student's name, age, and score together?

Quiz 3: Trend Analysis

Which graph is best for showing how a stock price changes over time?

Quiz 4: List Index

In `fruits = ['사과', '바나나', '오렌지']`, how do you get '바나나'?

Quiz 5: Matplotlib Function

Which function displays the graph on screen?

Why learn data and visualization?

Key 21st-Century Competency

Data and visualization skills	Real-life uses
Reading graphs	Understanding statistics in the news
Trend analysis	Grasping market trends
Pattern discovery	Scientific discovery
Map visualization	Location-based analysis
Communicating information	Presentations, writing reports

Data literacy is a democratic citizen essential for critically evaluating information as

1. Drawing Graphs with Turtle

Item	Content
What will you learn?	Basic concepts of data and visualization
Core Concepts	Defining data, bar graphs, pie charts

Data is collected information. With turtle graphics, students draw graphs by hand and understand the structure of bar graphs and pie charts. Bar graph is suited to comparing categories, and the pie chart is suited to showing each part's proportion of the whole. Before using a library, students grasp the principles.

2. Lists and Data

Item	Content
What will you learn?	Data structures and dictionaries
Core Concepts	Lists, dictionaries, data management

Dictionaryis {"이름": value} form to store data. Students learn how to store and access real data efficiently. This data structure is the core tool used most often in data analysis.

3. Matplotlib (Browser)

Item	Content
What will you learn?	Python's standard data and visualization library
Core Concepts	The Matplotlib API, various chart types

Matplotlib is the most widely used visualization library in Python. You can make almost any type of graph - bar graphs, line graphs, scatter plots, histograms, pie charts, and more. Students learn the tools data scientists and researchers actually use.

4. Music Data Lab

Item	Content
What will you learn?	Analyzing real music-industry data
Core Concepts	Trend analysis, correlation, hypothesis testing

real Spotify data to analyze music trends. They answer intriguing questions like "Is music getting louder and louder?" and "Are sad songs more popular?" With data, students form and test hypothesesand experience the scientific method.

Mission	Analysis topics
Loudness Wars	Analyzing change over time
Sad Banger	Correlation analysis
Seasonal	Seasonal trends
Major/Minor	Comparison by group

5. SneakerBot Streetwear Lab

Item	Content
What will you learn?	Fashion/consumer data analysis
Core Concepts	Market analysis, consumer behavior

Sneaker/streetwear sales data. They explore questions like "Nike vs Adidas, who wins?" and "Are limited editions really more expensive?" Students work with real business dataand get their first taste of the fundamentals of marketing and management.

Mission	Analysis topics
Hype Tax	Price premium
Brand Wars	Brand competition
Global	Analysis by region
Inflation	Price changes

When you finish this course...

Learning outcomes

Students will be able to:

Lists and dictionariesto structure data
various graph types(bar, line, pie) for the right situation
Matplotlibto create professional graphs
real datasetsand analyze them
identify a trends and patternsin the data
form and test hypothesesdata-science thinking

Next step

Students who finish Data & Visualization I can move on to Data and Visualization IIto learn map visualization, hands-on data analysis, and data storytelling.