Introduction to Statistics

Welcome to the first session of this module! Before we delve into the world of data, it would be worth spending some time knowing what data itself is and what are the methods of describing and visualising data (or in simple words, creating an image of what data could look like). 

We believe that being introduced to data before delving into its significance in decision-making would help you appreciate and understand the beauty of the world of data-based decisions. 

So, in this segment, you will learn about data and how it is measured. The upcoming video will start by giving you an overview of the data. 

So, in the video, you learnt that data is essential in order to convert opinions to decisions. It provides evidence to support ideas. 

Data is present all around us. But before you learn how to use data, you must be familiar with its definition and categories. Given below is a brief definition of data and its different categories:

1. Data: Raw information generated by any activity is known as data. Any attribute of an object can be recorded as data; for example, the colour of a car, the speed of a car.

2. Data Set: A data set is a collection of data from a particular group or study; for example, the speed of different cars on a highway on New Year’s Eve.

3. Variable: A variable is a name that represents data. For example, suppose you need to use some data again and again in a report. You can use a variable to represent the data, instead of describing it repeatedly in the report. Variables can be classified into the following two categories depending upon the values they can take:

   1. Qualitative or categorical variables: These variables can take values that describe the quality of an object or activity; for example, the colour of a car, customer satisfaction level.

   2. Quantitative variables: These variables can take fixed values defined by numbers; for example, the speed of a car, the number of students in a class. Quantitative variables can be further classified as discrete and continuous:

      1. Discrete variables: The variables that can be counted, and do not have any decimal parts, are known as discrete variables; for example, the number of students in a class. A class can have 10 students or 11 students, but it cannot have 10.25 students.

      2. Continuous variables: The variables that can be divided infinitely into smaller parts are known as continuous variables. For example, a student’s height can be 1 metre or 0.99 metres, or 0.998 metres.

To illustrate the concepts above, Thomas will now introduce you to a sample data set of a Portuguese bank, which ran a marketing campaign. Let us listen to Thomas as he describes the data set in the upcoming video. 

You can download the Portuguese bank data set provided as an Excel spreadsheet from the link given below.

Note: We recommend that you open Excel and follow the steps in the video in order to understand data visualisation better.

In the above video, of the Portuguese bank marketing campaign data set, you saw the different data fields, their types and their description.

Descriptive statistics is a branch of statistics that describes or summarises a collection of information. The first two sessions of this module discuss descriptive statistics in detail.

The next segment will introduce you to how you can learn from the data in this data set in a visually appealing manner.