This is the 2nd part of the series of Understanding Statistics.

**Vital terms in Statistics**

**Population**: As the term is self-explanatory, population is nothing but the universe of possible data.

· Example: People who visited a particular website.

**Parameter:** A numerical value associated with the population.

· Example: Average amount of time people spent on website.

**Sample**: A selection of observations from a population.

· Example: People who visited the website on a particular time of the day.

**Statistic**: A numerical value associated with an observed value.

· Example: Average amount of time people spent on a website on a particular day.

Vital terms of Statistics

In the previous article we’ve touched up on what data , however we haven’t understood how we get the data.

Lets look in to that for a bit .

**DATA SOURCES**

There are basically 2 Types of data Sources

1. Primary Data

2. Secondary Data

**Primary Data**: Data that is being collected regularly (Data collected on daily basis).

**Secondary Data**: Data which is already been collected and stored, which might be for a set of days.

Let’s touch a bit on the types of Data sets.

What are Data sets?

A **data set** is a collection of numbers or values that relate to a particular subject.

Example: The test scores of each student in a particular class is a data set.

Types of Data Sets:

Data sets are broadly classified in to 3 groups namely:

· **Record Data: **Most basic form of which has no relation between records or data fields and every data object has same set of attributes.

Examples**: **Transaction data, data matrix.

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Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.

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