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Lecture 3 — The Art of Collecting Data

By Vyoma Youth Society

In this lecture, we will discuss how data is collected and perform a small practical example using Python.

In the previous lectures, we discussed:

• what data really is
• why uncertainty exists in data

But before moving deeper into statistics and machine learning, we must understand something very basic:

Where does data come from?

Data does not appear magically.
Collecting data is a skill and a process.

So the first question we should ask is:

Where can we collect data from?

Before continuing, pause for a moment and write down a few places where you think data can be collected.

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Collection of Data

Before we start working with statistics or building models, we must first have data to work with.

Data collection is the foundation of data science.
Without data, analysis is impossible.

When we think about collecting data, there are generally two main types of sources.

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Types of Data Sources

1️⃣ Primary Sources

Primary data is the data that you collect yourself.

This usually involves first-hand methods such as:

• surveys
• interviews
• experiments
• observations

In primary data collection, you design the method and gather the data directly.

Example:
If you conduct a survey asking students about their study hours, the data you collect is primary data.

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2️⃣ Secondary Sources

Secondary data is data that has already been collected by someone else.

Instead of collecting it yourself, you use existing datasets.

Examples include:

• public datasets
• research papers
• government statistics
• online databases

Using secondary data is very common in data science because many organizations publish large datasets.

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For now, we will only introduce these concepts.
Later in this course, we will have a full module dedicated to data collection, where we will study these methods in much greater detail.

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Practical Section

In the last two lectures we focused mainly on theory.
So today we will do a small practical exercise.

Our goal is simple:

Compare the manual calculation of the mean with the mean calculated by a Python library.

This helps us understand that libraries are simply implementations of mathematical formulas.

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Python Practical

First, create a Python file and write the following code.

import numpy as np

a = np.array([i for i in range(10)])

# 1. Manual calculation
manual_mean = sum(a) / len(a)

# 2. Using NumPy
numpy_mean = np.mean(a)

print(f"Manual mean: {manual_mean}")
print(f"Numpy mean: {numpy_mean}")

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Understanding What Happened

Before understanding the code, we should understand what mean really is.

The mean is simply the average value of a dataset.

The formula is:

[ Mean = \frac{\text{Sum of all values}}{\text{Number of values}} ]

In the program:

• sum(a) adds all numbers in the dataset
• len(a) gives the number of elements
• sum(a) / len(a) calculates the mean manually

Then we use the NumPy function:

np.mean(a)

This function performs the same calculation internally.

When you run the code, both methods give the same result:

4.5

This shows that library functions are simply automated versions of mathematical formulas.

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What This Practical Teaches Us

1️⃣ Data must first be collected before analysis.
2️⃣ Statistics is built on simple mathematical ideas.
3️⃣ Programming libraries automate these calculations.

Understanding the mathematics behind the libraries is an important goal of this initiative.

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Our open source repository :-

https://github.com/psjdeveloper/vyomadatascience