???? Lecture 1 — Central Tendency of Data
Vyoma Data Science Initiative | Module 2: Statistics
???? Introduction
In any dataset, we often face a simple question:
Can we represent all this data using a single number?
Whether it’s exam scores, income levels, or age distributions — we try to summarize complexity into simplicity.
This idea leads us to central tendency.
???? What is Central Tendency?
Central tendency refers to the representative value of a dataset — the point around which the data tends to cluster.
It helps answer:
- What is typical?
- Where does the data concentrate?
- What does this dataset “look like” overall?
???? Measures of Central Tendency
1️⃣ Mean (Average)
[
\text{Mean} = \frac{\sum x_i}{n}
]
- Represents the balance point of the dataset
- Highly sensitive to outliers
- The middle value after sorting data
- If even number of values → average of two middle values
???? More robust than mean in real-world datasets
3️⃣ Mode (Most Frequent Value)
- The value that appears most often
- Useful for both numerical and categorical data
⚠️ When Averages Mislead
Consider:
2, 3, 4, 5, 100
- Mean = 22.8 ❌
- Median = 4 ✅
???? The mean is distorted by an extreme value.
???? Insight
A single number cannot always capture reality.
The choice of measure defines the “truth” you see.
???? Practical Implementation (Python)
Using the Titanic dataset, we analyze the Age column.
???? Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv(r"C:\Users\praso\vyomadatascience\module02\titanic.csv")
print(data.head())
print("Mean Age:", data["Age"].mean())
print("Mode Age:", data["Age"].mode())
print("Median Age:", data["Age"].median())
???? Optional Visualization
sns.histplot(data["Age"].dropna(), kde=True)
plt.title("Age Distribution (Titanic Dataset)")
plt.xlabel("Age")
plt.ylabel("Frequency")
plt.show()
???? Dataset Reference
You can download the Titanic dataset from:
(You can place it in your project folder: module02/titanic.csv)
???? GitHub Repo
Download the github repo of our course from here :
???? Final Reflection
Central tendency is not just about computing mean, median, or mode.
It is about understanding:
- How data behaves
- How summaries can distort reality
- And how interpretation matters more than calculation
✍️ Closing Line
Data does not speak for itself.
The way we summarize it decides what it says.
